FEDERAL RESERVE BANK OF SAN FRANCISCO

WORKING PAPER SERIES

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

Mathias S. Kruttli

The Board of Governors of the Federal Reserve System

Brigitte Roth Tran Federal Reserve Bank of San Francisco

Sumudu W. Watugala

Cornell University

March 2021

Working Paper 2021-23

https://www.frbsf.org/economic-research/publications/working-papers/2021/23/

Suggested citation:

Kruttli, Mathias S., Brigitte Roth Tran, Sumudu W. Watugala. 2021 “Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics,” Federal Reserve Bank of San Francisco Working Paper 2021-23. https://doi.org/10.24148/wp2021-23 The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the Board of Governors of the Federal Reserve System.

Pricing Poseidon: Extreme Weather Uncertaintyand Firm Return Dynamics∗

Mathias S. Kruttli, Brigitte Roth Tran, and Sumudu W. Watugala†

March 2021

We present a framework to identify market responses to uncertainty faced by firmsregarding both the potential incidence of extreme weather events and subsequent eco-nomic impact. Stock options of firms with establishments in forecast and realizedhurricane landfall regions exhibit large increases in implied volatility, reflecting signif-icant incidence uncertainty and long-lasting impact uncertainty. Comparing ex anteexpected volatility to ex post realized volatility by analyzing volatility risk premiachanges shows that investors significantly underestimate extreme weather uncertainty.After Hurricane Sandy, this underreaction diminishes and, consistent with Merton(1987), these increases in idiosyncratic volatility are associated with positive expectedstock returns.

JEL classification: G12, G14, Q54.Keywords: extreme weather, uncertainty, implied volatility, expected returns, climate risks.

∗We thank Lint Barrage (discussant), Michael Bauer (discussant), Riccardo Colacito (discussant), BenGroom (discussant), Matthew Gustafson (discussant), Burton Hollifield (discussant), Kris Jacobs (discus-sant), Scott Mixon (discussant), Aurelio Vasquez (discussant), Andrea Vedolin (discussant), Jawad Addoum,Rui Albuquerque, Vicki Bogan, Mikhail Chernov, Byoung-Hyoun Hwang, Andrew Karolyi, Fang Liu, DavidNg, Ian Martin, Justin Murfin, Emilio Osambela, Andrew Patton, Tarun Ramadorai, Laura Starks, ScottYonker, Youngsuk Yook, and seminar participants at the Federal Reserve Board, Cornell University, NOAA,UCSD, UCSB, CFTC, University of Zurich, UConn Finance Conference, UToronto-McGill Risk Manage-ment and Financial Innovation Conference in Memory of Peter Christoffersen, Conference on Commodities,Volatility and Risk Management, AERE Summer Conference, Northeast Workshop at Harvard KennedySchool, CEPR-EBRD-EoT-LSE Workshop, Resources for the Future, University of Oklahoma Energy andCommodities Finance Research Conference, Federal Reserve Day Ahead Conference, 2020 NBER AssetPricing Spring Meeting, 2021 AFA Meeting, UCLA Luskin Symposium on Climate Adaptation for helpfulcomments. Keely Adjorlolo, David Rubio, and Alan Yan provided outstanding research assistance. Theviews stated herein are those of the authors and are not necessarily the views of the Federal Reserve Board,the Federal Reserve Bank of San Francisco, or the Federal Reserve System.

†Kruttli: The Board of Governors of the Federal Reserve System. Email: mathias.s.kruttli@frb.gov. RothTran: The Federal Reserve Bank of San Francisco. Email: brigitte.rothtran@sf.frb.org. Watugala: CornellUniversity. Email: sumudu@cornell.edu.

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1 Introduction

From hurricanes and severe snow storms to droughts and wildfires, extreme weather events

have caused widespread devastation in recent years. For instance, in the record year of 2017,

the estimated damages from extreme weather events in the U.S. were over $300 billion.1 De-

spite an emerging climate finance literature, little is known about the uncertainty generated

by extreme weather events for firms or how that uncertainty is priced. Filling this gap in

the literature is important. Uncertainty, measured as the expectation of future volatility,

has been shown to affect financial markets and real economic activity in other contexts,2

and could impact the cost of capital for a firm.3 While the unpredictable impact of extreme

weather on productive capital, supply chains, local labor, and product demand could create

significant uncertainty, firms can potentially offset these effects through insurance, adapta-

tion, or relocation. Thus, it is not obvious a priori that extreme weather events generate

substantial uncertainty for firms. Moreover, the pricing of uncertainty related to extreme

weather is previously unstudied though highly relevant to the emerging, urgent debate about

the potential impact of climatic events on financial stability.4 Mispricing of such events in

asset markets could lead to sudden large price corrections that are destabilizing (Carney,

2015).

In this paper, we first use financial markets to isolate and quantify the extent of extreme

weather uncertainty faced by firms, and then analyze the pricing of this uncertainty. We

distinguish between two components of extreme weather uncertainty: (a) the “incidence

uncertainty” regarding where, when, and whether an extreme weather event will occur, and

(b) the “impact uncertainty” about an extreme weather event’s effect conditional on an

event occurring. The exogenous nature of the inception of extreme weather events allows

us to isolate the associated uncertainty cleanly and examine its pricing in our empirical

analysis because prevailing conditions of the firms do not affect the timing and likelihood

1National Oceanic and Atmospheric Administration (NOAA) damage estimates (https://www.climate.gov/news-features/blogs/beyond-data/2017-us-billion-dollar-weather-and-climate-disasters-historic-year).

2For example, political uncertainty is reflected in index option prices (Kelly, Pastor, and Veronesi, 2016)and leads to lower corporate investment (Julio and Yook, 2012; Jens, 2017). As in this paper, Bloom(2009); Pastor and Veronesi (2012, 2013); Jurado, Ludvigson, and Ng (2015) and others define uncertaintyas expected volatility, which is distinct from other strands of the literature on Knightian uncertainty.

3For example, Merton (1987) shows theoretically how even firm-specific uncertainty could impact theexpected return of a stock.

4Government agencies responsible for ensuring the resilience of the financial system have started toexamine the potential impact of climatic events. In 2020, the Commodity Futures Trading Commission’sreleased a report dedicated to climate risks (https://www.cftc.gov/PressRoom/PressReleases/8234-20). TheFederal Reserve Board states in its Financial Stability Report, November 2020: “...uncertainty about thetiming and intensity of severe weather events and disasters, as well as the poorly understood relationshipsbetween these events and economic outcomes, could lead to abrupt repricing of assets.” (https://www.federalreserve.gov/publications/2020-november-financial-stability-report-near-term-risks.htm).

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of an extreme weather event. This distinguishes our analysis from studies on other types

of uncertainty like macroeconomic or political uncertainty where periods of uncertainty are

generally endogenous to prevailing conditions of the economy or firm.5

Our empirical analysis focuses on hurricanes. We do so for a number of reasons. First,

hurricanes are among the most economically destructive extreme weather events.6 States all

along the Atlantic and Gulf coasts of the US, which include a wide variety of major centers

of economic activity, have been impacted by hurricanes. Second, NOAA publishes a range

of relevant forecast and realized data on hurricanes. These data are accessible to investors in

real-time. Third, hurricanes develop from inception at sea and resolve following landfall or

dissipation over fairly short but well-defined time frames, which allows us to estimate effects

in isolation. However, our framework can be applied to other extreme weather events like

snow storms and severe floods that are also subject to uncertainty about whether and where

they occur, and what the eventual impact will be.

We proxy for extreme weather uncertainty using changes to the implied volatility of stock

options, a measure that captures investor expectations of volatility.7 We carefully collect

novel data from NOAA on hurricane forecast and landfall spanning 22 years. We combine

those data with location data on establishments of individual firms to construct a granular

dataset that allows us to measure firm exposure to regions affected by each hurricane, which

determines treatment in our difference-in-differences analysis.

We present a simple theoretical framework to characterize how the two components of ex-

treme weather uncertainty affect the expected variance of a firm. While an extreme weather

event like a hurricane is forming and approaching an area in which a firm is located, the

associated extreme weather uncertainty for the firm comprises both incidence uncertainty

and expected impact uncertainty, and varies with the probability of incidence. Incidence

uncertainty is fully resolved at hurricane landfall (or dissipation in the case of a hurricane

that “missed”). Only impact uncertainty remains after landfall. Interestingly, under certain

conditions, incidence uncertainty can be high enough such that the total uncertainty faced

by a firm prior to hurricane landfall can even exceed the total uncertainty conditional on

landfall.

5See, for example, Bloom (2009); Jurado, Ludvigson, and Ng (2015); Baker, Bloom, and Davis (2016);Dew-Becker, Giglio, Le, and Rodriguez (2017); Hassan, Hollander, Van Lent, and Tahoun (2019). Somestudies on political uncertainty like Julio and Yook (2012); Kelly, Pastor, and Veronesi (2016); Jens (2017)focus on prescheduled political events, which are interpreted as known, exogenous points in time when apolicy (or regime) change might occur. However, the likelihood of whether a policy/regime change occurson the prescheduled date can still be endogenous to prevailing economic conditions. As Pastor and Veronesi(2012) discuss, such a change is more likely during downturns.

6For instance, in 2017, $265 billion of the aforementioned $300 billion in damages from extreme weatherevents in the US were due to hurricanes.

7As do Bloom (2009) and others.

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In our empirical analysis, first, using hurricane forecasts and associated landfall proba-

bilities (probabilities of incidence) issued in the days leading up to a hurricane’s landfall or

dissipation, we find that before landfall, implied volatilities of firms with exposure to the

forecast path increase even at a low landfall probability and increase up to 21 percent for a

high probability greater than 50%.8 These results imply that hurricanes cause substantial

uncertainty prior to landfall and that investors pay attention to hurricane forecasts.

After landfall, indicative of significant impact uncertainty, we find that the implied

volatility of firms with establishments in the landfall region are significantly elevated, rising

up to 23 percent higher than before the hurricane’s inception. Implied volatilities remain

elevated for several months after hurricane landfall, suggesting that the resolution of impact

uncertainty is slow. Comparing these impact uncertainty estimates to our total extreme

weather uncertainty estimates based on forecasts indicates that substantial incidence uncer-

tainty is reflected in options prices prior to landfall. These results on the extreme weather

uncertainty generated before and after landfall hold across and within industries, and are

economically significant.9

We next examine the accuracy of investor expectations of the volatility generated by

extreme weather events, which is important given the role of volatility in determining, for

instance, the risk associated with investment decisions, the cost of hedging physical climate

risks, and option prices. We analyze how volatility risk premia (VRP)—computed as the

difference between option-implied volatility and the subsequent realized volatility of the un-

derlying stock over the remaining life of an option—change due to a hurricane. We find

that the VRP of firms exposed to a hurricane forecast path or landfall region is substantially

lower for over a month, compared to control firms with no concurrent hurricane exposure.

This result implies that investors underreact to the volatility caused by a hurricane. Interest-

ingly, for hurricanes after Hurricane Sandy in 2012, the underreaction goes away, suggesting

that market efficiency improved after a particularly salient hurricane. This analysis con-

tributes to the discussion on whether markets efficiently price climatic risks. Our analysis

focused on volatility is distinct from recent work focused on returns that analyze if stock

investors efficiently price exposure to extreme weather events, which find evidence of both

underreaction (see Hong, Li, and Xu (2019) on how drought indices predict food company

stock returns) and overreaction (see Alok, Kumar, and Wermers (2020) on mutual fund

8We note here that unlike at the aggregate market level, stock returns and volatility at the firm level gen-erally exhibit positive contemporaneous correlation as shown in Duffee (1995); Albuquerque (2012); Grullon,Lyandres, and Zhdanov (2012). As such, the negative return-volatility relationship documented for marketindex volatility is not impacting our results, since our analysis is on firm-level volatility.

9Disclosures by firms following exposure to a hurricane reveal a myriad of unpredictable economic impactsthat illustrate the uncertainty regarding eventual firm outcomes (see, for example, excerpts of sample firmdisclosures in Table A.2).

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performance following natural disasters).

We also analyze whether investors demand compensation for the extreme weather uncer-

tainty faced by firms that our previous results establish to be substantial. Levy (1978) and

Merton (1987) show theoretically how such idiosyncratic volatility can be “priced” and be

positively related to expected stock returns because, in practice, investors may be underdi-

versified and unable to hold the market portfolio as predicted by the capital asset pricing

model.10 Prior papers such as Ang, Hodrick, Xing, and Zhang (2006) and Fu (2009) have

empirically tested this prediction assuming a particular volatility model or factor structure

for stock returns and arrived at mixed conclusions. We contribute to this debate by ex-

ploiting our empirical setting, which allows us to isolate exogenous increases to idiosyncratic

uncertainty faced by firms.11 We analyze how these shocks to idiosyncratic volatility are

related to expected stock returns. While in the early sample we do not find an impact

on expected returns, after Hurricane Sandy, we find strong evidence consistent with Merton

(1987) that firms exposed to higher idiosyncratic volatility have significantly higher expected

stock returns. Together with the VRP result, this is further evidence of investors learning

to price extreme weather uncertainty.

Our findings hold across and within industries, are not driven by small firms, are robust

to the exclusion of individual hurricanes from the sample, and are robust to measuring the

geographic footprint of a firm in terms of the distribution of sales instead of establishments.

While financial firms are excluded from our baseline analyses, we show that single stock

options of property and casualty insurance firms also reflect substantial extreme weather

uncertainty.

We extend our analysis in several ways. We show that for firms hit by hurricanes there is

a large cross-sectional dispersion in the cumulative abnormal stocks returns from hurricane

inception to up to six months after landfall, which is consistent with the large increases in

uncertainty established in our baseline results. The larger dispersion in cumulative abnormal

returns leads to both significant underperformance and outperformance in the sample of hit

firms compared to control firms, implying that some firms may benefit from a hurricane

making landfall. Further, while our results show that investors are attentive to short-term

forecasts and price in incidence uncertainty and expected impact uncertainty, we find no

evidence that they react to NOAA’s medium-term seasonal forecasts, which are much less

10These models are distinct from those, for example, in Martin and Wagner (2019), which concern thepricing of firm-specific sensitivity to aggregate volatility shocks like the global financial crisis. Here, weisolate and examine variation in firm-specific idiosyncratic volatility independent of market-wide shocks.

11Each hurricane can be considered an exogenous idiosyncratic shock in this context because it affects asubset of firms distributed across different industries (see, for example, Barrot and Sauvagnat (2016)). Thevast majority of firms within the market will be unaffected by a specific hurricane. Moreover, the set ofaffected firms will vary for each hurricane.

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informative than the forecasts for individual hurricanes. Also, analyzing the open interest

and trading volume of options, we find an increase in open interest following landfall in line

with increased hedging demand (see Hong and Yogo (2012)) and a dip in trading volume

right before hurricanes make landfall.12

This paper makes several important contributions. First, we present a novel framework

of incidence and impact uncertainty to formalize our understanding of uncertainty before

and after extreme weather events. Second, we estimate incidence and impact uncertainty

empirically and find that both are large and reflected in option prices with impact uncer-

tainty resolving slowly. A comprehensive analysis of extreme weather uncertainty is novel

to the existing literature on uncertainty. In addition, we characterize and empirically ana-

lyze incidence uncertainty, which is not possible in other settings that generally analyze the

uncertainty generated from endogenous or prescheduled events.13 Third, we contribute to a

nascent literature that analyzes how efficiently financial markets price climate risks by show-

ing that investor expectations of a hurricane’s impact on volatility are systematically too

low but improve over our sample period. The fact that markets consistently underreacted to

repeated events like hurricanes and only learned slowly is concerning for the efficient pricing

of novel risks caused by climate change. Finally, we further contribute to the asset pricing

literature by taking advantage of our unique empirical setting that allows us to identify ex-

ogenous increases to idiosyncratic volatility to analyze the relationship between idiosyncratic

volatility and expected stock returns.

The remainder of this paper is structured as follows. We describe our research design

and data in Sections 2 and 3, respectively. Section 4 presents our main results, followed by

extensions and robustness tests in Section 5. We conclude in Section 6.

2 Research design

2.1 Theoretical framework on incidence and impact uncertainty

Our framework distinguishes between two types of uncertainty that surround an extreme

weather event: incidence uncertainty and impact uncertainty. Prior to a (potential) extreme

weather event occurring, there is uncertainty regarding whether, when, and where the ex-

treme weather event will occur. We call this incidence uncertainty. Impact uncertainty is

12The dip in trading volume right before landfall is possibly caused by very high uncertainty inhibitingtrading (see Easley and O’Hara (2010)).

13 See, for example, Bloom (2009); Jurado, Ludvigson, and Ng (2015); Baker, Bloom, and Davis (2016)on macroeconomic uncertainty or Julio and Yook (2012); Kelly, Pastor, and Veronesi (2016); Jens (2017) onpolitical uncertainty around scheduled elections.

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the uncertainty regarding how an extreme weather event will impact a firm once the event

occurs. Intuitively, one can think of impact uncertainty as uncertainty about the inten-

sive margin of an extreme weather event and incidence uncertainty as uncertainty regarding

the extensive margin. While the empirical analysis in this paper focuses on hurricanes, our

framework is general enough that it can be applied to other types of extreme weather events.

More formally, if an extreme weather event h is expected to occur at time t+ 1, then an

all-equity firm i’s stock return at t+ 1 is given by

ri,t+1 = εi,t+1 + θi,h,t+1gi,h,t+1, (1)

where εi,t+1 ∼ N(0, σ2) represents a random shock to the firm’s return at time t + 1 that

is unrelated to the extreme weather event.14 The random variable gi,h,t+1 ∼ N(µg, σ2g) is

independent of εi,t+1 and captures the impact of the extreme weather event on the value of

firm i, conditional on the the firm being hit. The random variable θi,h,t+1 indicates whether

firm i is hit by extreme weather event h. θi,h,t+1 has a Bernoulli distribution (one draw of a

binomial distribution), θi,h,t+1 ∼ B(1, φ), where Pr(θi,h,t+1 = 1) = 1 − Pr(θi,h,t+1 = 0) = φ

and 0 ≤ φ ≤ 1. The product of the two random variables, θh,t+1gi,h,t+1, is the component of

the return attributable to the extreme weather event.

