An Examination of Share Price Behavior Surrounding the 2005

132Journal of Insurance Issues, 2010, 33 (2): 132–151.Copyright © 2010 by the Western Risk and Insurance Association.All rights reserved.

An Examination of Share Price Behavior Surrounding the 2005 Hurricanes Katrina and Rita

Partha Gangopadhyay1, Joseph D. Haley2, and Li Zhang3

Abstract: Natural disasters can affect insurer share prices in two counterbalancingways: (1) through the uncertainty regarding an insurer’s ability to handle loss claimsand (2) through a potential increase in demand for insurance and/or increase ininsurance rates. Given the historically catastrophic damage inflicted by hurricaneKatrina in 2005, and the close geographic and temporal occurrence of hurricane Rita,an evaluation of insurer share price response is warranted. Our analysis shows thatstatistically significant reactions did occur. Insurer share prices reacted negatively tohurricane Katrina. The reaction of insurer share prices to Rita was significant, thoughmixed, with positive/negative results being influenced by Rita’s changing circum-stances. Our study also reveals the level of underwriting exposure to be a significantdeterminant in the magnitude of the insurer share price reactions. [Key words: catas-trophes, share price behavior, market efficiency.]

INTRODUCTION

he stock market is frequently portrayed in an anthropomorphic man-ner by the popular press. It is described as being “nervous” or “jittery.”

The market is one day in a “good” mood and the next day is “downbeat.”While these descriptions are useful when trying to convey a certain image,the market itself is simply a massive compilation of investors’ buy/sell

1Professor of Finance, St. Cloud State University, [email protected] of Risk Management & Insurance, St. Cloud State University, [email protected] Professor of Risk Management & Insurance, St. Cloud State University, ResearchAssociate, University of Calgary, [email protected] authors would like to thank Randy Dumm, Huihua Li, and participants at the 2009Western Risk and Insurance Association meetings for helpful suggestions.

T

SHARE PRICE BEHAVIOR SURROUNDING HURRICANES 133

decisions. These decisions are based on continuous analysis of informationand a continuous forming (and re-forming) of expectations regarding theprospects of companies whose shares are traded in the stock market. For amarket to be considered efficient, share prices must respond quickly to new(and relevant) information.

This paper is an event study that attempts to determine whether theoccurrence of back-to-back 2005 hurricanes, Katrina and Rita, caused inves-tors’ perceptions of insurance company stocks to be blown to and fro likemany of the objects that were in the hurricanes’ paths. This study includesinformation on the pre-landfall projected tracks and intensities of these twohurricanes and evaluates if, and how quickly, insurer share pricesresponded to changes in these projections. Share prices in the days follow-ing the landfalls of Katrina and Rita are also evaluated as information onthe amount of damage inflicted became known.

Investors analyzing catastrophe events and insurance company shareprices are largely concerned with two counterbalancing hypotheses: (1) theability of an insurer’s loss reserves to absorb the damage claims from thecatastrophe, i.e., the damage hypothesis, and (2) the possibility that insur-ers, in the aftermath of the event, experience an increase in demand forinsurance and/or increase in premium rates, i.e., the revenue hypothesis.

A priori, it cannot be predicted which of these two hypotheses willprevail in a given catastrophe. The evidence in the literature is mixed. Thecatastrophic damage inflicted by hurricane Katrina and the arrival ofhurricane Rita just three weeks later provide ideal circumstances to empir-ically re-examine the damage and revenue hypotheses. Our empiricalresults for Katrina unequivocally support the damage hypothesis. The Ritaresults do not provide clear support for either hypothesis. However, wefind share prices responding to the changing circumstances and newsabout both hurricanes in an efficient manner. Our study adds to theresearch on catastrophic natural disasters and to the accumulating empir-ical evidence indicating efficient reaction of insurer stock prices to thearrival of new information.

The paper is organized as follows. Section 2 reviews the literature.Background information on hurricanes Katrina and Rita is reported insection 3. Section 4 describes our sample and discusses the methodology.Empirical results are presented in section 5. The final section summarizesand concludes the paper.