Conditional on an extreme weather event occurring at time t + 1, σ2g represents the

impact uncertainty.15 While in our framework, the occurrence of an extreme weather event

introduces impact uncertainty for affected firms, it is unclear empirically how substantial

this uncertainty is. On the one hand, predicting at the time of an event which firms will

be most affected could be challenging due to different factors. Knowing ex ante which

areas will actually flood in a particular storm, the extent and duration of power outages,

whether a levy will break, or how long infrastructure repairs will take, is challenging if not

impossible. On the other hand, firms could be insured against extreme weather events or

move establishments to locations that are less likely to be affected.

Prior to a (potential) extreme weather event, at time t, we can decompose the uncer-

tainty generated for the firm from the event into incidence uncertainty and expected impact

uncertainty as follows.

The expected return conditional on whether or not the extreme weather event occurs is

Et[ri,t+1|θ = 1] = µg and Et[ri,t+1|θ = 0] = 0. The conditional variance of firm i’s return is,

14Here, we have normalized the unconditional drift term of the stock to 0 without loss of generality becausethe extreme weather event is exogenous to εi,t+1. Using εi,t+1 ∼ N(µ, σ2) instead has no impact on equation(6).

15This definition of uncertainty as the variance of an unpredictable disturbance is in line with, for example,Pastor and Veronesi (2012, 2013); Jurado, Ludvigson, and Ng (2015).

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V art(ri,t+1|θ = 0) = σ2, (2)

V art(ri,t+1|θ = 1) = σ2 + σ2g . (3)

It follows that the expected conditional variance16 and the variance of the conditional ex-

pectation17 are, respectively,

E[V art(ri,t+1|θ)] = σ2 + φσ2g , (4)

V ar(Et[ri,t+1|θ]) = φ(1− φ)µ2g. (5)

Applying the law of total variance, we can derive the unconditional variance V art(ri,t+1)

using (4) and (5),

V art(ri,t+1) = E[V art(ri,t+1|θ)] + V ar(Et[ri,t+1|θ]),

= σ2 + φσ2g + φ(1− φ)µ2

g. (6)

Incidence uncertainty is captured in the total variance by the third term in equation (6),

φ(1 − φ)µ2g. For a given µg 6= 0, incidence uncertainty is highest when the probability

of incidence, φ, is 0.5. When µg = 0, meaning that an extreme weather event has no

mean impact on firm returns, there is no contribution from incidence uncertainty to total

variance at time t. In this case, V art(ri,t+1) varies with φ purely due to the expected impact

uncertainty, φσ2g .

Figure 1 depicts how the variance prior to an extreme weather event, V art(ri,t+1), varies

with the probability of incidence φ when σ = 0.4 and σg = 0.05. The four dashed lines

have absolute value of µg of 0.1, 0.07, 0.05, and 0. The horizontal solid line shows the level

of variance conditional on the firm being hit by the extreme weather event, V art(ri,t+1|θ =

1) = σ2 + σ2g . The x-axis intersects the y-axis at the level of variance if the firm is not hit

by the extreme weather event, V art(ri,t+1|θ = 0) = σ2.

Prior to an event, as the probability of incidence, φ, varies from 0 to 1, the relative

contribution to total variance from incidence uncertainty and expected impact uncertainty

will vary depending on the parameter values of µg and σ2g . All else equal, as µg increases,

the contribution of incidence uncertainty to total variance increases. In Figure 1, incidence

uncertainty at a given φ is the vertical distance between a curve and the red dot-dash

straight line depicting V art(ri,t+1) when µg = 0.18 In our empirical analysis, we analyze the

16E[V art(ri,t+1|θ)] = (1− φ)σ2 + φ(σ2 + σ2g) = σ2 + φσ2

g .17E[Et[ri,t+1|θ]] = φµg;V ar(Et[ri,t+1|θ]) = E[(Et[ri,t+1|θ]− E[Et[ri,t+1|θ]])2] = φ(µg − φµg)2 + (1− φ)(0− φµg)2 = φ(1− φ)µ2

g.18V art(ri,t+1) will in fact be greater than V art(ri,t+1|θ = 1) when |µg| > 1√

φσg. In the figure, this is

the case where the dashed lines are above the solid black line. When φ > 0 and at least one of µg or σg isnon-zero, V art(ri,t+1) is always greater than V art(ri,t+1|θ = 0) = σ2.

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uncertainty reflected in option prices prior to a potential extreme weather event at different

probabilities of incidence.

2.2 Firm exposure to hurricanes

We separately determine firm exposure to hurricane forecast and hurricane landfall regions.

In both cases, we first determine which counties are in the forecast path or the landfall region

of a hurricane, and then measure a firm’s exposure based on the share of establishments

located in these counties. Figure 2 Panels A and B show stylized examples of how we measure

a firm’s exposure to a forecast path or a landfall region, respectively. We are agnostic on

the channel through which the hurricane affects a firm. A firm could be negatively affected

through, for example, damage to property, disruption to production processes, or decrease

in demand due to the wealth shock to the local population. On the other hand, a firm

could be positively affected because, for example, demand for its products increases in the

rebuilding process or local competitors were more severely affected. We show a sample of

firm disclosures in Appendix Table A.2, which illustrate how varied the impacts can be. Due

to these range of possible channels, establishment locations is a general way to capture firm

exposure to hurricanes.19

We use hurricane wind speed forecasts to develop daily firm-specific exposures to hurri-

canes before landfall. For each potential hurricane, NOAA issues high frequency location-

specific probabilities of hurricane force winds occurring within the following five day period.

While NOAA updates these forecasts multiple times a day, we use the last forecast made

before market close on each trading day. We denote the set of counties that have a probabil-

ity of at least P of experiencing hurricane level wind speeds, based on the forecast on day t,

as FP,t. Importantly, counties in a forecast hurricane path include both counties later hit by

hurricanes and those spared by evolving hurricane paths. These probabilities facilitate the

connection between our framework from Section 2.1 and our empirical analysis. Forecasts on

the hurricane eye or rainfall lack such granularity and exact probabilities.20 Figure 3 shows a

graphical depiction of an example of NOAA’s wind speed forecasts. We present more detail

on the forecast data in Section 3.1.

We compute firm i’s exposure to the forecast path of hurricane h, Γ days before hurricane

landfall or dissipation, as the share of i’s establishments located in the set of counties in the

19County-firm level sales are used instead of county-firm establishment counts as a robustness check in theInternet Appendix. County-firm sales data are often imputed and hence, less reliable than establishmentlocation data.

20Also, a storm is officially considered a hurricane based on wind speed and not rainfall. There is clearlya strong positive correlation between hurricane wind speed and rainfall. Therefore, the wind speed forecastsalso proxy for rainfall.

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forecast path, FP,Th−Γ. This forecast exposure, a continuous variable ranging from 0 to 1, is

given by

ForecastExposurei,P,Th−Γ =∑c

(FirmCountyExposurei,Th−Γ,c × Ic∈FP,Th−Γ). (7)

Panel A of Figure 2 shows a stylized example of how the ForecastExposure variable is com-

puted, where the shaded blue squares represent the set of counties in the forecast hurricane

path. Because hurricanes occur in different regions across the Atlantic and Gulf coasts, a

firm can have a large exposure to the forecast path of one hurricane but have no exposure

to another hurricane.

We take a similar approach for our post-landfall analyses by determining the set LR,Thof counties located in the landfall region. Using the landfall data described in section 3.2,

we determine a county c to be in the landfall region of a hurricane if the county’s centroid is

within a radius R of the eye of the storm at landfall. The radius accounts for the fact that

hurricanes can impact counties that are not located in immediate proximity to the eye of

the storm through wind and rain. Using data on the eye of the storm location to determine

a hurricane’s landfall region has the distinct advantage that these data are available to

investors in real-time during a hurricane strike.21 We then calculate the share of firm i’s

establishments in counties located in the hurricane’s landfall region. Formally, on landfall

day Th, firm i’s exposure to the landfall region of hurricane h is given by

LandfallRegionExposurei,R,Th =∑c

(FirmCountyExposurei,Th,c × Ic∈LR,Th). (8)

A firm’s exposure to a hurricane landfall region is again a continuous variable ranging from

0 to 1. Similar to the forecast analyses prior to landfall that are performed on a series of

probability thresholds, we perform the landfall analyses for several radii around the eye of

the storm. With larger radii, the average intensity of impact on firms decreases, but the

number of firms with a large share of establishments in the landfall region increases. Panel

B of Figure 2 depicts a stylized example of how the LandfallRegionExposure variable is

computed, with the shaded red squares being the set of counties in the landfall region.

21Alternative data that could be used to determine the landfall region of a hurricane, for example, countylevel damages, are only published by agencies like NOAA and the Federal Emergency Management Agencywith a substantial time lag of at least several months.

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2.3 Baseline estimation strategy

2.3.1 Incidence and impact uncertainty estimation

We employ a difference-in-differences framework to estimate the uncertainty dynamics sur-

rounding hurricanes. We separately analyze hurricane forecasts and landfalls. We jointly

estimate the treatment effect across all hurricanes, where each hurricane landfall (or forecast)

yields a separate treatment. The treatment intensity varies, because treatment is defined

continuously as exposure to the forecast path or landfall region, shown in equations (7) and

(8), respectively. Firms with zero exposure to a hurricane serve as the controls for that event.

We follow the recommendation of Bertrand, Duflo, and Mullainathan (2004) by collapsing

the time series information into a pre- and post-treatment period for each difference-in-

differences, that is, each hurricane. Panel C of Figure 2 illustrates the hurricane timeline.

The pre-treatment period is the trading day before hurricane inception, where the day of

hurricane inception is denoted T ∗h .22 For the hurricane forecast analysis, the post-treatment

period is Γ days before the landfall or dissipation date, Th. For the hurricane landfall analysis,

the post-treatment period is τ days after landfall.

We examine how hurricane forecasts affect implied volatilities of firms located in the path

of a hurricane by estimating the following firm-hurricane panel regression model

log

(IVi,Th−Γ

IVi,T ∗h−1

)= λF,P,ΓForecastExposurei,P,Th−Γ + πh + ψInd + εi,h,Γ. (9)

Here, each hurricane represents a separate time period. The dependent variable is the change

in implied volatility IVi,t of firm i from the last trading day before the inception of hurricane

h, to Γ calendar days before hurricane landfall or dissipation. Details on the IVi,t measure

are presented in Section 3.3. ForecastExposurei,P,Th−Γ is our continuous treatment variable

that ranges from 0 to 1, as defined in equation (7). We include hurricane fixed effects (πh),

which is equivalent to including time fixed effects because each hurricane enters the regression

as a separate time period. We include industry fixed effects (ψInd) based on firm two-digit

SIC numbers. Given the geographic nature of our treatment, we cluster standard errors

by the county to which the firm has the largest exposure (see Abadie, Athey, Imbens, and

Wooldridge (2017)).

We estimate the regression separately for each combination of calendar days before land-

fall Γ ∈ {1, 2, 3, 4, 5} and probability threshold P ∈ {1, 10, 20, 30, 40, 50}. Estimating the

regression for different probability thresholds allows us to empirically trace the predictions on

22The inception day of a hurricane is defined as the first day on which the hurricane is predicted to makelandfall with at least a 1 percent probability. For hurricanes before 2007, we do not have hurricane forecastdata available and choose as inception day the first day that the hurricane appeared as a tropical depression.

11

incidence and expected impact uncertainty developed by our framework and shown in Figure

1. Only storms for which the day Th − Γ is a trading day are included in a regression for a

given Γ. This means that the set of hurricanes included in the regression sample depends on

Γ and P .23 We exclude firms that have missing implied volatility estimates for more than

half of the trading days from inception to Γ days before landfall/dissipation. The time series

for these forecast regressions start in 2007, because we have hurricane wind speed forecast

data from 2007 onwards, and ends in 2017 due to the establishment data time series. In

terms of interpreting results, a positive and significant λF,P,Γ is consistent with firms in the

forecast path of a hurricane facing substantial incidence uncertainty and expected impact

uncertainty prior to landfall (as shown in equation (6)).

After landfall—when the incidence uncertainty has been fully resolved—options should

only reflect impact uncertainty. We isolate and estimate impact uncertainty by examining

the implied volatilities after landfall, when investors know where the hurricane has hit, but

do not necessarily know what the eventual impact on exposed firms will be.

We estimate impact uncertainty using the following firm-hurricane panel regression model,

where again each hurricane enters the regression as a separate time period.

log

(IVi,Th+τ

IVi,T ∗h−1

)= λL,R,τLandfallRegionExposurei,R,Th + πh + ψInd + εi,h,τ , (10)

where the dependent variable is the change in implied volatility from the day before inception

to τ trading days after landfall. LandfallRegionExposurei,R,Th is the measure defined in

equation (8) of firm i’s exposure to counties within the landfall region, which can vary from

0 to 1.24 The regression sample begins in 1996 when the option data time series starts. A

positive and significant λL,R,τ reflects impact uncertainty in the aftermath of a hurricane.

2.3.2 Testing the accuracy of volatility expectations

The question on how accurately investors price climatic event has recently received consid-

erable attention in policy discussions, because inefficient pricing of extreme weather events

is a potential financial stability concern (see Carney (2015)). Other papers analyze how effi-

ciently markets price physical climate risks by looking at food company stock returns (Hong,

23Some storms dissipate or make landfall before reaching higher probability levels in NOAA forecasts.24Because this regression is estimated up to long periods post landfall (for large values of τ), we exclude

firms that have been hit (treated) by a hurricane from the control set of other hurricanes that occur within180 calendar days to avoid distortions due to overlapping. For this purpose, we consider a firm as hit if thelandfall region exposure is at least 0.25. Varying this threshold leads to qualitatively similar results.

12

Li, and Xu (2019)) and mutual fund performance (Alok, Kumar, and Wermers (2020)).25,26

Our analysis sheds light on the accuracy of investors’ volatility expectations, which is in-

strumental for the hedging of climate risks.

To test the efficiency of the volatility expectations derived from option prices, we define

the volatility risk premium as the difference between the ex ante risk-neutral expectation

and ex post realization of return volatility and examine how this spread varies as a firm is

exposed to a hurricane. We use IVi,t,M as the proxy for the ex ante risk-neutral expected

value of the future stock return volatility of firm i between time t and M . Details on the

IVi,t,M measure are presented in Section 3.3. We use the annualized standard deviation of

the underlying stock’s daily returns between t and M as the measure of realized volatility

RVi,t,M .

V RPi,t = V RPi,t,M = IVi,t,M −RVi,t,M . (11)

Note that this definition of VRP captures the difference between ex ante market expectations

of future volatility over a period and the ex post realized volatility over the same period (not

a lagged or predicted measure of physical realized volatility). This is important because we

use our VRP measure to analyze how efficiently investors price the uncertainty associated

with hurricanes, not to predict future returns as in, for example, Bollerslev, Tauchen, and

Zhou (2009) or Della Corte, Ramadorai, and Sarno (2016).27

To analyze the effect of hurricane landfall on VRP, we estimate the regression specification

given by

V RP i,Th+τ = λV RPL,R,τLandfallRegionExposurei,R,Th + πh + Ψi + εi,h,τ , (12)

The dependent variable is the level of the VRP averaged from landfall to τ trading days after

25A different literature focuses on the pricing of a transition to a low carbon economy, as opposed tophysical climatic events (see, for example, Andersson, Bolton, and Samama (2016); Roth Tran (2019);Baker, Hollifield, and Osambela (2020); Engle, Giglio, Kelly, Lee, and Stroebel (2020); Krueger, Sautner,and Starks (2020); Ilhan, Sautner, and Vilkov (2021)). The fact that this transition has not materialized asof now makes it difficult to assess whether financial markets efficiently price this risk.

26For long-run physical risks, for example, sea level rise, assessing pricing efficiency is challenging, becausesuch risks have not yet been realized and can only be proxied for through forecasts with unknown accuracy(see Giglio, Maggiori, Rao, Stroebel, and Weber (2021); Bernstein, Gustafson, and Lewis (2019); Bakkensenand Barrage (2019); Baldauf, Garlappi, and Yannelis (2020); Murfin and Spiegel (2020)).

27The definition of volatility risk premia we use is similar in spirit to definitions of variance risk premia inBollerslev, Tauchen, and Zhou (2009); Kelly, Pastor, and Veronesi (2016) and others. For instance, Kelly,Pastor, and Veronesi (2016) effectively define the variance risk premium as EQ

t [RV 2i,t,M ] − EP

t [RV 2i,t,M ] =

IV 2i,t,M −RV 2

i,t,M because RV 2i,t,M is an unbiased estimate of the expected volatility under the physical mea-

sure. We use volatility risk premia in our empirical analysis for its intuitive interpretation, as in Della Corte,Ramadorai, and Sarno (2016).

13

landfall. Ψi is a firm fixed effect that absorbs differences in VRP levels across firms that are

unrelated to hurricanes.28 A negative (positive) estimate of λV RPL,R,τ implies a decline (increase)

in VRP and is consistent with investor underreaction (overreaction). This would represent a

systematic bias in option prices for hurricane-exposed firms compared to unexposed (control)

firms. The regression specification that estimates the effect of hurricane forecasts on the VRP

is similar to equation (12) with the independent variable of interest being the exposure to

the forecast path instead of the landfall region.

2.3.3 Expected returns and extreme weather uncertainty

Several theories assume that investors are not perfectly diversified and predict that this

underdiversification will lead to expected idiosyncratic volatility being positively related

to the expected stock returns (see Levy (1978); Merton (1987)). However, the empirical

evidence of idiosyncratic shocks to expected volatility being priced in stocks returns is mixed

with Ang, Hodrick, Xing, and Zhang (2006, 2009) finding that high idiosyncratic volatility

predicts low stock returns and Fu (2009) coming to the opposite conclusion.29 Unlike these

papers whose estimates of idiosyncratic volatility depend on a factor model that can be

subject to misspecifications, our setting allows us to exploit an exogenous and idiosyncratic

shock to a stock’s volatility, and to analyze if such a shock is priced in stock returns. Each

hurricane affects a subset of firms distributed across different industries. The vast majority

of firms will be unaffected by a specific hurricane. Moreover, the set of affected firms varies

for each hurricane.