LITERATURE REVIEW

Previous studies have evaluated the responses of property-liabilityinsurer share prices to large unexpected disasters. Some studies support

134 GANGOPADHYAY, HALEY, AND ZHANG

the damage hypothesis. Ewing, Hein, and Kruse (2006) found an overallcumulative negative effect of hurricane Floyd on insurer stock prices. Theirresults also show significant market reaction to the news concerning thepath and strength of the storm prior to landfall. Cummins and Lewis (2003)examined the reaction of the U.S. Property-Casualty insurers’ stock pricesto the World Trade Center terrorist attack of September 11, 2001. Theirempirical findings show insurer stock prices generally declined followingthe WTC attack. Yanase and Yasuda (2010) also analyzed the impact of theWTC attack. They found a (delayed) negative response in the share pricesof Japanese property-casualty insurers. The response was transmitted viathe reinsurance market and did not occur until after a mid-sized Japaneseinsurer, TAISEI Fire and Marine Insurance, became bankrupt due to mas-sive reinsurance claims.4 Yamori and Kabayashi (2002) investigated thereaction of Japanese insurers’ stock value in response to the 1995 Hanshin-Awaji earthquake and found a negative stock price reaction. Lamb (1995,1998) and Angbazo and Narayanan (1996) found that 1992’s hurricaneAndrew had a negative impact on the share prices of the property-liabilityinsurance companies that held significant exposure in Florida and Louisi-ana. Cagle’s (1996) empirical results indicate that hurricane Hugo had anegative impact on the stock price of insurers with high exposure, with nosignificant influence on less-exposed insurers.

Some studies support the revenue hypotheses. Born and Viscusi (2006)studied homeowners’ insurance coverage by state, by firm, and by year for1984–2004. Their results show that insurers adapt to catastrophic risk byraising insurance rates, and that catastrophes reduce the supply of insur-ance in the influenced states. Lamb and Kennedy (1997) found that the 1994Los Angeles earthquake increased the share prices of exposed insurers.Shelor, Anderson, and Cross (1992) and Aiuppa, Carney, and Krueger(1993) found that the 1989 Loma Prieta earthquake in California increasedthe share prices of property-liability insurers.

On a more general note regarding the revenue hypothesis and thecorporate demand for insurance, Lai, McNamara, and Oppenheimer (2002)provided evidence of investors viewing corporate insurance purchases ina positive light. Their study evaluated the share price response of (nonin-surance) firms that experienced large, nonoperating losses. The share priceof firms whose loss was not covered by an insurance contract had asignificant, negative response. The share price response of firms whose losswas covered by an insurance contract was statistically insignificant. These

4The TAISEI Fire and Marine bankruptcy occurred on November 22, 2001, roughly twomonths after the WTC attacks.


results support Mayers and Smith’s (1982) comments on the value ofcorporate insurance purchases.

Some studies show catastrophic events having no significant effectson insurers’ stock price. Aiuppa and Krueger (1995) indicated that exposedinsurers maintained their stock value following the 1994 Los Angelesearthquake. Lamb (1995, 1998) found that hurricane Hugo in 1989 had littleeffect on property-liability insurers regardless of their exposure in thehurricane-affected areas (North and South Carolina).

These mixed empirical results are not at odds with financial theory.Theory does not provide a clear answer to whether insurer share prices,overall, should respond positively or negatively to catastrophic loss events.A positive, negative, or neutral price reaction to a catastrophic loss eventis determined by the counterbalancing forces of the damage and revenuehypotheses.

THE TALES OF TWO HURRICANES

After passing over the southern tip of Florida on August 25, 2005 as aCategory 1 hurricane, Katrina traveled across the Gulf of Mexico, makinga second landfall on August 29 (initially) near Buras, LA. It then continuedon to hit near the Louisiana/Mississippi border. When it made landfall,Katrina was at the “upper end of Category 3 intensity” (Knabb, Rhome,and Brown, 2005: 3) and had maximum sustained winds of 125 mph. Thedamage inflicted by Katrina was catastrophic. Some 1,833 individuals losttheir lives and total damage cost was estimated at $81 billion (Knabb,Rhome, and Brown, 2005).

Less than a month after hurricane Katrina blasted its way onto the GulfCoast near the Louisiana/Mississippi border, hurricane Rita came ashorenear the Louisiana/Texas border. At the time of landfall on September 24,Rita was a Category 3 hurricane with maximum winds of 120 mph. Thedeath toll directly attributable to Rita was seven, while 55 other deaths wereconsidered an indirect result of Rita (Knabb, Brown, and Rhome, 2006: 8).The total property damage of Rita was estimated to be roughly $10 billion.

Track Forecasts

Of key importance to investors establishing expectations about theultimate effects of a hurricane are the assorted predictions leading up tolandfall, namely, announcements regarding expected track and expectedintensity of the hurricane. According to the National Hurricane CenterTropical Cyclone Reports for Katrina and Rita, the “average official trackforecast errors” for both hurricanes were considerably smaller than the


average official (Atlantic) track errors for the preceding 10-year period,1995–2004. However, a comparison of the track forecasts of Katrina andRita reveals some differences.

From late in the day August 26 until landfall in the early morning hoursof August 29, the official track forecasts for hurricane Katrina were “excep-tionally accurate and consistent” (Knabb, Rhome, and Brown, 2005: 14).Figure 1 displays the accurate landfall prediction made on August 26.Subsequent predictions correctly maintained this landfall point.

The track forecasts for hurricane Rita, on the other hand, varied overthe days preceding landfall. The track forecasts on September 20–21 were“biased to the south” of the ultimate landfall (Knabb, Brown, and Rhome,2006: 9). Figure 2 shows the landfall forecast track to be considerablysouthwest of Galveston Bay near Matagorda Bay, whereas just 12 hourslater, the forecast track was almost directly on Galveston Bay (see Figure3). Rita ultimately made landfall near the Louisiana/Texas border.

Fig. 1. The predicted location of hurricane Katrina’s landfall, August 26, Friday, 11 pm.The tracking line represents the “point estimate” of Katrina’s landfall. The white cone-shapedarea represents the probability distribution of the potential landfall.


Intensity ForecastsThe story of intensity forecasts is similar for both Katrina and Rita.

Both hurricanes were correctly predicted, approximately three days inadvance, to be at least Category 3 intensity upon landfall. Both hurricanesalso had a period of unanticipated increased intensity (Category 5) a coupleof days before landfall and then rapidly lost their intensity. The NationalHurricane Center describes the forecasting of “such rapid intensitychanges” as an “operational challenge” (Knabb, Brown, and Rhome, 2006:9). With Rita, this period of rapid intensification occurred on September20–22 while landfall was forecast to be on Galveston Bay, which put thecity of Houston in the hurricane’s path. Figure 3 shows expected landfallat Galveston Bay with maximum wind speeds of 175 mph.

SAMPLE AND METHODOLOGY

Our data contains 58 property-liability insurers for which daily returndata, covering 2002–2005, could be retrieved from the Center for Research

Fig. 2. Prediction of hurricane Rita landfall on September 21, Wednesday, 4 pm.


in Security Prices (CRSP). Of these firms, 28 had some insured exposure inLouisiana, Mississippi, or Texas in 2005. Table 1 contains a listing of the“exposed” firms and “unexposed” firms. Insured exposure should be animportant determinant in how an insurer’s share price will respond to thehurricane track and intensity projections as well as the post-landfall dam-age reports. This does not, however, rule out the possibility of industry-wide contagion (Angbazo and Narayanan, 1996). Stock prices of unex-posed insurers may also be affected to some extent.

The daily CRSP returns for each of these 58 firms are used to calculatethe market model parameters for three different quiet periods. Quiet period#1 is from January 1, 2002 through July 31, 2004. September and October2003 are excluded from quiet period #1 because a Category 5 hurricane,Isabel, made landfall in North Carolina on September 18, 2003. Quietperiod #2 includes all of quiet period #1 plus the eight-month period fromNovember 1, 2004 through June 30, 2005. August through October 2004 areexcluded in quiet period #2 because several Atlantic hurricanes (Charley,Frances, Ivan, and Jeanne) made landfall during that time. Quiet period #3

Fig. 3. Prediction of hurricane Rita landfall on September 22, Thursday, 4 am.