To test how idiosyncratic shocks to a firm caused by extreme weather events are related

to expected returns, we estimate a firm-hurricane panel regression model that is similar to

the previously discussed models. The specification is given by

ExcessReturni,h,PostLandfall − ExcessReturni,h,PreInception =

λERL,RLandfallRegionExposurei,R,Th + πh + ψInd + εi,h, (13)

where LandfallRegionExposurei,R,Th is again our proxy for firm uncertainty caused by a

hurricane landfall. If a hurricane landfall leads to uncertainty and an idiosyncratic increase

in uncertainty is compensated with higher expected returns, then the estimate of λERL,R would

28Unlike with the implied volatility regression (10), it is not possible subtract the pre-inception value ofthe dependent variable in these VRP regressions because the realized volatility over the remaining life of anoption calculated on the pre-inception date, RVi,T∗

h−1,M , will include the impact of the hurricane. Includinga firm fixed effect effectively allows for the estimation of deviations from a firm’s mean VRP.

29Martin and Wagner (2019) derive excess return predictions from option prices. However, their methoddoes not capture idiosyncratic shocks to firms but the differences in firm sensitivity to market wide shocks.

14

be positive and significant.

For our baseline specification, we compute ExcessReturni,h,t as the firm’s stock return

minus the risk-free rate over a horizon of 20 trading days. This horizon is in line with

the approximately one month average time to expiry of options in our sample (see Table

2). For robustness, we also show results using a shorter (10 trading days) and a longer

(40 trading days) horizon in place of 20 trading days. The pre-inception excess returns are

computed over 20 trading days up to the last trading day before hurricane inception. The

post landfall excess returns are computed over 20 trading days starting from 5 trading days

after landfall.30 The independent variable is the exposure to a hurricane’s landfall region, as

defined in equation (8). The time period starts in 1996 and ends in 2017 to correspond to

the option sample used previously.

3 Data and summary statistics

Our analysis uses data from a range of sources. We combine NOAA data on wind speed fore-

casts and realized storm tracks with firm establishment data from the National Establishment

Time-Series (NETS) database. We obtain stock and options data from CRSP-Compustat

and OptionMetrics. We describe each of these data sources below. Additional information

on the hurricane data can be found in the Internet Appendix.

3.1 Hurricane forecasts

We use NOAA’s National Hurricane Center (NHC) wind speed probability forecasts to mea-

sure uncertainty prior to hurricane landfall. Figure 3 shows an example of the forecast chart

of cumulative probability bands for hurricane force winds, as presented by the NHC, in the

case of Hurricane Sandy in 2012. The NHC publishes hurricane forecast charts and text

advisories, both produced from the same underlying hurricane forecast models. The charts

are used in real-time by news outlets in the run-up to hurricanes and stored in NOAA’s

hurricane archives.31

We use these time-stamped text files from the NOAA website which contain the probabil-

ities that a given set of locations, for example, Norfolk, VA, will experience winds in excess of

34, 50, and 64 knots for a particular hurricane over the subsequent days. These forecasts are

updated every six hours and available starting in 2007. We obtain the forecasts just before

market close for each trading day in our analysis. Our analysis is based on the forecasts for

30The results are qualitatively similar to changing the starting point of the post landfall excess return.31The NOAA hurricane archives: https://www.nhc.noaa.gov/archive.

15

64 knots, the minimum wind speed at which a tropical storm is considered a hurricane. The

wind speed probabilities are presented up to five days out from the time of each forecast.

We translate the reported location-specific wind speed forecasts to county specific forecasts

in two steps. First, we determine the set of locations that have reported probabilities of

hurricane force winds above each probability threshold P ∈ {1, 10, 20, 30, 40, 50}, and match

these locations to counties. Second, we add counties that are within a 75 mile radius of the

counties identified in the first step.32 Figure 4 illustrates a sample of processed wind speed

data at different probability thresholds for Hurricane Sandy over a four day period. Table 1

Panel A lists the hurricanes included in our forecast sample. In the Internet Appendix, we

provide further details on how we process the hurricane forecast data.

3.2 Hurricane landfall regions

To identify hurricane landfall regions, we use hurricane track data also collected from the

NOAA hurricane archives. These data show the actual location and intensity of the hur-

ricane’s eye at various points of time. To account for the fact that hurricanes can impact

counties that are not located in immediate proximity to the eye of a storm, we consider

a county to be in the hurricane landfall region if the county’s centroid is inside a given

radius of the hurricane eye location within 24 hours before and after the hurricane makes

landfall.33,34 We generate the sets of counties that lie within 50, 100, 150, 200 miles of the

eye of each hurricane. Having the time window around the time of landfall ensures that we

capture counties that lie more inland and, for hurricanes that move along the coast before

turning inland, counties that were close to the eye of the hurricane before landfall. Figure 5

shows which counties fall within each set for the hurricanes: 2005 Katrina, 2012 Sandy, 2016

Matthew, and 2017 Harvey. Table 1 Panel B lists the hurricanes included in our landfall

region sample. We present additional details on the landfall data in the Internet Appendix.

Importantly, these data are published by NOAA in real-time. Therefore, investors have

access to the information on the landfall region of a hurricane as soon as it makes landfall.

Other papers use damaged counties to discern which firms were affected by natural disasters

(for example, Barrot and Sauvagnat (2016) and Dessaint and Matray (2017).) In our setting,

32For the 75 mile radius, our forecast data derived from NOAA’s text advisories are closely comparableto the charts published by NOAA, for example Figure 3. However, our results are robust to using otherreasonable radii.

33We also consider other time windows, for example, within 12, 36, and 48 hours before and after landfall,and the results are qualitatively similar.

34Two hurricanes in the sample, 2004 Charley and 2005 Katrina, made two landfalls in the US. To avoiddouble-counting with these two hurricanes, the date when the hurricane made landfall at a higher windspeed—corresponding to a higher storm category on the Saffir-Simpson scale—is considered the landfalldate in our analysis.

16

doing so would introduce a forward-looking bias because investors do not know at the time

of a hurricane landfall which counties will experience damage from a hurricane—this is part

of the uncertainty. County-specific damage data only become available with a substantial

lag of at least several months.

3.3 Firm establishment, option, and stock data

We use NETS firm establishment location data to precisely estimate a firm’s exposure to each

hurricane. These data have been used in several other studies.35 The NETS data contain

establishment information at the county level and are updated annually.36 For each hurricane

season, we use firm geographic footprints from the previous year to avoid the possibility that

we will miss establishments closed during the year. Because our NETS data extend only

through 2014, we use the 2014 geographic footprint for 2016 and 2017, in addition to 2015.

Figure 6 shows the number of establishments per county sorted into deciles using the NETS

data for 2010 and 2014. This map illustrates that economic activity as measured by the

density of firm establishments is high in areas exposed to hurricanes along the Atlantic and

the Gulf Coasts.

We obtain daily data on stocks from CRSP-Compustat and single-name stock options

from OptionMetrics. We use standard stock return filters.37 We use data from traded

options with non-missing pricing information in OptionMetrics that are slightly out-of-the-

money. As discussed in previous studies using stock options, such options are more liquid

and have a relatively small difference due to any potential early-exercise premium between

American versus European options (see, among others, Carr and Wu (2009); Kelly, Pastor,

and Veronesi (2016); Martin and Wagner (2019)). We apply standard filters to the options

data consistent with the existing literature. In our sample, we include single-stock options

which meet the following criteria: (i) standard settlement, (ii) a positive open interest, (iii)

a positive bid price and bid-ask spread (valid prices), (iv) the implied volatility estimate is

not missing, (v) greater than 7 days and at most 200 calendar days to expiry, and (vi) an

option delta, δ, that satisfies 0.2 ≤ |δ| ≤ 0.5. The estimate for the average implied volatility

35For example, Neumark, Wall, and Zhang (2011) investigate the job creation of small businesses basedon NETS. Addoum, Ng, and Ortiz-Bobea (2020) use NETS to analyze the effect of temperature fluctuationson firm sales.

36Our baseline results rely on the establishment location data. NETS also contains establishment-levelsales data, but these data are often imputed and not as reliable as the establishment location data. Ananalysis using sales data to compute firm exposure yield qualitatively similar results and is shown in theInternet Appendix.

37To ensure that stocks with stale prices are excluded from our analysis, a stock is required to have returndata for at least half of all trading days within the period of a particular analysis. Further, we exclude stockswith share prices below $5 (see Amihud (2002)) and micro-cap stocks, defined as stocks in the bottom 20percent in terms of market capitalization of listed equity (see Fama and French (2008)).

17

of firm i at time t is,

IVi,t = IVi,t,M =1

N

N∑j=1

IVi,j,t,M , (14)

where M is the nearest-to-maturity expiration at time t of options on firm i stock, which

satisfy the above six criteria and N is the number of valid options for firm i with that expiry.

Here, IVi,t,M proxies for the ex ante risk-neutral expected value of the future stock return

volatility of firm i between time t and M .38

We use firm name and headquarter address to link the firms in NETS to those in Option-

Metrics and CRSP-Compustat, as described in the Internet Appendix. Our linked sample

starts in 1996, the first year in our OptionMetrics data. Because financial firms’ geographic

exposure to natural disasters may not be reflected by their establishment locations and fi-

nancial firms are generally excluded in asset pricing studies, our baseline results exclude all

financial firms by dropping firms with SIC numbers from 6000 to 6799 from our analysis.

We provide a separate analysis on insurance firms in the Internet Appendix.

We report summary statistics on our sample of firms in Table 2. Panel A shows that

we have 2,509 unique firms in our sample. For comparison, we show summary statistics for

the set of firms with significant exposure to a hurricane at least once in our sample period.

A firm is included in this subsample of “hit” firms if its establishment share in the landfall

region of at least one hurricane is 0.25 or higher for a landfall region based on a 200 mile

radius around a hurricane eye. This subsample includes 1,119 firms. On average, a firm

has 113 establishments in a given year. For the subsample of hit firms, the average number

of establishments is 109. Interestingly, these hit firms are also comparable to the non-hit

firms in terms of market capitalization, with a $5.2 billion average market capitalization for

hit firms and an average of $5.0 billion for all firms. The summary statistics of the option

measures are also similar between the total sample and the subsample of hit firms. The

average (annualized) IV and VRP for all firms are 48.7 and 4.8 percent, respectively.

Panel B reports summary statistics on firm exposure to hurricane landfall regions. For

landfall regions based on the 50 and 200 mile radii around the eye of the hurricane, the average

firm establishment share in a given hurricane landfall region is 0.01 and 0.07, respectively.

These values are reasonable, as a hurricane generally only affects a few states and our sample

encompasses firms across the US. Columns five to eight show that our sample includes a large

number of firms with high shares of their establishments in hurricane landfall regions. For

example, for the 50 and 200 mile radii, we have 172 and 2,471 firm-hurricane observations,

respectively, with establishment shares in hurricane landfall regions of at least 0.25.

38This measure of IVi,t is similar to that used in Kelly, Pastor, and Veronesi (2016) for options on inter-national stock indices.

18

4 Baseline Results

4.1 Uncertainty before landfall

We first test whether option prices react to hurricane forecasts before landfall or dissipation

and price in incidence and expected impact uncertainty, as predicted by our framework. The

change in a firms’ implied volatilities should depend on the probability that a hurricane will

make landfall in counties in which the firm operates. The total sample of hurricanes used in

the analysis is listed in Table 1 Panel A. In Table 3, we report results of estimating equation

(9) for each combination of days before landfall (Γ) and hurricane-force wind probability

threshold (P ) for which we have sufficient observations.39

Each column in Table 3 presents results from a separate regression performed for the

specified Γ (1 to 5 days before landfall) and P (1 to 50 percent). Because the location-

specific NOAA wind speed probabilities rarely get high when a hurricane is far from the

coast, the maximum P for which we can estimate equation (9) declines as the number of

days to landfall or dissipation increases. Also, because for a given hurricane Th − Γ might

be a non-trading day, the sample of hurricanes differs across the columns of Table 3. For

example, not all of the 13 hurricanes with 1% probability 5 days before landfall are in the

sample for the regression for 1% probability 2 days before landfall and vice versa. For each

regression, the table reports the total number of firm observations with a forecast exposure

(share of establishments in the forecast region) greater than 0 and at least 0.2. The number

of firms with a given exposure to the forecast path decreases as the probability threshold

increases because the region covered by a higher probability is smaller, as shown in Figure

4, which illustrates the forecast data for Hurricane Sandy.

The results in Table 3 show that substantial uncertainty arises for firms in the forecast

path of a hurricane. The estimates of λF,P,Γ are always positive, regardless of whether time

and industry fixed effects are included separately (Panel A) or interacted with each other

(Panel B). The λF,P,Γ estimates are always significant with the exception of some of the

estimates at the 1% probability threshold in Panel B. For a given Γ, the magnitude of λF,P,Γ

generally increases with higher landfall probabilities, reaching up to 21.40 This implies that

a firm with all its establishments located in the path of a hurricane sees an increase in its

implied volatility of 21 percent. The results in Panels A and B show qualitatively similar

estimates. The coefficients are positive and increase with the probability threshold. The

changes to implied volatility represent substantial increases in hedging costs.

39We require a sample to include at least three hurricanes.40The results for the 30% threshold are omitted to ensure readability of the table, but they are in line

with the reported results.

19

These results show that extreme weather uncertainty is large. Option markets price

in substantial uncertainty before hurricane landfall, in line with the framework presented

in Section 2.1 that shows incidence uncertainty and expected impact uncertainty should

be priced in before hurricane landfall. The empirical estimates confirm that uncertainty

generally increases with the probability of landfall as predicted in Figure 1. Also, these

increases in implied volatilities are not driven by underlying stock price responses to expected

damage. While at the aggregate stock index level, volatility is known to increase when

returns decrease, the relationship changes at the individual stock level. Generally, at the

individual stock level, contemporaneous returns and volatility are positively correlated (see

Duffee (1995); Albuquerque (2012); Grullon, Lyandres, and Zhdanov (2012)).

These estimates of uncertainty before landfall implicitly test for investor attention to

hurricane forecasts. If investors did not pay attention to NOAA’s hurricane forecasts, then

we would not observe an option price reaction. The emerging climate finance literature

investigates investor attention to other climatic events. For example, using drought indices

and food company stock prices, Hong, Li, and Xu (2019) show that investors are inattentive

to droughts. Also, there exists mixed evidence on whether or not residential real estate

owners pay attention to sea level rise forecasts.41 In this context, the strong evidence of

investors paying attention to hurricane forecasts documented in this paper is not a given.

These climatic events are different from one another in terms of, for example, intensity and

duration, and it might be these differences that capture investors’ attention in distinct ways.

4.2 Uncertainty after landfall

We now turn to estimating uncertainty post landfall. Incidence uncertainty resolves at

hurricane landfall, and only impact uncertainty remains. In Table 4, we present results from

estimating equation (10) for 5 trading days (1 week) after landfall in Panel A and for 30

trading days (1.5 months) after landfall in Panel B. We show results from regressions for

which the landfall region is based on different radii around the eye of the storm, ranging

from 50 to 200 miles. The specifications include either separate industry and time (hurricane)

fixed effects or interacted industry and time (hurricane) fixed effects. Table 1 Panel B lists

the 33 hurricanes included in the full sample.

Table 4 Panel A shows that the λL,R,τ estimate goes up to 13 percent, and are positive

and significant across all specifications. Interestingly, while these estimates are large, they

are lower than the increases in IV right before landfall for high probabilities of landfall shown

in Table 3 (there the estimates show increases of up to 21 percent). This result of higher

41See, for example, Bernstein, Gustafson, and Lewis (2019); Giglio, Maggiori, Rao, Stroebel, and Weber(2021); Bakkensen and Barrage (2019); Baldauf, Garlappi, and Yannelis (2020); Murfin and Spiegel (2020).

20

uncertainty before landfall than after landfall is in line with our framework, which shows

that in certain scenarios the total expected variance before the realization of the extreme

weather event can exceed the total expected variance at realization (see Figure 1).

Panel B shows that even 30 trading days (1.5 months) after landfall, hurricanes yield a

large and significant increase in IV. The estimated magnitude of the effect is up to 23 percent

for the 50 mile radius. This implies that relative to its pre-inception IV level, a firm with

100 percent of its establishments in the landfall region will see its implied volatility increase

by 23 percent. These are substantial magnitudes for impact uncertainty. The magnitude of

the effect decreases with larger radii, which implies that firms with establishments located

further away from the epicenter of the storm face less impact uncertainty. Also, while

the statistical significance is stronger 5 trading days post landfall, the coefficient estimates

are often higher 30 trading days after landfall. This result points to a delayed reaction of

investors to hurricane landfall.

In Figure 7, we build on the results in Table 4 by showing how affected firms’ implied

volatilities evolve over the 100 trading days (5 months) after landfall. Each point in the

figure shows the coefficient estimate from a separate regression estimating equation (10) for

a range of trading days after landfall, τ . In Panel A, which uses a 50 mile radius around

the eye of the hurricane to determine a firm’s landfall region exposure, the estimate of λL,R,τ

increases until 30 trading days post landfall, at which point it reaches about 23 percent.

Thereafter, the implied volatility effect gradually decreases until it becomes insignificant

around 60 trading days (3 months) after landfall based on 95% confidence bands. In Panel

B, where we apply a 200 mile radius for the hurricane landfall region, we similarly observe

that the increase in implied volatility rises for some time before peaking and falling back

to baseline. The peak happens earlier at 20 trading days after landfall and has a smaller

magnitude of around 8 percent.

To assess the economic significance of these estimates, we use our regression coefficient

estimates to compute how much hedging costs increase in the aftermath of a hurricane for

investors of firms with exposure to the landfall region, if, hypothetically, investors were

to hedge 100 percent of the equity of exposed firms.42 After hurricane landfall, the total

additional cost of hedging the impact uncertainty over our sample period would have been

as high as 45 to 85 billion U.S. dollars in 2017 inflation-adjusted terms.43 This magnitude

42The higher the implied volatility of an option, all else equal, the higher the option premium, reflectingincreasing costs to hedging.

43These values are based on IV change coefficient estimates for the landfall region of 200 mile radiusaround the eye of the storm, as shown in Table 4, of 3.748 and 6.924 for 5 and 30 trading days post landfall,respectively. These estimates are transformed from log into simple changes and multiplied by the average IVlevel of the firms, 0.48, to obtain the percentage point change in IV for a fully exposed firm. This in turn ismultiplied by the average LandfallRegionExposurei,R,Th

for a firm in the landfall region, 0.14. To obtain

21

is considerable, representing up to 15 percent of the $583 billion (also inflation-adjusted to

2017) in total hurricane damages estimated by NOAA for the same time period (see Table

1).

4.3 Do investors underreact to extreme weather uncertainty?

The results in the previous sections show that investors price in substantial uncertainty before

and after a hurricane makes landfall. A question that naturally follows is how this higher

expected volatility priced in option markets compares to the subsequent realized volatility

for hurricane-hit firms. Do option markets efficiently price the effects of extreme weather on

volatility or is there evidence of underreaction?

The results of the regression specification in equation (12), which compare ex ante market

expectations of future volatility with ex post realized volatility for a firm are reported in

Table 5. Panel A shows the estimates for one day before landfall.44 The coefficient on firm

exposure to the forecast hurricane path is negative in all cases and strongly significant in

the majority of specifications. The economic significance of the coefficient estimates is also

large. The coefficient estimates imply a decrease in VRP of up to 37 percentage points for a

firm with all its establishments in the forecast path of the hurricane. Considering that the

average VRP is around 5 percent (see Table 2), the ex post realized volatility is generally

substantially larger than the ex ante expected volatility for firms in the forecast hurricane

path. Further, VRP becomes more negative—the underreaction is more pronounced—when

landfall probability increases.

Table 5 Panel B shows the effects of hurricanes on average VRP over five trading days

post landfall. In line with investors underreacting to hurricanes, the coefficient estimates are

consistently negative and strongly significant in nearly all specifications. The underreaction

is particularly strong for the firms with establishments within 50 miles of the eye of the

hurricane, which experience 8 to 19 percentage points lower VRPs.

In Figure 8, we show how long it takes for the negative VRP effect due to landfall exposure

to revert back to zero, that is, for the underreaction to resolve. The figure depicts the

estimates of λV RPL,R,τ in equation (12) with VRP averaged over five-trading-day increments post

landfall. In Panel A, the landfall region is based on a radius around the eye of the hurricane

the increase in the option premium, we multiply this average increase in implied volatility by the averagevega of the same options, $0.035, where the option vega is defined as the change in option premium for a1 percentage point change in implied volatility. Finally, we multiply the average premium increase for oneoption of an exposed firm by the total number of shares outstanding of the exposed firms (4,395.8 billion intotal for the period) to obtain the total increase in hedging costs in dollars. The values are inflation-adjustedto 2017 dollars.

44The results are qualitatively similar for two to five days before landfall.

22

of 50 miles. The negative VRP gradually reverts back to zero, but the underreaction persists

for about 2 months (40 trading days). Panel B shows a similar picture but with a faster

reversion of VRP back to 0 for the landfall region based on a 200 mile radius around the eye

of the hurricane.

While these results imply that investors underestimate the hurricanes’ impact on return

volatility, it is possible that this underreaction has diminished over time if investors have

learned to price extreme weather events more efficiently. Particularly, one could imagine

that after a salient hurricane this underreaction disappears for subsequent hurricanes. In our

sample the hurricane most likely to have had such an effect is Hurricane Sandy, which made

landfall in the New Jersey and New York area in 2012. Sandy was not only exceptionally

damaging, as reported in Table 1, but it also hit the financial center of the US, an area that

had previously been largely unaffected by hurricanes, and therefore likely increased investor

attention to hurricanes.

We test whether the negative VRP effect diminished after Hurricane Sandy by estimating

the regression in equation (12) with an additional term that interacts the landfall exposure

variable with a PostSandyh indicator that takes the value one for hurricanes from 2013

onwards. We only conduct the analysis for the post landfall data where we have a large

number of hurricanes available.45 We report the results for 5 and 30 trading days post landfall

in Table 6. The coefficient estimates on the interaction term are always positive and at least

weakly significant for the majority of the specifications. Particularly, for the landfall regions

based on 150 and 200 miles around the eye of the hurricane, where we have more treated

firms and statistical power, the coefficients are strongly significant for all 4 specifications.

The coefficients are also economically large, canceling out the negative coefficient estimate

on LandfallRegionExposure in several specifications. These results suggest that option

markets have started to price extreme weather uncertainty more efficiently. However, it

took a particular salient event for an improvement in efficiency to take place. This finding

casts doubt on whether financial markets will be able to react quickly to climate change and

efficiently price extreme weather events that are novel in terms of their intensity and where

they occur.

The Internet Appendix presents an analysis of the returns to a trading strategy that

takes on the implied volatility exposure (using delta-neutral straddles) at landfall for firms

hit by hurricanes compared to the returns to the same strategy for control firms that are

not exposed. The results are consistent with an underestimation in option prices to the

45For the forecast analysis, the number of hurricanes is as low as three for certain probability thresholdsas shown in Table 5, which does not leave sufficient observations for an analysis after dividing hurricanesinto pre- and post-Sandy.

23

uncertainty generated for firms from hurricane landfall.

4.4 Extreme weather uncertainty and expected returns

Our results in the previous sections show that hurricanes cause a significant increase in

uncertainty for exposed firms. A question that naturally follows is whether this uncertainty

leads to higher expected returns.

If a hurricane strike leads to an increase in excess returns, we would expect the estimate

of λERL,R in equation (13) to be positive and significant. We show the results in Table 7 Panel

A. The coefficient estimates are negative in all but one specification and significant in half

of the cases. These results are inconsistent with investors demanding a premium for holding

stocks exposed to higher uncertainty due to hurricanes. If anything, the results suggest that

the uncertainty surrounding the impact of the hurricane leads to negative excess returns.46

The reason for this finding could be twofold. First, the theories of Levy (1978) and Merton

(1987) may not be empirically supported, and idiosyncratic shocks to the expected volatility

of a stock lead to lower and not higher expected returns. Second, investors may fail to

correctly price the uncertainty associated with a hurricane. Our findings in Section 4.3 show

that after Hurricane Sandy, the implied volatility of option prices more accurately reflects

future realized volatility. If investors price hurricanes more accurately later in the sample, the

natural question to examine is if a similar pattern exists for the expected returns associated

with hurricane strikes.

We extend the regression model in equation (13) by adding a term that interacts our

landfall region exposure variable with an indicator that takes the value one for hurricanes

after Hurricane Sandy. We report the results in Table 7 Panel B. While the coefficient esti-

mate on LandfallRegionExposure is still negative and of a larger magnitude than in Panel

A, the coefficient estimate on LandfallRegionExposure interacted with the post-Sandy in-

dicator is always positive and significant for the majority of specifications. Interestingly, the

magnitude of the coefficient estimate is generally more than double the magnitude of the

estimate on LandfallRegionExposure. These estimates imply that the idiosyncratic shock

to a firm that comes with an exogenous event like a hurricane leads to positive expected

returns and provide empirical support for the theory of Merton (1987).

46We find similar results when analyzing exposure to the forecast path of the hurricane instead of exposureto the (realized) landfall region.

24

5 Robustness and extensions

In this section, we present several extensions and robustness tests of our main results.

5.1 Robustness

5.1.1 Firm selection

A potential concern with our specification is whether our results are driven by small firms.

However, as reported in Table 2, relative to the total sample, the subsample of firms that had

large exposure to a hurricane making landfall at least once, where we define large exposure

as having an establishment share of at least 0.25 in a landfall region, has a comparable, if

slightly higher, average market capitalization. Firms with coastal exposure can differ from

other firms based on unobserved characteristics, but for a given hurricane event, we have

coastal firms in both treated and control sets. A firm that is severely affected to one hurricane

could have zero exposure to others. As such, selection on a set of coastal firms is unlikely

to drive our results. Further, it is possible that firms that would be more vulnerable to

hurricanes because of their particular line of business avoid being exposed to the Atlantic or

Gulf Coasts. However, such sorting would bias us against finding evidence of large extreme

weather uncertainty in option markets.

5.1.2 Industry effects

One question that may arise is whether the uncertainty caused by hurricanes varies substan-

tially across industries. To answer this question, we test whether our baseline post-landfall

results are driven by a particular industry. We choose the post-landfall analysis for this

purpose because the larger number of hit firms provides a more representative sample of

firms for each industry. Building on equation (10), an industry-specific interaction term is

added as follows

log

(IVi,Th+τ

IVi,T ∗h−1

)=λL,R,τLandfallRegionExposurei,R,Th

+ ωL,R,τLandfallRegionExposurei,R,Th × Ii∈Industryg + πh + ψInd + εi,h,τ ,

(15)

where Ii∈Industryg indicates whether firm i is in Industryg, the industry being examined.

We estimate this regression separately for each industry, varying the interacted industry

indicator. Based on a firm’s two-digit SIC number, the analyzed industries are construc-

25

tion, manufacturing, mining, retail, services, transportation, and wholesale.47 If our baseline

effects were driven primarily by one industry, then we would expect λL,R,τ to be statisti-

cally indistinguishable from zero in the regression where we include an interaction with the

indicator for that industry.

In Table 8, we present our results for the 200 mile radius, a landfall region for which we

have a sufficient number of firms in each industry with a large exposure to hurricane landfall

regions.48 The parameter τ is set to five trading days. The estimates of λL,R,τ are positive

and significant in every industry specification, suggesting that our baseline results are not

driven primarily by one sector. The estimate of ωR,τ , the coefficient on the interaction term,

is insignificant for most specifications, suggesting limited industry-specific heterogeneity.

Construction is the only industry for which the estimates of ωR,τ are strongly significant

regardless of fixed effect specification. The negative sum λL,R + ωR,τ for the construction

industry suggests that investors perceive lower uncertainty for construction firms due to

hurricanes, which could reflect an expected boost from rebuilding activity.

5.1.3 Other robustness

We include several robustness tests in the Internet Appendix. In particular, we show that

our baseline results on extreme weather uncertainty before and after landfall are robust to

using county-level sales rather than establishment locations to measure firm exposure to

hurricanes. Finally, we show that our baseline results are not driven by any one particular

hurricane by showing the results are robust to excluding individual hurricanes.

5.2 Long-run impact on firm value

The resolution of the uncertainty following hurricane landfall is likely to lead to a large

cross-sectional dispersion in cumulative abnormal stock returns of hit firms. The resolution

of impact uncertainty may take time and investors may only learn over time how individual

firms are affected by exposure to a hurricane. Moreover, it is also unclear if hit firms will

only be negatively affected by the hurricane, or if some firms can profit from the opportunity

that a hurricane presents. For example, firms could benefit from rebuilding activity and an

increase in demand for their products.

We examine the cross-sectional dispersion of the cumulative abnormal stock returns of hit

firms compared to control firms. We estimate the Fama-French five-factor model (see Fama

and French (2015)) for each stock with 250 trading days (roughly one calendar year) before

47We exclude the agriculture and non-classified categories because of the small number of firms.48The results are qualitatively similar when using smaller radii.

26

the inception day of the hurricane. The coefficient estimates from this first stage regression

are then used to compute abnormal returns for each firm and hurricane as follows:

rai,d = ri,d − (α̂i + β̂1,irm,d + β̂2,irsmb,d + β̂3,irhml,d + β̂4,irrmw,d + β̂5,ircma,d). (16)

We next aggregate the abnormal returns to a cumulative abnormal return from inception to

τ trading days after landfall, raci,T ∗h :Th+τ .

49

Importantly, here we aggregate the cumulative returns from hurricane inception, based on

the initial stock price before the hurricane existed. In Section 4.4, we estimate the hurricane’s

effect on expected returns by analyzing how the hurricane is priced as it evolves.

To account for market shocks that coincide with but are independent of a given hurricane,

we take the cumulative abnormal return for a given firm i and hurricane h and subtract

the mean cumulative abnormal return across all stocks for the concurrent period of the

hurricane. We split the firm-hurricane observations into two groups. One group contains the

cumulative abnormal returns of the hit firms, that is, the firms with at least 25 percent of their

establishments in the hurricane landfall region. The other group contains the cumulative

abnormal returns of the control firms, that is, firms with less than 25 percent establishment

exposure. Then, we compute the differences in percentiles between the cumulative abnormal

return distributions of the hit and control firms across all the hurricanes. We run this analysis

separately for different τ from 5 to 120 trading days (6 months) after landfall.50

We illustrate the results for the 200 mile landfall region in Figure 9.51 At the short

horizons of τ=5 trading days after landfall, the cumulative returns from inception of hit

and control firms are not significantly different across the distribution. After τ=10 trading

days, the larger return dispersion of the hit firms is visible at the left tail of the distribution.

At longer horizons of τ=60 and τ=120 trading days, the hit firms have a substantially

larger dispersion of their returns, in line with the higher realized volatility. Interestingly,

the dispersion is not only driven by the left tail of the distribution. High performing hit

firms have higher abnormal returns than high performing control firms. By 120 trading days

post-landfall, the differences of the hit and control firms’ return distributions are -8.9 and

-7.4 percentage points and strongly significant for the 5th and 10th percentile, respectively.

However, for the 90th and 95th percentiles, the differences are 5.5 and 12.3 percentage points,

respectively, and also strongly significant.

There are numerous reasons why firms in the landfall region might outperform the control

49The results are qualitatively similar when simply using excess returns instead of abnormal returns.50We choose a horizon of 120 trading days as that corresponds to half a calendar year. The hurricane

season lasts half a calendar year (from June to November), and thus, we avoid overlaps with the subsequentyear’s hurricane season.

51Results for other radii are shown in the Internet Appendix.

27

firms. For example, firms could benefit from rebuilding activity, struggling competitors,

population migrations, or generous disaster aid. While other papers find small mean return

effects of hurricanes and natural disasters (see, for example, Barrot and Sauvagnat (2016);

Dessaint and Matray (2017); Lanfear, Lioui, and Siebert (2019)), we find that focusing on

mean returns ignores the large differences in the cross-sectional dispersion of returns.

5.3 Hurricane season effects

Hurricanes off the US Atlantic and Gulf coasts generally occur during the hurricane season

which starts in June and ends in November. The hurricane season could consistently increase

uncertainty and be priced in options. However, because the timing of the hurricane season

does not vary from year-to-year, it is challenging to disentangle hurricane season effects from

other seasonal effects that are unrelated to hurricanes but also affect firms with establish-

ments in coastal locations. Because of that, we use variation in NOAA’s hurricane season

outlooks to estimate the effect of the hurricane season on uncertainty.

NOAA releases hurricane seasonal outlooks in May of each year. Dating back to 2001,

each seasonal outlook reports the probability that the season will be above-normal, near-

normal, or below-normal.52 Panel A of Figure A.1 shows that there is significant variation

in the probabilities reported in these pre-season outlooks.

Examining options with a longer time to expiration than in our baseline analyses (120

to 210 calendar days to expiry), we test if the options of firms that have establishments

located in counties historically affected by hurricanes exhibit higher implied volatilities af-

ter NOAA issues a forecast of a hurricane season with above-normal activity. We choose

options with a longer expiry because they cover the majority of the hurricane season.

We use two methods to determine which counties could be hit by a hurricane during

the hurricane season and construct firm-level variables capturing exposure to a hurricane

season. First we use coastal counties from the Atlantic and Gulf coasts as the set of

counties that could reasonably be exposed to a hurricane in any given hurricane season

(CoastalExposurei,Ts). Second, we use historical landfall regions over the preceding 30

years to compute the annual probability with which a county ends up in the landfall region

of a hurricane (HistoricalHurricaneExposurei,Ts). In the Internet Appendix, we provide

further detail on the counties included in each method.

52See National Weather Service “NOAA 2012 Atlantic Hurricane Season Outlook” https://www.cpc.ncep.noaa.gov/products/outlooks/hurricane2012/May/hurricane.shtml for an example.

28

For the first method, the regression specification is given by

log

(IVi,Ts+5

IVi,Ts−1

)=λS,1CoastalExposurei,Ts

+ λS,2CoastalExposurei,Ts ×AboveNormalSeasonProbTs+ πTs + ψInd + εi,Ts , (17)

where Ts−1 is the last trading day before NOAA’s hurricane season outlook is announced

in May, and Ts+5 occurs 5 trading days later.53 The regression jointly estimates the effect

of the seasonal outlooks for the years 2001 to 2017. Following the methodology in equations

(7) and (8), CoastalExposurei,Ts is a variable that ranges from 0 to 1 and measures the

share of establishments of firm i located in counties along the Atlantic and Gulf coast.

Under the second method of measuring seasonal exposure, we replace CoastalExposurei,Ts

in equation (17) with HistoricalHurricaneExposurei,Ts , which measures the share of a

firm’s establishments located in counties with an elevated probability of being hit during a

hurricane season. The variable AboveNormalSeasonProbs is the probability NOAA issues

in May of a given year for an above-normal hurricane season. A positive estimate of λS,2

would be consistent with investor attention to medium-term seasonal forecasts and imply

heightened uncertainty if the probability of an above-normal season is high.

Table A.1 presents the estimates of equation (17). Panel A shows the results for the spec-

ification using CoastalExposurei,Ts , and Panel B uses HistoricalHurricaneExposurei,Ts .

Across both panels, none of the estimates of λS,2 are statistically significant, and some of

the point estimates even have a negative sign. Thus, we find no support for the hypothesis

that implied volatility increases for exposed firms when NOAA’s hurricane season outlook

reports a high probability of an above-normal season.54

Our results in Section 4.1 established that investors pay close attention to NOAA’s fore-

cast of hurricane paths. What might explain investors not paying attention to seasonal

forecasts? This could result from NOAA’s seasonal forecasts being less predictive of damag-

ing hurricanes making landfall on the Atlantic and Gulf coasts. The scatter plots in Figure

A.1 Panel B show only a weakly positive relationship between the seasonal outlooks and the

number of hurricanes making landfall in a given year. Another potential explanation is that

investors pay no attention to seasonal forecasts because they are medium term and lack the

immediacy of the hurricane path forecasts. Distinguishing between these two explanations

53Varying the window length leads to qualitatively similar results.54The coefficient estimate of λS,1 is positive and marginally significant for some specifications. A possible

explanation is that the saliency of any up coming hurricane season leads to a general increase in uncertaintyin May for firms with establishments located along the Atlantic and Gulf coasts. However, the significanceof the λS,1 estimate is weak and not robust to alternative specifications.

29

is not feasible with the available data.

5.4 Other extensions

The Internet Appendix contains additional analyses. First, while the analyses in this paper

focus on the universe of firms excluding financials, we conduct a similar analysis on stock

options of property and casualty insurance firms and find even larger estimates of extreme

weather uncertainty. Second, in an analysis on option trading volume and open interest, we

find that volume decreases right before landfall, in line with findings that investors refrain

from trading when uncertainty is high (see Easley and O’Hara (2010)). Post landfall, we find

that open interest increases, consistent with an increase in hedging activity (see Hong and

Yogo (2012)). Finally, we analyze the returns to a trading strategy that takes on the implied

volatility exposure of stock options at landfall (using delta-neutral straddles held till expiry)

for firms hit by a hurricane, compared to the returns to the same strategy for control firms

that are not exposed to the hurricane. The results are consistent with an underestimation

in option prices to the uncertainty generated for firms from hurricane landfall.

6 Conclusion

This paper fills a gap in the literature on the uncertainty faced by firms due to extreme

weather. We present a framework that distinguishes between incidence uncertainty (on where

an extreme weather event will occur, if at all) and impact uncertainty (on the consequences to

the local firms and economy following the extreme weather event). We isolate and estimate

extreme weather uncertainty, assess how efficiently extreme weather uncertainty is priced in

option markets, and analyze if investors are compensated for extreme weather uncertainty

with higher expected stock returns.

Using daily hurricane forecasts from NOAA, we find that both incidence uncertainty and

expected impact uncertainty are reflected in option prices before a hurricane makes land-

fall, consistent with our framework. We also show that options of firms operating in regions

affected by hurricanes have considerably higher implied volatility after hurricanes hit, imply-

ing substantial impact uncertainty. The impact uncertainty resolves slowly, and the implied

volatilities return to pre-hurricane levels only several months after landfall. This finding of

significant and prolonged increases to uncertainty for firms hit by hurricanes suggests that

firms are not fully insured or that there is uncertainty regarding the extent to which firms

are insured against such events.

Despite these large increases in the expectations of volatility implied in option markets,

30

we find that investors underestimate a hurricane’s impact on the eventual realized return

volatility of hit firms. After Hurricane Sandy in 2012, the ex ante expectations of future

volatility embedded in the option prices of hit firms are closer to the ex post realized volatility,

suggesting that option markets have improved the efficiency with which they price extreme

weather events. When analyzing expected stock returns following a hurricane strike, we find

further evidence that the efficiency with which markets price hurricane strikes improved later

in the sample. Merton (1987) predicts idiosyncratic shocks to expected volatility of a stock

are compensated with higher expected stock returns. Our results show that while hurricanes

did not cause higher expected returns for hit firms over the whole sample, post Hurricane

Sandy, the expected returns of hit firms are indeed positive in the aftermath of a hurricane

landfall.

Our novel analysis and framework contribute to a nascent climate finance literature that

examines how efficiently financial markets price climatic events. Also, we add to the uncer-

tainty literature by introducing a novel type of uncertainty and showing how idiosyncratic

shocks to firms’ uncertainty are priced. Our results suggest that markets need time to learn

how to price extreme weather events and are unlikely to efficiently price novel climate risks

stemming from climate change.

One potential method to reduce uncertainty associated with extreme weather events and

increase pricing efficiency could be better firm disclosures of exposure to extreme weather

events and associated hedging and insurance decisions. Future research could apply our

framework and methodology to examine uncertainty resulting from other types of extreme

weather events and shed more light on the link between extreme weather uncertainty and

real economic activity.

31

References

Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey Wooldridge, 2017, When

should you adjust standard errors for clustering?, Working Paper.

Addoum, Jawad M., David Ng, and Ariel Ortiz-Bobea, 2020, Temperature shocks and es-

tablishment sales, Review of Financial Studies 33, 1331–1366.

Albuquerque, Rui, 2012, Skewness in stock returns: Reconciling the evidence on firm versus

aggregate returns, Review of Financial Studies 25, 1630–1673.

Alok, Shashwat, Nitin Kumar, and Russ Wermers, 2020, Do fund managers misestimate

climatic disaster risk?, Review of Financial Studies 33, 1146–1183.

Amihud, Yakov, 2002, Illiquidity and stock returns: Cross-section and time-series effects,

Journal of Financial Markets 5, 31–56.

Andersson, Mats, Patrick Bolton, and Frederic Samama, 2016, Hedging climate risk, Finan-

cial Analysts Journal 72, 13–32.

Ang, Andrew, Robert J Hodrick, Yuhang Xing, and Xiaoyan Zhang, 2006, The cross-section

of volatility and expected returns, Journal of Finance 61, 259–299.

, 2009, High idiosyncratic volatility and low returns: International and further us

evidence, Journal of Financial Economics 91, 1–23.

Baker, Steven D., Burton Hollifield, and Emilio Osambela, 2020, Asset prices and portfolios

with externalities, Working Paper.

Baker, Scott R., Nicholas Bloom, and Steven J. Davis, 2016, Measuring economic policy

uncertainty, Quarterly Journal of Economics 131, 1593–1636.

Bakkensen, Laura, and Lint Barrage, 2019, Flood risk belief heterogeneity and coastal home

price dynamics: Going under water?, Working Paper.

Baldauf, Markus, Lorenzo Garlappi, and Constantine Yannelis, 2020, Does climate change

affect real estate prices? only if you believe in it, Review of Financial Studies 33, 1256–

1295.

Barrot, Jean-Noel, and Julien Sauvagnat, 2016, Input specificity and the propagation of

idiosyncratic shocks in production networks, Quarterly Journal of Economics 131, 1543–

1592.

32

Bernstein, Asaf, Matthew Gustafson, and Ryan Lewis, 2019, Disaster on the horizon: The

price effect of sea level rise, Journal of Financial Economics 134, 253–272.

Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan, 2004, How much should we

trust differences-in-differences estimates?, Quarterly Journal of Economics 119, 249–275.

Bloom, Nicholas, 2009, The impact of uncertainty shocks, Econometrica 77, 623–685.

Bollerslev, T., G. Tauchen, and H. Zhou, 2009, Expected stock returns and variance risk

premia, Review of Financial Studies 22, 4463–4492.

Carney, Mark, 2015, Breaking the tragedy of the horizon - climate change and financial

stability, Speech given at Lloyd’s of London (29 September).

Carr, P., and L. Wu, 2009, Variance risk premiums, Review of Financial Studies 22, 1311–

1341.

Della Corte, Pasquale, Tarun Ramadorai, and Lucio Sarno, 2016, Volatility risk premia and

exchange rate predictability, Journal of Financial Economics 120, 21–40.

Dessaint, Olivier, and Adrien Matray, 2017, Do managers overreact to salient risks? Evidence

from hurricane strikes, Journal of Financial Economics 126, 97–121.

Dew-Becker, Ian, Stefano Giglio, Anh Le, and Marius Rodriguez, 2017, The price of variance

risk, Journal of Financial Economics 123, 225–250.

Duffee, Gregory R, 1995, Stock returns and volatility a firm-level analysis, Journal of Fi-

nancial Economics 37, 399–420.

Easley, David, and Maureen O’Hara, 2010, Liquidity and valuation in an uncertain world,

Journal of Financial Economics 97, 1–11.

Engle, Robert, Stefano Giglio, Bryan Kelly, Heebum Lee, and Johannes Stroebel, 2020,

Hedging climate change news, Review of Financial Studies 33, 1184–1216.

Fama, Eugene F., and Kenneth R. French, 2008, Dissecting anomalies, Journal of Finance

63, 1653–1678.

, 2015, A five-factor asset pricing model, Journal of Financial Economics 116, 1–22.

Fu, Fangjian, 2009, Idiosyncratic risk and the cross-section of expected stock returns, Journal

of Financial Economics 91, 24–37.

33

Giglio, Stefano, Matteo Maggiori, Krishna Rao, Johannes Stroebel, and Andreas Weber,

2021, Climate change and long-run discount rate: Evidence from real estate, Review of

Financial Studies Forthcoming.

Grullon, Gustavo, Evgeny Lyandres, and Alexei Zhdanov, 2012, Real options, volatility, and

stock returns, Journal of Finance 67, 1499–1537.

Hassan, Tarek A, Stephan Hollander, Laurence Van Lent, and Ahmed Tahoun, 2019, Firm-

level political risk: Measurement and effects, The Quarterly Journal of Economics 134,

2135–2202.

Hong, Harrison, Frank W. Li, and Jiangmin Xu, 2019, Climate risks and market efficiency,

Journal of Econometrics 208, 265–281.

Hong, Harrison, and Motohiro Yogo, 2012, What does futures market interest tell us about

the macroeconomy and asset prices?, Journal of Financial Economics 105, 473–490.

Ilhan, Emirhan, Zacharias Sautner, and Grigory Vilkov, 2021, Carbon tail risk, Review of

Financial Studies 34, 1540–1571.

Jens, Candace E., 2017, Political uncertainty and investment: Causal evidence from U.S.

gubernatorial elections, Journal of Financial Economics 124, 563–579.

Julio, Brandon, and Youngsuk Yook, 2012, Political uncertainty and corporate investment

cycles, Journal of Finance 67, 45–83.

Jurado, Kyle, Sydney C. Ludvigson, and Serena Ng, 2015, Measuring uncertainty, American

Economic Review 105, 1177–1216.

Kelly, Bryan, Lubos Pastor, and Pietro Veronesi, 2016, The price of political uncertainty:

Theory and evidence from the option market, Journal of Finance 71, 2417–2480.

Krueger, Philipp, Zacharias Sautner, and Laura T Starks, 2020, The importance of climate

risks for institutional investors, Review of Financial Studies 33, 1067–1111.

Lanfear, Matthew G., Abraham Lioui, and Mark G. Siebert, 2019, Market anomalies and

disaster risk: Evidence from extreme weather events, Journal of Financial Markets 46,

1–29.

Levy, Haim, 1978, Equilibrium in an imperfect market: A constraint on the number of

securities in theportfolio, American Economic Review 68, 643–658.

34

Martin, Ian W.R., and Christian Wagner, 2019, What is the expected return on a stock?,

The Journal of Finance 74, 1887–1929.

Merton, Robert C., 1987, A simple model of capital market equilibrium with incomplete

information, Journal of Finance 42, 483–510.

Murfin, Justin, and Matthew Spiegel, 2020, Is the risk of sea level rise capitalized in residen-

tial real estate?, Review of Financial Studies 33, 1217–1255.

Neumark, David, Brandon Wall, and Junfu Zhang, 2011, Do small businesses create more

jobs? New evidence for the united states from the national establishment time series,

Review of Economics and Statistics 93, 16–29.

Pastor, Lubos, and Pietro Veronesi, 2012, Uncertainty about government policy and stock

prices, Journal of Finance 67, 1219–1264.

, 2013, Political uncertainty and risk premia, Journal of Financial Economics 110,

520–545.

Roth Tran, Brigitte, 2019, Divest, disregard, or double down? Philanthropic endowment

investments in objectionable firms, American Economic Review: Insights 2, 241–256.

35

0.2 0.4 0.6 0.8 1.0ϕ

0.161

0.162

0.163

0.164

Vart(rt+1)

σg=0.05,|μg|=0.1 σg=0.05,|μg|=0.07

σg=0.05,|μg|=0.05 σg=0.05,|μg|=0.0

Figure 1: Expected variance as a function of the probability of an event

This figure shows the total variance prior to an extreme weather event, V art(ri,t+1) derived in equation (6),as the probability of the event occurring, φ, varies from 0 to 1. In this figure, σ = 0.4 and σg = 0.05. Thefour dashed lines have absolute value for µg of 0.1, 0.07, 0.05, and 0, respectively. The horizontal solid lineshows the level of variance conditional on the firm experiencing an event, V art(ri,t+1|θ = 1) = σ2 + σ2

g , asdefined in equation (3). The x-axis intersects the y-axis at the level of variance if the firm is not exposed tothe event, V art(ri,t+1|θ = 0) = σ2.

36

(a) Stylized example of firm exposure to hurricane forecast

(b) Stylized example of firm exposure to hurricane landfall region

(c) Hurricane timeline

Figure 2: Identification strategy

Panel A shows a stylized example of firm exposure to a hurricane forecast based on the share of establishmentslocated in counties in the forecast path. These firm exposures illustrate the variable ForecastExposure inour analysis. Panel B shows a stylized example of firm exposure to a hurricane landfall region based on theshare of establishments located in counties in the landfall region. These firm exposures illustrate the variableLandfallRegionExposure in our analysis. Panel C illustrates the timeline of a hurricane.

37

Figure 3: Example of a hurricane forecast

This figure from NOAA illustrates the five-day forecast for Hurricane Sandy on October 27, 2012. We obtainand process the raw data underpinning such hurricane forecast visualizations for our analysis.

38

4 days before landfall

3 days before landfall

2 days before landfall

1 day before landfall

≥1% ≥10% ≥20% ≥50%

Figure 4: Hurricane forecasts at different time frames and wind speed probabilitythresholdsThis figure presents an example of the processed forecast data used in the paper. Each map shows thecounties indicated as being in the forecast path for Hurricane Sandy given the number of days before landfallin each row and the wind speed probability threshold in each column. For each day, the last available forecastbefore 4pm (market close) is shown.

39

(a) 2005 Katrina (b) 2012 Sandy

(c) 2016 Matthew (d) 2017 Harvey

Figure 5: Counties in a hurricane landfall region

This figure highlights the counties that are within 50, 100, 150, and 200 miles of the eye of the hurricane atlandfall for four selected hurricanes from our sample of 33 hurricane landfalls.

40

0.0 2.5 5.0 7.5 10.0

(a) Year 2010

0.0 2.5 5.0 7.5 10.0

(b) Year 2014

Figure 6: Firm establishments by county

This figure plots counties based on the number of establishments located in that county for the years 2010(Panel A) and 2014 (Panel B). Data are from the National Establishment Time Series and only firms in oursample included. The counties are sorted into deciles based on the number of establishments.

41

0

20

40

0 10 20 30 40 50 60 70 80 90 100Trading days after landfall

Per

cent

cha

nge

in im

plie

d vo

latil

ity

(a) 50 mile radius around eye of hurricane

0

4

8

12

0 10 20 30 40 50 60 70 80 90 100Trading days after landfall

Per

cent

cha

nge

in im

plie

d vo

latil

ity

(b) 200 mile radius around eye of hurricane

Figure 7: Changes in implied volatilities post hurricane landfall

This figure plots coefficient estimates from running separate regressions with the specification (10) varyingthe number of tradings days after landfall. Changes in implied volatilities from inception of the hurricane toup to 100 trading days (5 months) post hurricane landfall are regressed on the landfall region establishmentshare of firms. A coefficient estimate of, for example, 20 means that a firm with all of its establishments inthe landfall region is estimated to experience a 20% increase in the implied volatility. The landfall region isbased on a 50 mile radius around the eye of the hurricane in Panel A and 200 mile radius around the eye ofthe hurricane in Panel B. Confidence bands of 95% are shown.

42

−40

−30

−20

−10

0

0 10 20 30 40 50Trading days after landfall

Per

cent

age

poin

t cha

nge

in V

RP

(a) 50 mile radius around eye of hurricane

−5

0

0 10 20 30 40 50Trading days after landfall

Per

cent

age

poin

t cha

nge

in V

RP

(b) 200 mile radius around eye of hurricane

Figure 8: Changes in volatility risk premium post hurricane landfall

This figure plots coefficient estimates from running separate regressions with the specification (12) varyingthe number of tradings days after landfall. A coefficient estimate of, for example, -10 means that a firm withall of its establishments in the landfall region is estimated to have a 10 percentage points lower VRP thanthe control firms. The landfall region is based on a 50 mile radius around the eye of the hurricane in PanelA and 200 mile radius around the eye of the hurricane in Panel B. Confidence bands of 95% are shown. TheVRP is the difference between ex ante expected and ex post realized volatility as shown in equation (11).

43

−10

0

10

20

10 20 30 40 50 60 70 80 90Percentile

Diff

. in

cum

. abn

orm

al r

etur

ns

(a) 5 trading days (1 week) post landfall

−10

0

10

20

10 20 30 40 50 60 70 80 90Percentile

Diff

. in

cum

. abn

orm

al r

etur

ns

(b) 10 trading days (2 weeks) post landfall

−10

0

10

20

10 20 30 40 50 60 70 80 90Percentile

Diff

. in

cum

. abn

orm

al r

etur

ns

(c) 60 trading days (3 months) post landfall

−10

0

10

20

10 20 30 40 50 60 70 80 90Percentile

Diff

. in

cum

. abn

orm

al r

etur

ns

(d) 120 trading days (6 months) post landfall

Figure 9: Differences in cumulative abnormal returns between hit and controlfirms

This figure plots the difference in cumulative abnormal returns between firms with at least 25% of theirestablishments in the landfall region of a hurricane (the hit firms) and firms with less than 25% of theirestablishments in the landfall region (the control firms), as described in Section 5.2. The differences betweenpercentiles of the two return distributions (hit and control) are shown. The cumulative abnormal returns arecomputed from hurricane inception up to 5, 10, 60, and 120 trading days (1 week, 2 weeks, 3 months, and 6months) post landfall. The landfall region is based on 200 miles around the eye of the hurricane. Confidencebands of 95% are shown.

44

Table 1: Hurricane sample

This table shows the hurricanes included in our analyses. Panel A reports the sample for the forecastanalyses, which includes storms that were at least once forecast to make landfall with hurricane force windswith a 1% probability or more. Because the forecasts include storms that never make landfall in the U.S.,we indicate storms that make landfall with asterisks (*). The forecast sample is from 2007 to 2017. PanelB shows the landfall and inception dates for storms that are included in the post-landfall analyses. Thedamage estimates shown come from the National Hurricane Center’s Tropical Cyclone Reports and havebeen inflated to 2017 values using the consumer price index from the U.S. Census Bureau. Landfall datescome from the Tropical Cyclone Reports. The landfall sample is from 1996 to 2017.

Panel A: Hurricanes included in forecast analyses

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Dean Dolly* Ana Alex Don Debby Andrea Arthur* Ana Colin Harvey*Humb.* Edouard Bill Bonnie Emily Isaac* Karen Erika Herm.* Irma*Noel Fay Danny Earl Irene* Leslie Joaquin Matt.* Jose

Gustav* Ida Paula Nate Sandy* MariaHanna Nate*Ike*KylePaloma

Panel B: Hurricanes included in post-landfall analyses

Post-landfall analysis only Forecast and post-landfall analyses

Damages DamagesHurricane Landfall Inception 2017 $mn Hurricane Landfall Inception 2017 $mn

Bertha Jul. 12, 96 Jul. 5, 96 421 Humberto Sep. 13, 07 Sep. 13, 07 N/AFran Sep. 6, 96 Aug. 23, 96 4,994 Dolly Jul. 23, 08 Jul. 20, 08 1,198Danny Jul. 18, 97 Jul. 16, 97 153 Gustav Sep. 1, 08 Aug. 25, 08 5,271Bonnie Aug. 27, 98 Aug. 19, 98 1,085 Ike Sep. 13, 08 Sep. 2, 08 33,692Earl Sep. 3, 98 Aug. 31, 98 119 Irene Aug. 27, 11 Aug. 21, 11 17,258Georges Sep. 28, 98 Sep. 15, 98 9,594 Isaac Aug. 29, 12 Aug. 22, 12 2,514Bret Aug. 23, 99 Aug. 18, 99 89 Sandy Oct. 30, 12 Oct. 24, 12 53,481Floyd Sep. 16, 99 Sep. 7, 99 10,184 Arthur Jul. 4, 14 Jul. 1, 14 2Irene Oct. 15, 99 Oct. 13, 99 1,181 Hermine Sep. 2, 16 Sep. 1, 16 562Lili Oct. 3, 02 Sep. 21, 02 1,264 Matthew Oct. 8, 16 Sep. 30, 16 10,215Claudette Jul. 15, 03 Jul. 8, 03 240 Harvey Aug. 26, 17 Aug. 23, 17 125,000Isabel Sep. 18, 03 Sep. 6, 03 7,175 Irma Sep. 10, 17 Sep. 4, 17 50,000Charley Aug. 13, 04 Aug. 9, 04 19,661 Nate Oct. 8, 17 Oct. 5, 17 225Frances Sep. 5, 04 Aug. 25, 04 12,368Ivan Sep. 16, 04 Sep. 2, 04 24,483Jeanne Sep. 26, 04 Sep. 13, 04 9,965Dennis Jul. 10, 05 Jul. 4, 05 3,202Katrina Aug. 29, 05 Aug. 23, 05 135,894Rita Sep. 24, 05 Sep. 18, 05 15,146Wilma Oct. 24, 05 Oct. 15, 05 26,433

45

Tab

le2:

Fir

mest

abli

shm

ent

and

opti

on

sum

mary

stati

stic

s

Th

ista

ble

rep

orts

the

sum

mar

yst

atis

tics

onth

efi

rms

incl

ud

edin

ou

rsa

mp

le.

Th

esa

mp

leis

from

1996

to2017.

InP

an

elA

rep

ort

sst

ati

stic

sfo

ral

lfi

rms

and

asu

bsa

mp

leof

“hit

”fi

rms.

Hit

firm

sh

ad

at

least

on

cean

esta

bli

shm

ent

share

of

0.2

5or

more

ina

hu

rric

an

ela

nd

fall

regio

n(u

sin

g200

mil

era

diu

sar

oun

dth

eey

eof

the

hu

rric

ane)

.E

stab

lish

men

tsd

ata

are

at

an

an

nu

al

freq

uen

cy;

mark

etca

pit

aliza

tion

at

aqu

art

erly

freq

uen

cy;

an

dall

opti

ons

dat

aar

eat

ad

aily

freq

uen

cy.

Imp

lied

and

reali

zed

vola

tili

tyle

vels

are

an

nu

ali

zed

.P

an

elB

rep

ort

ssu

mm

ary

stati

stic

son

firm

s’es

tab

lish

men

tsh

ares

inhu

rric

ane

lan

dfa

llre

gion

s.T

he

last

fou

rco

lum

ns

show

the

tota

lnu

mb

erof

firm

obse

rvati

on

sw

ith

an

esta

bli

shm

ent

share

that

exce

eds

agi

ven

thre

shold

for

lan

dfa

llre

gion

sbase

don

agiv

enra

diu

saro

un

dth

eey

eof

ahu

rric

an

e.

Pan

elA

:F

irm

char

acte

rist

ics

Nu

mb

erof

un

iqu

efi

rms

2,5

09

Nu

mb

erof

un

iqu

eh

itfi

rms

1,1

19

Avg.

Std

.d

ev.

10

thp

erce

nti

le25

thp

erce

nti

le50

thp

erce

nti

le75

thp

erce

nti

le90

thp

erce

nti

le

Est

abli

shm

ents

113

.304

422.6

32

1.0

002.

000

10.0

00

51.0

00218.

000

Est

abli

shm

ents

hit

firm

s109

.137

382.6

32

1.0

00

3.000

12.0

00

55.0

00

219.

000

Mark

etca

p.

(in

bil

lion

$)5.0

12

22.1

35

0.0

78

0.223

0.7

12

2.3

998.

219

Mark

etca

p.

hit

firm

s(i

nb

illi

on

$)5.2

0920.6

72

0.0

97

0.279

0.8

56

2.8

439.

541

IVi,t

(in

%)

48.7

3427.7

50

22.3

93

29.9

98

41.

792

59.9

42

83.

382

IVi,t

hit

firm

s(i

n%

)47.7

0826.

645

22.

254

29.6

97

41.0

9158.6

32

81.2

06

VRPi,t

(in

%)

4.824

21.5

53-1

3.6

28-2

.330

5.3

60

13.3

92

23.

954

VRPi,t

hit

firm

s(i

n%

)4.

562

21.0

95

-13.6

42

-2.3

86

5.2

36

13.1

11

23.

334

log(IVi,t/IVi,t−

1)

(in

%)

0.1

29

11.

373

-9.7

17

-3.9

05

0.0

38

4.148

10.2

76

log(IVi,t/IVi,t−

1)

hit

firm

s(i

n%

)0.1

2711.

065

-9.5

26

-3.8

50

0.0

28

4.068

10.0

81

Day

sto

exp

iryi,t

36.

374

32.5

0610

.000

17.0

00

26.0

00

38.

000

81.0

00

Day

sto

exp

iryi,t

hit

firm

s35

.447

31.

408

10.

000

17.0

00

26.0

0038.0

00

78.0

00

Tot

al

open

inte

resti,t

2,117.3

418,2

84.

665

13.0

00

50.0

00237.

000

1,1

65.0

00

4,5

01.

000

Tot

al

open

inte

resti,t

hit

firm

s1,

958.8

526,8

25.

444

14.0

00

53.

000

245.

000

1,1

83.0

00

4,4

79.

000

Pan

elB

:F

irm

obse

rvat

ion

cou

nts

and

esta

bli

shm

ent

shar

es(0

to1)

inhu

rric

an

ela

nd

fall

regi

on

Tot

alfi

rmob

serv

ati

ons

wit

hla

nd

fall

regio

nes

tab

.sh

are≥x

Nu

mb

erof

Avg.

firm

SD

firm

Est

ab.

share

Est

ab

.sh

are

Est

ab.

shar

eE

stab

.sh

are

Rad

ius

arou

nd

eye

ofth

ehu

rric

an

ehu

rric

an

eses

tab

.sh

are

esta

b.

share

≥0.1

≥0.2

5≥

0.5

0=

1

50

mil

es33

0.0

08

0.0

43

504

172

64

27100

mil

es33

0.0

27

0.0

84

2,122

742

288

84

150

mil

es33

0.0

48

0.1

18

4,367

1,5

38

619

183

200

mil

es33

0.0

71

0.1

46

6,995

2,4

71

976

298

46

Table

3:

Fore

cast

hurr

icane

path

and

impli

ed

vola

tili

ty

Th

ista

ble

rep

orts

the

coeffi

cien

tsan

dte

stst

atis

tics

wh

enes

tim

ati

ng

the

firm

-hu

rric

an

ep

an

elm

od

elin

equ

ati

on

(9).

Th

em

od

elis

esti

mate

dfo

rd

iffer

ent

pro

bab

ilit

ies

ofla

nd

fall

and

day

sb

efor

ela

nd

fall

or

dis

sip

ati

on

,Γ

.T

he

dep

end

ent

vari

ab

leis

the

chan

ge

(in

%)

inth

eim

pli

edvo

lati

lity

of

firm

ifr

omth

etr

adin

gd

ayb

efor

ehu

rric

ane

ince

pti

on

,T∗ h,

toΓ

day

sb

efore

lan

dfa

llor

dis

sip

ati

on

,Th,

of

the

hu

rric

an

e.H

urr

ican

esfo

rw

hic

hΓ

day

sb

efor

ela

nd

fall

ord

issi

pat

ion

fall

son

an

on-t

rad

ing

day

are

excl

ud

edfo

rth

at

regre

ssio

n.

Th

ein

dep

end

ent

vari

ab

lem

easu

res

how

mu

ch(f

rom

0to

1)

ofth

ege

ogra

ph

icfo

otp

rint

ofa

firm

,in

term

sof

fract

ion

of

esta

bli

shm

ents

,is

inco

unti

eslo

cate

din

the

fore

cast

path

of

ahu

rric

an

e.T

he

fore

cast

pat

hof

the

hu

rric

ane

isfr

omN

OA

Aan

dgi

ves

am

inim

um

pro

bab

ilit

yof

bei

ng

hit

by

asp

ecifi

chu

rric

an

efo

rea

chco

unty

.F

or

each

regre

ssio

n,

the

tota

lnu

mb

erof

firm

obse

rvat

ion

sw

ith

anes

tab

lish

men

tsh

are

inth

efo

reca

stp

ath

of

gre

ate

rth

an

0an

dat

least

0.2

are

rep

ort

ed.

Th

ed

ata

are

from

2007

to20

17.

Th

eva

lues

inpar

enth

eses

are

the

t-st

ati

stic

s.T

he

stan

dard

erro

rsare

clu

ster

edby

cou

nty

base

don

afi

rm’s

larg

est

exp

osu

re.

Ind

ust

ryan

dti

me

fixed

effec

tsar

eu

sed

sep

arat

ely

inP

anel

Aan

dare

inte

ract

edin

Pan

elB

.T

he

tim

efi

xed

effec

tca

nb

ein

terp

rete

das

ahu

rric

an

efi

xed

effec

tas

we

incl

ud

ea

sep

arat

eti

me

per

iod

inth

ep

anel

for

each

hu

rric

an

eas

show

nin

equ

ati

on

(9).

Th

esi

gn

ifica

nce

of

the

coeffi

cien

tes

tim

ate

isin

dic

ate

dby

*fo

rp<

0.10

,**

forp<

0.05

,an

d**

*fo

rp<

0.0

1.

Pan

elA

:W

ith

tim

e(h

urr

ican

e)an

din

dust

ryfixed

effec

ts

Dep

enden

tva

riab

le:

Chan

gein

IVfr

omhurr

ican

ein

cepti

onto

Γday

sb

efor

ela

ndfa

ll/d

issi

pat

ion

(in

%),log( IV i,

Th−

Γ/IVi,T

∗ h−

1

)Γ

1D

ay2

Day

s3

Day

s4

Day

s5

Day

s

Pro

b.

ofhurr

ican

ehit≥

1%10

%20

%40

%50

%1%

10%

20%

40%

50%

1%10%

20%

1%

10%

1%

ForecExposurei,P,T

h−

Γ3.

814∗∗∗

9.58

0∗∗∗

18.3

83∗∗∗

20.8

17∗∗∗

20.

398∗∗∗

1.619∗∗

7.20

4∗∗∗

9.12

4∗∗∗

16.3

52∗∗∗

11.3

33∗

1.2

21∗

8.7

17∗∗∗

14.7

91∗∗∗

1.3

52∗

11.0

11∗∗∗

1.7

88∗∗

(3.5

58)

(6.3

06)

(7.1

64)

(7.7

75)

(7.5

37)

(1.9

72)

(4.4

36)

(4.6

97)

(4.8

05)

(1.8

80)

(1.7

96)

(3.1

18)

(6.0

96)

(1.8

73)

(3.3

71)

(2.4

14)

Adju

sted

R2

12.8

4716

.482

15.8

6716

.284

21.5

7310

.878

12.1

3113

.565

16.4

244.

706

11.6

60

16.1

73

14.2

15

14.8

99

20.1

51

10.9

12

Obse

rvat

ions

36,7

3211

,027

9,86

06,3

004,9

9427

,296

13,6

219,

993

5,04

13,

843

20,7

45

9,8

51

4,908

15,7

78

6,1

76

15,6

67

Obs.

For

ecE

xp

o.>

011

,556

2,81

02,

234

1,3

23

1,091

12,1

644,

059

2,947

1,64

11,

038

10,2

82

2,9

96

1,6

90

8,7

46

2,2

32

7,5

01

Obs.

For

ecE

xp

o.≥

0.2

1,13

216

196

7069

2,51

929

621

811

737

2,48

8212

114

2,9

37

162

1,6

70

Hurr

ican

es30

98

54

2211

84

317

84

13

513

Indust

ryF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esT

ime

(Hurr

ican

e)F

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

Pan

elB

:W

ith

indust

ry×

tim

e(h

urr

ican

e)fixed

effec

ts

ForecExposurei,P,T

h−

Γ2.

486∗∗

5.96

2∗∗∗

12.4

99∗∗∗

13.0

36∗∗∗

13.

391∗∗∗

1.3

18

4.50

6∗∗

6.0

05∗∗

10.8

91∗∗∗

12.0

48∗∗

0.74

84.4

19∗

8.6

93∗∗∗

0.8

37

6.3

20∗∗∗

1.8

21∗∗

(2.3

43)

(3.3

16)

(5.1

30)

(4.5

26)

(4.7

98)

(1.5

26)

(2.4

12)

(2.5

75)

(3.5

68)

(1.9

98)

(1.1

30)

(1.9

51)

(3.4

51)

(1.2

23)

(2.7

94)

(2.3

76)

Adju

sted

R2

13.2

5416

.860

16.2

0016

.685

22.2

4011

.334

12.5

9914

.043

17.1

214.

695

12.2

15

16.7

96

14.8

74

15.8

09

21.1

35

11.4

72

Indust

ry×

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Tim

e(H

urr

ican

e)F

E

47

Table 4: Hurricane effects on implied volatility post landfall

This table reports the coefficients and test statistics when estimating the panel model in equation (10).The dependent variable is the change (in %) in the implied volatility of firm i from the trading day beforehurricane inception, T ∗h , until 5 trading days (1 week) and 30 trading days (1.5 months) after landfall, Th, inPanel A and B, respectively. The independent variable measures how much (from 0 to 1) of the geographicfootprint of a firm, in terms of fraction of establishments, is in counties located in the landfall region of ahurricane. To identify counties that lie in the landfall region of a hurricane we rely on the location of the eyeof the hurricane and a radius of 50, 100, 150, and 200 miles surrounding the eye. The data are from 1996 to2017. The values in parentheses are the t-statistics. The standard errors are clustered by county based ona firm’s largest exposure. Industry and time fixed effects are used. The time fixed effect can be interpretedas a hurricane fixed effect as we include a separate time period in the panel for each hurricane as shown inequation (10). The significance of the coefficient estimate is indicated by * for p < 0.10, ** for p < 0.05,and *** for p < 0.01.

Panel A: Inception to 5 trading days (1 week) after landfall

Dependent variable: Change in IV (in %), log(IVi,Th+5/IVi,T∗

h−1

)Radius around eye of the hurricane

50 miles 100 miles 150 miles 200 miles

LandfallRegionExposurei,R,Th13.009∗∗∗ 8.337∗ 6.193∗∗∗ 4.474∗∗ 3.898∗∗∗ 3.014∗∗ 3.748∗∗∗ 2.511∗∗∗

(2.675) (1.872) (3.363) (2.572) (3.250) (2.560) (3.939) (2.772)

Adjusted R2 (%) 12.229 12.748 12.276 12.821 12.286 12.828 12.330 12.882Observations 33,408 33,408 33,131 33,131 32,863 32,863 32,785 32,785Hurricanes 33 33 33 33 33 33 33 33

Industry FE Yes No Yes No Yes No Yes NoTime (Hurricane) FE Yes No Yes No Yes No Yes NoIndustry × Time (Hurricane) FE No Yes No Yes No Yes No Yes

Panel B: Inception to 30 trading days (1.5 months) after landfall

Dependent variable: Change in IV (in %), log(IVi,Th+30/IVi,T∗

h−1

)Radius around eye of the hurricane

50 miles 100 miles 150 miles 200 miles

LandfallRegionExposurei,R,Th23.481∗∗ 14.085∗ 5.272∗ 1.771 5.659∗∗∗ 3.857∗∗ 6.924∗∗∗ 5.156∗∗∗

(2.535) (1.910) (1.709) (0.718) (3.039) (2.167) (3.403) (2.874)

Adjusted R2 (%) 35.301 35.617 35.609 35.957 35.722 36.054 35.772 36.082Observations 33,485 33,485 33,202 33,202 32,932 32,932 32,853 32,853Hurricanes 33 33 33 33 33 33 33 33

Industry FE Yes No Yes No Yes No Yes NoTime (Hurricane) FE Yes No Yes No Yes No Yes NoIndustry × Time (Hurricane) FE No Yes No Yes No Yes No Yes

48

Table 5: Hurricane effects on volatility risk premium

This table reports the coefficients and test statistics when estimating the panel model in (12). The VRPis computed as the difference between the ex ante expected and ex post realized volatility, as specified inequation (11). The hurricane forecast regressions are in Panel A. The dependent variable is the averageVRP (in %) of firm i from inception of the hurricane, T ∗h , to one day before landfall or dissipation, Th, ofthe hurricane. The regression is estimated separately for different probabilities of landfall. Hurricanes forwhich one day before landfall or dissipation falls on a non-trading day are excluded. The independent variablemeasures how much (from 0 to 1) of the geographic footprint of a firm, in terms of fraction of establishments,is in counties located in the forecast path of a hurricane. The forecast path of the hurricane is from NOAAand gives a minimum probability of being hit by a specific hurricane for each county. The forecast data arefrom 2007 to 2017. The hurricane landfall regressions are in Panel B. The dependent variable is the averageVRP (in %) of firm i from the landfall day of the hurricane, Th, until 5 trading days (1 week) after landfall.The independent variable measures how much (from 0 to 1) of the geographic footprint of a firm, in termsof fraction of establishments, is in counties located in the landfall region of a hurricane. To identify countiesthat lie in the landfall region of a hurricane we rely on the location of the eye of the hurricane and a radiusof 50, 100, 150, and 200 miles surrounding the eye. The landfall data are from 1996 to 2017. In both panels,the values in parentheses are the t-statistics. The standard errors are clustered by county based on a firm’slargest exposure. Firm, time, and time interacted with industry fixed effects are used. The time fixed effectcan be interpreted as a hurricane fixed effect as we include a separate time period in the panel for eachhurricane as shown in equation (12). The significance of the coefficient estimate is indicated by * for p <0.10, ** for p < 0.05, and *** for p < 0.01.

Panel A: Forecast hurricane path

Dependent variable: VRP (in %) avg. over inception to 1 day before landfall, V RP i,Th−1,

Prob. of hurricane hit ≥ 1% 10% 20% 40% 50% 1% 10% 20% 40% 50%

ForecastExposurei,P,Th−1 -2.777 -18.829∗∗∗ -28.531∗∗∗ -35.975∗∗∗ -36.886∗∗∗ -1.551 -9.697∗∗ -15.005∗∗ -17.397 -18.980∗

(-1.520) (-5.320) (-6.012) (-3.801) (-3.613) (-0.880) (-2.266) (-2.433) (-1.622) (-1.686)

Adjusted R2 (%) 34.479 35.254 36.143 44.010 44.348 35.367 36.759 37.595 45.593 46.155Observations 33,910 10,176 9,094 5,813 4,590 33,910 10,176 9,094 5,813 4,590Hurricanes 30 9 8 5 4 30 9 8 5 4

Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTime (Hurricane) FE Yes Yes Yes Yes Yes No No No No NoIndustry X Time (Hurricane) FE No No No No No Yes Yes Yes Yes Yes

Panel B: Post landfall (5 trading days)

Dependent variable: VRP (in %) avg. over 5 trading days post landfall, V RP i,Th+5

50 miles 100 miles 150 miles 200 miles

LandfallRegionExposurei,R,Th-19.297∗∗∗ -8.565∗∗ -7.331∗∗∗ -3.634∗∗ -4.830∗∗∗ -2.502∗ -5.246∗∗∗ -2.942∗∗∗

(-2.873) (-2.501) (-3.693) (-2.102) (-3.324) (-1.921) (-4.020) (-2.777)

Adjusted R2 (%) 28.275 29.704 28.408 29.895 28.628 30.165 28.711 30.209Observations 31,400 31,400 31,121 31,121 30,883 30,883 30,793 30,793Hurricanes 33 33 33 33 33 33 33 33

Firm FE Yes Yes Yes Yes Yes Yes Yes YesTime (Hurricane) FE Yes No Yes No Yes No Yes NoIndustry X Time (Hurricane) FE No Yes No Yes No Yes No Yes

49

Table 6: Hurricane effects on volatility risk premium post Sandy

This table reports the coefficients and test statistics when estimating the panel model in equation (12) witha post Sandy 2012 interaction term added. The VRP is computed as the difference between the ex anteexpected and ex post realized volatility, as specified in equation (11). The dependent variable is the averageVRP (in %) of firm i averaged over 5 and 30 trading days (1 week and 1.5 months), respectively, post landfall,Th. The independent variable measures how much (from 0 to 1) of the geographic footprint of a firm, interms of fraction of establishments, is in counties located in the landfall region of a hurricane. Further, thelandfall region exposure variable is interacted with a dummy variable that takes the value 1 for all hurricanespost Sandy 2012. To identify counties that lie in the landfall region of a hurricane we rely on the locationof the eye of the hurricane and radii of 50, 100, 150, and 200 miles surrounding the eye. The data are from1996 to 2017. The values in parentheses are the t-statistics. The standard errors are clustered by countybased on a firm’s largest exposure. Firm, time, and time interacted with industry fixed effects are used. Thetime fixed effect can be interpreted as a hurricane fixed effect as we include a separate time period in thepanel for each hurricane as shown in equation (12). The significance of the coefficient estimate is indicatedby * for p < 0.10, ** for p < 0.05, and *** for p < 0.01.

Dependent variable: VRP (in %) avg. over τ trading days post landfall, V RP i,Th+τ

Radius around eye of hurricane 50 miles 100 miles 150 miles 200 miles

Trading days post landfall, τ 5 30 5 30 5 30 5 30

LandfallRegionExposurei,R,Th-21.027∗∗∗ -15.385∗∗ -7.936∗∗∗ -4.174∗∗ -5.571∗∗∗ -2.646∗∗ -6.368∗∗∗ -4.474∗∗∗

(-3.179) (-2.551) (-3.905) (-2.419) (-3.344) (-2.239) (-3.894) (-2.946)

LandfallRegionExposurei,R,Th14.406 12.499∗ 3.719 2.656 4.950∗∗ 4.719∗∗ 4.899∗∗ 7.085∗∗∗

×PostSandyh (1.645) (1.680) (1.032) (0.888) (2.126) (2.246) (2.344) (3.672)

Adjusted R2 (%) 29.295 39.730 29.399 39.853 29.610 40.099 29.705 40.377Observations 31,530 31,539 31,251 31,259 31,012 31,019 30,926 30,926Hurricanes 33 33 33 33 33 33 33 33Hurricanes post Sandy 6 6 6 6 6 6 6 6

Firm FE Yes Yes Yes Yes Yes Yes Yes YesTime (Hurricane) FE Yes Yes Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes Yes Yes Yes Yes

50

Table

7:

Hurr

icane

eff

ect

son

exce

ssre

turn

s

Th

ista

ble

rep

orts

the

coeffi

cien

tsan

dte

stst

atis

tics

wh

enes

tim

ati

ng

the

effec

tsof

hu

rric

an

eson

exce

ssre

turn

sth

rou

gh

the

pan

elm

od

elgiv

enin

equ

atio

n(1

3)w

ith

and

wit

hou

tp

ost

San

dy

2012

inte

ract

ion

term

.T

he

dep

end

ent

vari

ab

leis

the

diff

eren

ce(i

np

erce

nta

ge

poin

ts)

bet

wee

nth

ecu

mu

lati

veex

cess

retu

rnof

firm

ip

rein

cep

tion

an

dp

ost

lan

dfa

llof

the

hu

rric

ane.

Th

eh

ori

zon

for

the

exce

ssre

turn

sis

10,

20,

an

d40

trad

ing

day

s,re

spec

tive

ly.

Th

eex

cess

retu

rns

pre

ince

pti

onar

eco

mp

ute

du

pto

the

trad

ing

day

bef

ore

hu

rric

an

ein

cep

tion

,T∗ h−

1.

Th

eex

cess

retu

rns

post

lan

dfa

llar

eco

mp

ute

dfr

om5

trad

ing

day

saf

ter

lan

dfa

ll,Th

+5.

Th

ein

dep

end

ent

vari

ab

lem

easu

res

how

much

(fro

m0

to1)

of

the

geo

gra

ph

icfo

otp

rint

of

afi

rm,

inte

rms

offr

acti

onof

esta

bli

shm

ents

,is

inco

unti

eslo

cate

din

the

lan

dfa

llre

gio

nof

ahu

rric

an

e.In

Pan

elB

,th

ela

nd

fall

regio

nex

posu

reva

riab

leis

inte

ract

edw

ith

ad

um

my

vari

able

that

take

sth

eva

lue

1fo

rall

hu

rric

an

esp

ost

San

dy

2012.

To

iden

tify

cou

nti

esth

at

lie

inth

ela

nd

fall

regi

onof

ahu

rric

ane

we

rely

onth

elo

cati

onof

the

eye

of

the

hu

rric

an

ean

dra

dii

of

50,

100,

150,

an

d200

mil

essu

rrou

nd

ing

the

eye,

resp

ecti

vely

.T

he

dat

aar

efr

om19

96to

2017

.T

he

valu

esin

pare

nth

eses

are

the

t-st

ati

stic

s.T

he

stan

dard

erro

rsare

clu

ster

edby

cou

nty

base

don

afi

rm’s

larg

est

exp

osu

re.

Ind

ust

ryan

dti

me

fixed

effec

tsar

eu

sed

.T

he

tim

efi

xed

effec

tca

nb

ein

terp

rete

das

ahu

rric

an

efi

xed

effec

tas

we

incl

ud

ea

sep

ara

teti

me

per

iod

inth

ep

anel

for

each

hu

rric

ane

assh

own

ineq

uati

on

(13).

Th

esi

gn

ifica

nce

of

the

coeffi

cien

tes

tim

ate

isin

dic

ate

dby

*fo

rp<

0.1

0,

**

forp

<0.

05,

and

***

forp<

0.01

.

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elA

:W

ith

out

San

dy

inte

ract

ion

Dep

end

ent

vari

able

:D

iffer

ence

inex

cess

retu

rns

(in

per

centa

ge

poin

ts),ExcessReturns i,h,PostLandfall−ExcessReturns i,h,PreInception

Rad

ius

aro

un

dey

eof

hu

rric

ane

50

mil

es10

0m

iles

150

mil

es200

mil

es

Exce

ssre

turn

hor

izon

(tra

din

gd

ays)

10

20

40

1020

40

10

20

40

1020

40

LandfallRegionExposurei,R,T

h-3

.488

-0.8

55

2.160

-3.1

63∗∗∗

-4.3

45∗∗∗

-2.4

31

-2.5

81∗∗∗

-2.2

95∗∗∗

-1.1

26

-2.3

43∗∗∗

-2.0

37∗∗∗

-1.4

57

(-1.5

70)

(-0.4

41)

(0.4

56)

(-3.2

31)

(-3.8

11)

(-1.2

19)

(-4.2

40)

(-2.8

14)

(-0.8

07)

(-4.3

92)

(-2.

905)

(-1.1

10)

Ad

just

edR

2(%

)21.

105

31.8

8630.

048

21.

350

32.3

35

30.8

66

21.6

3332.

578

31.2

24

21.6

13

32.5

92

31.2

66

Ob

serv

atio

ns

39,

241

38,9

58

38,4

4138,8

72

38,

593

38,

083

38,5

36

38,2

75

37,7

64

38,5

02

38,2

42

37,7

30

Hu

rric

anes

33

3333

33

33

33

33

33

33

33

33

33

Ind

ust

ryF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esT

ime

(Hu

rric

an

e)F

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

Pan

elB

:W

ith

San

dy

inte

ract

ion

LandfallRegionExposurei,R,T

h-3

.824

-1.2

81

1.864

-3.4

75∗∗∗

-5.1

06∗∗∗

-2.9

79

-3.0

22∗∗∗

-3.1

06∗∗∗

-1.8

05

-2.7

44∗∗∗

-2.8

73∗∗∗

-2.3

53∗

(-1.

640

)(-

0.625

)(0

.374

)(-

3.3

40)

(-4.1

27)

(-1.3

74)

(-4.2

53)

(-3.5

98)

(-1.

285)

(-4.3

81)

(-3.5

80)

(-1.7

84)

LandfallRegionExposurei,R,T

h7.9

849.

958

6.7

80

5.0

40∗

11.

932∗∗∗

8.4

79

5.793∗∗∗

10.4

49∗∗∗

8.624∗∗

3.0

32∗

6.2

64∗∗

6.6

33∗∗

×PostSandy h

(1.4

84)

(1.1

07)

(0.7

38)

(1.8

86)

(2.7

77)

(1.3

34)

(2.7

36)

(3.2

27)

(2.2

84)

(1.8

60)

(2.3

16)

(2.4

52)

Ad

just

edR

2(%

)21.

105

31.8

8630.

047

21.

354

32.3

48

30.8

68

21.6

4832.

602

31.2

31

21.6

23

32.6

12

31.2

76

Ob

serv

atio

ns

39,

241

38,9

58

38,4

4138,8

72

38,

593

38,

083

38,5

36

38,2

75

37,7

64

38,5

02

38,2

42

37,7

30

Hu

rric

anes

33

3333

33

33

33

33

33

33

33

33

33

Hu

rric

anes

pos

tS

and

y6

66

66

66

66

66

6

Ind

ust

ryF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esT

ime

(Hu

rric

an

e)F

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

51

Table

8:

Hu

rric

ane

eff

ect

son

impli

ed

vola

tili

typ

ost

landfa

llw

ith

indust

ryin

tera

ctio

ns

Th

ista

ble

rep

orts

the

coeffi

cien

tsan

dte

stst

atis

tics

wh

enes

tim

ati

ng

the

panel

mod

elin

equ

ati

on

(10)

bu

tin

clu

din

gan

ind

ust

ryd

um

my

inte

ract

ion

term

.T

he

dep

end

ent

vari

able

isth

ech

ange

(in

%)

inth

eim

pli

edvo

lati

lity

of

firm

ifr

om

the

day

bef

ore

the

ince

pti

on

day

of

the

hu

rric

an

e,T∗ h,

unti

l5

trad

ing

day

saf

ter

lan

dfa

ll,Th.

Th

ein

dep

end

ent

vari

ab

lem

easu

res

how

much

(fro

m0

to1)

of

the

geo

gra

ph

icfo

otp

rint

of

afi

rm,

inte

rms

of

fract

ion

ofes

tab

lish

men

ts,

isin

cou

nti

eslo

cate

din

the

lan

dfa

llre

gio

nof

ahu

rric

an

e.T

oid

enti

fyco

unti

esth

at

lie

inth

ela

nd

fall

regio

nof

ahu

rric

an

ew

ere

lyon

the

loca

tion

ofth

eey

eof

the

hu

rric

ane

and

ara

diu

sof

200

mil

essu

rrou

nd

ing

the

eye.

Th

ed

ata

are

from

1996

to2017.

Th

eva

lues

inp

are

nth

eses

are

the

t-st

atis

tics

.T

he

stan

dar

der

rors

are

clu

ster

edby

county

base

don

afi

rm’s

larg

est

exp

osu

re.

Ind

ust

ryan

dti

me

fixed

effec

tsare

use

d.

Th

eti

me

fixed

effec

tca

nb

ein

terp

rete

das

ahu

rric

ane

fixed

effec

tas

we

incl

ud

ea

sep

ara

teti

me

per

iod

inth

epan

elfo

rea

chhu

rric

an

eas

show

nin

equati

on

(10)

.T

he

sign

ifica

nce

ofth

eco

effici

ent

esti

mat

eis

ind

icate

dby

*fo

rp<

0.1

0,

**

forp<

0.0

5,

an

d***

forp<

0.0

1.

Dep

enden

tva

riab

le:

Chan

gein

IV(i

n%

),log( IV i,

Th

+5/IVi,T

∗ h−

1

)In

dust

ryin

tera

cted

wit

hLandfallRegionExposurei,R,T

h

Man

ufa

cturi

ng

Whole

sale

Ser

vic

esT

ransp

ort

ati

on

Ret

ail

Min

ing

Const

ruct

ion

LandfallRegionExposurei,R,T

h6.

482∗∗∗

4.64

5∗∗∗

4.7

90∗∗∗

3.2

22∗∗∗

5.4

49∗∗∗

3.7

23∗∗∗

4.5

87∗∗∗

3.1

04∗∗

5.3

23∗∗∗

3.8

28∗∗∗

3.3

12∗∗

3.55

0∗∗

5.2

68∗∗∗

3.8

55∗∗∗

(4.6

67)

(2.9

58)

(4.0

28)

(2.6

13)

(4.4

72)

(2.9

81)

(3.8

59)

(2.3

71)

(4.7

20)

(3.1

44)

(2.5

63)

(2.5

24)

(4.5

86)

(3.2

24)

LandfallRegionExposurei,R,T

h×I i∈Indg

-3.8

95∗

-2.4

224.8

67

8.9

23

-2.5

51

-0.3

02

2.8

25

3.3

04

-8.2

55-3

.930

8.3

27∗∗

0.77

4-2

3.5

08∗∗∗

-17.

048∗∗

(-1.

711)

(-1.

004)

(1.1

64)

(1.7

79)

(-0.8

16)

(-0.0

88)

(0.7

79)

(0.8

50)

(-1.4

23)

(-0.5

49)

(2.0

52)

(0.1

75)

(-2.

963

)(-

2.003

)

Adju

sted

R2

(%)

12.3

8412.

943

12.3

74

12.9

51

12.3

73

12.9

38

12.3

74

12.9

43

12.3

8012.

940

12.4

1012

.938

12.3

93

12.9

48O

bse

rvat

ions

19,5

7019,

570

19,5

70

19,5

70

19,5

70

19,5

70

19,5

70

19,5

70

19,

570

19,

570

19,5

7019

,570

19,

570

19,5

70O

bse

rvat

ions

inte

ract

ion

indust

ry8,

564

8,5

64610

610

3,674

3,6

74

2,5

75

2,5

75

1,91

91,9

191,7

58

1,75

832

9329

Hurr

ican

es33

3333

33

33

33

33

33

3333

33

3333

33

Indust

ryF

EY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oT

ime

(Hurr

ican

e)F

EY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oIn

dust

ryX

Tim

e(H

urr

ican

e)F

EN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

es

52

Appendix A Additional figures and tables

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

(a) Probability of above average hurricane season

0

1

2

3

4

5

0 0.2 0.4 0.6 0.8 1H

urric

anes

mak

ing

land

fall

in U

.S.

May Outlook on Chance of Above Normal Season

(b) Hurricane landfall count per year

Figure A.1: NOAA’s Atlantic and Gulf Hurricane Season Outlook

Panel A shows the probability of an above average hurricane season that NOAA issues each year in the MayOutlook for the Atlantic and Gulf hurricane season. Whether a season is above average is based on thenumber of hurricanes that are predicted to form in the Atlantic Ocean and the Gulf. Panel B depicts therelationship between the season outlook and the number of hurricanes that make landfall for that season.

53

Table A.1: Hurricane season outlook effects on implied volatility

This table reports the coefficients and test statistics when estimating the panel model in equation (17). Thedependent variable is the change (in %) in the implied volatility of firm i from the last trading day beforeNOAA’s outlook for the hurricane season is released (Ts−1) to 5 trading days thereafter. Longer-dated optionsthat cover the majority of the hurricane season (120 to 210 days to expiry) are used. The independent variableAboveNormalSeasonProbabilityTs is the probability which NOAA assigns to an above average hurricaneseason in terms of number of storms. In Panel A, the independent variable CoastalExposurei,Ts

measuresthe share of a firm’s establishments that are located in Atlantic and Gulf coastal counties. For columns 4and 5, the counties on the Atlantic coast north of Florida are excluded. In Panel B, the independent variableHistoricalHurricaneExposurei,Ts

measures the share of a firm’s establishments (0 to 1) that are locatedin counties that had a probability of being hit by a hurricane in a given season of at least 0.05 and 0.1,respectively, based on hurricane landfalls over the previous 30 years. The data range from 2001 to 2017.The values in parentheses are the t-statistics. The standard errors are clustered by county based on a firm’slargest exposure. Industry and time fixed effects are used separately and interacted. The significance of thecoefficient estimate is indicated by * for p < 0.10, ** for p < 0.05, and *** for p < 0.01.

Panel A: Atlantic and Gulf coast counties

Dependent variable: Change in IV (in %), log(IVi,Ts+5

IVi,Ts−1

)All coastal counties Excl. counties north of FL

CoastalExposurei,Ts0.393 0.437 1.499∗ 1.538∗

(0.571) (0.609) (1.782) (1.711)

CoastalExposurei,Ts0.451 0.374 -0.946 -0.938

×AboveNormalSeasonProbTs(0.391) (0.318) (-0.718) (-0.644)

Adjusted R2 (%) 3.702 4.038 3.713 4.051Observations 18,396 18,396 18,396 18,396

Industry FE Yes No Yes NoTime FE Yes No Yes NoIndustry X Time FE No Yes No Yes

Panel B: Counties selected based on historical probability of being hit

Counties with prob. ≥ 0.05 Counties with prob. ≥ 0.1

HistoricalHurricaneExposurei,Ts0.342 0.245 1.628∗ 1.671

(0.356) (0.239) (1.669) (1.587)

HistoricalHurricaneExposurei,Ts-0.020 0.188 -1.224 -1.209

×AboveNormalSeasonProbTs (-0.015) (0.127) (-0.779) (-0.690)

Adjusted R2 (%) 3.675 4.010 3.710 4.048Observations 18,396 18,396 18,396 18,396

Industry FE Yes No Yes NoTime FE Yes No Yes NoIndustry X Time FE No Yes No Yes

54

Table

A.2

:Sam

ple

Fir

mD

iscl

osu

res

of

Hurr

icane

Impact

Th

ese

illu

stra

tive

exam

ple

ssh

owqu

otes

from

Sec

uri

ties

an

dE

xch

an

ge

Com

mis

sion

(SE

C)

fili

ngs

for

firm

sth

at

had

at

least

half

of

thei

rN

ET

Sd

ata

esta

bli

shm

ents

wit

hin

200

mil

esof

the

eye

ofa

hu

rric

an

e.T

he

ind

ust

ryis

base

don

each

firm

’s2-d

igit

SIC

cod

e.

Ph

enom

enon

Fir

mN

am

e(Industry)

Exce

rpt

Filin

g

No

sign

ifica

nt

neg

ati

ve

imp

act

s

Horn

bec

kO

ffsh

ore

Ser

vic

es,

Inc.

(WaterTransportation)

“T

he

Com

pany

exp

erie

nce

dn

od

am

age

toany

of

its

ves

sels

as

are

sult

of

Hu

rric

an

eIs

aac.

...

Inad

dit

ion

,H

urr

ican

eIs

aac

did

not

resu

ltin

cust

om

erca

nce

llati

on

sof

any

pre

-sto

rmsp

ot

or

term

ves

sel

chart

ers.

...

No

physi

cal

dam

age

rela

ted

toH

urr

ican

eIs

aac

occ

urr

edto

the

Com

pany’s

corp

ora

teh

ead

qu

art

ers

inC

ovin

gto

n,

LA

,w

hic

hre

main

sfu

lly

op

erati

on

al

wit

hall

elec

tric

al

pow

er,

Inte

rnet

con

nec

tivit

yand

tele

com

mu

nic

ati

on

sse

rvic

e.”

Sep

4,

2012

8-K

Physi

cal

dam

age

Bel

lSou

thC

orp

ora

tion

(Communications)

“H

urr

ican

e-re

late

dex

pen

ses

-R

epre

sents

the

incr

emen

tal

lab

or

an

dm

ate

rial

cost

sin

curr

edd

uri

ng

the

thir

dqu

art

erre

late

dto

serv

ice

rest

ora

tion

an

dn

etw

ork

rep

air

sin

the

wir

elin

eb

usi

nes

sd

ue

toH

urr

ican

esC

harl

ey,

Fra

nce

s,Iv

an

an

dJea

nn

e.”

Jan

25,

2005

8-K

Tem

pora

rycl

osu

res

wit

hou

tm

enti

on

of

physi

cal

dam

ages

Sea

Worl

dE

nte

rtain

men

t,

Inc.

(Aumsemen

tand

RecreationServices)

“A

tten

dan

cefo

rth

eth

ird

qu

art

erw

as

als

oad

ver

sely

imp

act

edby

the

effec

tsof

Hu

rric

an

eIr

ma

inF

lori

da,

wh

ich

cau

sed

park

closu

res

inO

rlan

do

an

dT

am

pa,

an

dto

ale

sser

exte

nt,

the

effec

tsof

Hu

rric

an

eH

arv

ey,

wh

ich

cau

sed

park

closu

res

an

dtr

avel

dis

rup

tion

sin

Tex

as

as

wel

las

wea

ther

imp

act

sin

Vir

gin

ia.”

Nov

7,

2017

8-K

Tem

pora

rysl

ow

dow

nin

dem

an

d

En

zoB

ioch

em,

Inc.

(HealthServices)

“E

nzo

Clin

ical

Lab

s,as

note

d,

was

tem

pora

rily

imp

act

edby

Hu

rric

an

eS

an

dy,

suff

erin

gn

op

hysi

cal

imp

air

men

tof

faci

liti

esb

ut

con

stra

ined

by

the

wea

ther

wh

ich

esse

nti

ally

elim

inate

dsp

ecim

envolu

me

for

thre

efu

lld

ays.

”

Dec

10,

2012

8-K

Per

man

ent

dec

lin

ein

dem

an

das

firm

sad

ap

tto

hu

rric

an

esby

relo

cati

ng

Allis

-Ch

alm

ers

En

ergy

Inc.

(Oil&

GasExtraction)

“O

ilan

dn

atu

ral

gas

op

erati

on

sof

ou

rcu

stom

ers

loca

ted

off

shore

an

donsh

ore

inth

eG

ulf

of

Mex

ico

an

din

Mex

ico

may

be

ad

ver

sely

aff

ecte

dby

hu

rric

an

esan

dtr

op

ical

storm

s,re

sult

ing

inre

du

ced

dem

an

dfo

rou

rse

rvic

es.

For

exam

ple

,...

inA

ugu

stto

Oct

ob

erof

2007

we

wit

nes

sed

ad

eclin

ein

off

shore

dri

llin

gri

gop

erati

on

sin

the

Gu

lfof

Mex

ico

inanti

cip

ati

on

of

the

hu

rric

an

ese

aso

n.

Many

of

those

rigs

have

not

retu

rned

toth

eU

.S.

Gu

lfan

dh

ave

bee

nre

loca

ted

toth

ein

tern

ati

on

al

mark

ets.

”

Ju

l25,

2008

8-K

Incr

ease

dd

eman

dO

cean

eeri

ng

Inte

rnati

on

al,

Inc.

(Oil&

GasExtraction)

“In

gen

eral,

ou

rO

ilan

dG

as

bu

sin

ess

focu

ses

on

sup

ply

ing

serv

ices

an

dp

rod

uct

sto

the

dee

pw

ate

rse

ctor

of

the

off

shore

mark

et.

Inth

ep

ast

cou

ple

of

yea

rs,

we

have

had

ah

igh

level

of

dem

an

dd

ue

toh

isto

rica

lly

hig

hhyd

roca

rbon

pri

ces

an

dhu

rric

an

ed

am

age

toth

eoil

an

dgas

pro

du

cin

gin

frast

ruct

ure

inth

eG

ulf

of

Mex

ico.

”

Au

g7,

2008

10-Q

Pri

cech

an

ges

Ply

Gem

Hold

ings,

Inc.

(Lumber&

Wood

Products)

“H

urr

ican

eH

arv

eysi

gn

ifica

ntl

yim

pact

edth

ep

etro

chem

icalin

du

stry

inth

eT

exas

Gu

lfC

oast

wh

ich

resu

lted

insh

ut

dow

ns

of

PV

Cre

sin

manu

fact

ure

rsan

dch

emic

al

feed

erst

ock

toth

ese

PV

Cre

sin

faci

liti

es.

Alt

hou

gh

Ply

Gem

was

ab

leto

ob

tain

an

ad

equ

ate

sup

ply

of

PV

Cre

sin

an

doth

erch

emic

ally

dep

end

ent

raw

mate

rials

,th

em

ark

etco

sts

an

dass

oci

ate

dfr

eight

cost

sh

ave

sign

ifica

ntl

yin

crea

sed

.”

Nov

6,

2017

8-K

Su

pp

lych

ain

effec

ts

Nort

hro

pG

rum

man

Corp

ora

tion(Instru

men

ts&

RelatedProducts)

“In

2008,

the

Exp

edit

ion

ary

Warf

are

bu

sin

ess

had

net

neg

ati

ve

per

form

an

cead

just

men

tsof

$263

million

du

ep

rin

cip

ally

toad

just

men

tson

the

LH

D8

contr

act

,co

stgro

wth

an

dsc

hed

ule

del

ays

on

the

LP

Dp

rogra

man

dth

eeff

ects

of

Hu

rric

an

eIk

eon

asu

bco

ntr

act

or’

sp

erfo

rman

ce.”

Feb

9,

2011

10-K

55

Tab

leA

.2:

Sam

ple

Fir

mD

iscl

osu

res

of

Hurr

icane

Impact

(Conti

nued)

Ph

enom

enon

Fir

mN

am

e(Industry)

Exce

rpt

Filin

g

Insu

ran

ceex

pec

ted

toco

ver

dam

ages

Bri

stow

Gro

up

,In

c.

(Transportationby

Air)

“W

hile

ath

oro

ugh

dam

age

ass

essm

ent

of

the

faci

liti

esis

curr

entl

yu

nd

erw

ay,

itis

exp

ecte

dth

at

loss

esw

ill

be

cover

edby

insu

ran

ce,

net

of

ded

uct

ible

s,an

dth

eref

ore

will

not

have

am

ate

rial

effec

ton

the

Com

panys

fin

an

cial

resu

lts.

”

Oct

6,

2005

8-K

Inco

mp

lete

an

d/or

un

cert

ain

insu

ran

cep

ayou

ts

San

der

son

Farm

sIn

c.

(Food&

Kindred

Products)

“T

he

un

reco

gn

ized

lost

pro

fits

an

dex

pen

ses

of

$5.1

million

wer

eth

ed

irec

tre

sult

of

the

effec

tof

Hu

rric

an

eK

atr

ina

an

dth

eco

mp

any’s

effort

sto

min

imiz

ep

ote

nti

al

loss

esfr

om

the

hu

rric

an

e,an

dw

illb

ere

cogn

ized

as

are

ceiv

ab

leon

cen

egoti

ati

on

sw

ith

ou

rin

sura

nce

carr

iers

are

com

ple

tean

dth

efi

nal

am

ou

nts

are

det

erm

ined

.”

Dec

12,

2005

8-K

Cost

lyp

rep

ara

tion

sto

pre

ven

tim

min

ent

hu

rric

an

ed

am

age

Exact

ech,

Inc.

(Instru

men

ts&

Related

Products)

“W

ew

ere

fort

un

ate

that

ou

rfa

cili

ties

wer

eu

nd

am

aged

du

rin

gth

ehu

rric

an

es.

How

ever

,...

du

eto

hu

rric

an

ep

rep

ara

tion

pro

ced

ure

sat

ou

rG

ain

esville

an

dS

ara

sota

loca

tion

s,ou

rm

anu

fact

uri

ng

an

dsh

ipp

ing

op

erati

on

slo

sttw

od

ays

of

op

erati

ng

cap

aci

ty.

”

Oct

12,

2017

8-K

Inves

tmen

tin

futu

rere

silien

ce

Pep

coH

old

ings(E

lectric,

Gas,

&Sanitary

Services)

“T

oh

elp

imp

rove

ou

rp

erfo

rman

cein

futu

reem

ergen

cies

,w

een

gaged

form

erF

EM

Ad

irec

-to

rJam

esL

eeW

itt

toco

nd

uct

an

ind

epen

den

tev

alu

ati

on

of

ou

rd

isast

erre

spon

seeff

ort

s.M

r.W

itt

an

dh

iste

am

of

exp

erts

...

reco

mm

end

edw

eex

pan

dou

rap

pro

ach

by

trea

tin

gsu

chev

ents

as

com

mu

nit

yd

isast

ers

requ

irin

gm

uch

gre

ate

rco

ord

inati

on

wit

hoth

erre

spon

seagen

-ci

es,ra

ther

than

sim

ply

as

asi

gn

ifica

nt

pow

erou

tage.

Th

eyals

ore

com

men

ded

that

we

ad

op

tad

van

ced

emer

gen

cyp

lan

nin

gp

roce

sses

sim

ilar

toth

ose

use

dby

the

fed

eral

gover

nm

ent,

bu

tn

ot

typ

ically

follow

edby

uti

liti

esin

the

past

.”

Ap

r1,

2004

DE

F14A

Dam

age

ass

essm

ents

take

tim

e

Ap

ach

eC

orp

ora

tion(O

il

&GasExtraction)

“T

he

Com

pany

isco

nti

nu

ing

its

ass

essm

ent

ofd

am

age

cau

sed

by

the

hu

rric

an

es,b

ut

isu

nab

leto

esti

mate

the

ult

imate

cost

sof

ab

an

don

men

tan

dre

pair

at

this

tim

e.”

Nov

9,

2005

10-Q

Ban

kru

ptc

yE

nte

rgy

Corp

ora

tion

(Electric,

Gas,

&Sanitary

Services)

“B

ecau

seof

the

effec

tsof

Hu

rric

an

eK

atr

ina,

on

Sep

tem

ber

23,

2005,

Ente

rgy

New

Orl

ean

sfi

led

avolu

nta

ryp

etit

ion

inth

eU

nit

edS

tate

sB

an

kru

ptc

yC

ou

rtfo

rth

eE

ast

ern

Dis

tric

tof

Lou

isia

na

seek

ing

reorg

an

izati

on

relief

un

der

the

pro

vis

ion

sof

Ch

ap

ter

11

of

the

Un

ited

Sta

tes

Ban

kru

ptc

yC

od

e(C

ase

No.

05-1

7697).

”

Nov

9,

2005

10-Q

Fed

eral

aid

Ente

rgy

Corp

ora

tion

(Electric,

Gas,

&Sanitary

Services)

“E

nte

rgy

pla

ns

top

urs

ue

ab

road

ran

ge

ofin

itia

tives

tore

cover

storm

rest

ora

tion

an

db

usi

nes

sco

nti

nu

ity

cost

san

din

crem

enta

llo

sses

.In

itia

tives

incl

ud

eob

tain

ing

reim

bu

rsem

ent

ofce

rtain

cost

sco

ver

edby

insu

ran

ce,ob

tain

ing

ass

ista

nce

thro

ugh

fed

eralle

gis

lati

on

for

Hu

rric

an

eR

ita

as

wel

las

Hu

rric

an

eK

atr

ina,an

dp

urs

uin

gre

cover

yth

rou

gh

exis

tin

gor

new

rate

mec

han

ism

sre

gu

late

dby

the

FE

RC

an

dlo

cal

regu

lato

ryb

od

ies.

”

Nov

9,

2005

10-Q

Unu

sual

hu

rric

an

e

even

ts

Kir

by

Corp

ora

tion

(WaterTransportation)

“H

isto

rica

lly,

the

imp

act

of

ahu

rric

an

eis

short

tom

ediu

m-t

erm

posi

tive

for

Kir

by

as

are

sult

of

dev

iati

on

sin

the

norm

al

sup

ply

an

dd

istr

ibu

tion

patt

ern

s,le

ad

ing

toin

crea

sed

volu

mes

tran

sport

ed.

How

ever

,w

eh

ave

nev

erex

per

ien

ced

back

tob

ack

Gu

lfC

oast

hu

rric

an

esof

such

magn

itu

des

,m

akin

git

diffi

cult

top

roje

ctre

cover

yvolu

mes

an

dra

tes

from

such

wid

esp

read

dis

rup

tion

s.”

Oct

13,

2005

8-K

56