Table 1. The “Exposed” Firms and “Unexposed” Firms in Data Sample

Firms WITH exposure in Louisiana, Mississippi, or Texas

Firms WITHOUT exposure inLouisiana, Mississippi, or Texas

Alleghany Corp De* Affirmative Insurance Hldgs*

Allstate Corp* Allmerica Financial Corp

American International Group* American Financial Group Inc*

Assurant Inc* Commerce Group Inc Mass*

Berkley W R Corp* Danielson Holding Corp

Berkshire Hathaway Inc Del* Enstar Group Inc Ga

C N A Financial Corp* Fremont General Corp*

Chubb Corp* Gainsco Inc*

Direct General Corp* Loews Corp*

Donegal Group Inc* Mercer Insurance Group Inc*

Fairfax Finl Holdings Ltd* Merchants Group Inc*

Horace Mann Educators Corp N* Millea Holdings Inc

Infinity Property & Casualty* Municipal Mortgage & Equity

Kingsway Financial Services* N Y M A G I C Inc*

Markel Corp* Penn America Group

Mercury General Corp New* Odyssey Re Holdings Corp*

Old Republic International C* P I C O Holdings Inc*

St Paul Travelers Cos Inc Phoenix Cos Inc*

Scor* Procentury Corp*

Bristol West Holdings* Prudential Plc*

Endurance Specialty Holdings* R T W Inc*

United Fire & Cas Co* Safety Insurance Group Inc*

Zenith National Insurance Co* Tokio Marine & Fire Ins

Arch Capital Group Ltd* United National Group

Aspen Insurance Holdings Ltd* Transatlantic Holdings Inc*

Quanta Capital Holdings Ltd* 21st Century Insurance Group*

Renaissancere Holdings Ltd* Ace Ltd*

White Mountains Ins Group In Max Re Capital Ltd

Platinum Underwriters Hldgs*

P X R E Group Ltd Bermuda*

An insurer’s exposure in a particular state is computed by the following: Direct WrittenPremiums in the State)/(Direct Written Premiums Total). The data source is A.M. Best,Best’s Statement File: Property/Casualty-United States. *Denotes subset of companiesused for the regression estimates of equations (3a) and (3b).


is April 2004 through June 2005, excluding August, September, and Octo-ber of 2004. The empirical results for all three quiet periods are very similar.Because of this, only the results from quiet period #1 are reported.

The market model parameters are estimated from the following time-series regression:

(1)

where Rit is the (quiet period) stock return of firm i on day t, and MKTt isthe market return on day t.

The daily abnormal return for day t, ARit, during the event periods iscomputed for each firm i as follows:

. (2)

The cumulative abnormal return (CAR) for firm i, over an eventwindow (t1,t2) are computed by summing the daily abnormal returns overeach day in the event window. Z-statistics for the CARs are computed usingthe method outlined in Liu, Smith, and Syed (1992).

A further analysis of the CAR–1,+1 results is conducted by estimatingthe parameters from the following cross-sectional regressions:

(3a)

(3b)

where CARj(t1,t2) is the cumulative abnormal return on firm j’s stock overthe three-day (–1, +1) event window (day 0 is the date of landfall, or thefirst trading day after the landfall) for each hurricane. EXPj is a dummyvariable that takes on a value of 1 if firm j has direct premiums written inthe affected states, and 0 otherwise. DEXPj is the ratio of the dollar amountof direct premiums written in the affected states and the total directpremiums written for firm j. For hurricane Katrina, the affected states wereLouisiana and Mississippi, whereas states affected by Rita were Texas andLouisiana. SIZEj is logarithm of market capitalization of firm j in June 2005.MBj is the market-to-book ratio of firm j in June 2005. The SIZE and MBvariables are included to control for the effect of size and market valuation(Fama and French, 1992, 1995, 1996).

Rit αi βi MKTt( ) εit+ +=

ARit Rit α̂i β̂i MKTt( )+( )–=

CARj t1 t2,( ) λ1 η1EXPj γ1SIZEj δ1MBj εj+ + + +=

CARj t1 t2,( ) λ2 η2EXPj γ2SIZEj δ2MBj εj+ + + +=


EMPIRICAL RESULTS

Katrina: Daily and Cumulative Abnormal Returns

Table 2 reports the daily average abnormal returns for firms withexposures to hurricanes Katrina and Rita, and for firms without anyexposure in the states that were affected by the hurricanes. Katrina’s dailyabnormal returns are reported for the (–4, +10) window, with day 0 beingAugust 29, the day of landfall.5 All of the daily abnormal returns, for bothexposed and unexposed firms, are either significantly negative or insignif-icant except on day +10, when the exposed firms experienced significant,positive results. The exposed firms experienced significantly negativereturns on days –3, 0, +1, +2, +4, and +7, whereas the unexposed firmsexperienced significantly negative returns only on days 0 and +1. All otherdays’ results for the unexposed firms are insignificant.

The significantly negative returns for days –3, 0, +1, and +2 (exposedand unexposed firms) can reasonably be explained as investors anticipat-ing and responding negatively to the events in New Orleans. The signifi-cantly negative results for days +4 and +7 (exposed firms) coincide closelywith Risk Management Solutions revised insured loss estimates. RiskManagement Solutions made a preliminary estimate, on August 29 (day0), of $10–$25 billion in insured losses. On September 2 (day +4), thisestimate was revised upward to $20–$35 billion, and on September 9 (day+8) revised upward again to $40–$60 billion (Risk Management Solutions,2005a, 2005b, 2005c).

The only day with a significantly positive return is September 13 (day+10) and occurs with only the exposed firms. This result can possibly beexplained as an optimistic reaction to a series of September 8–12 newsreports detailing a Congressional appropriation of an additional $51.8billion in relief funding as well as changes in FEMA leadership (ThinkProgress).

Table 3 reports the average CARs6 for firms with and without exposureto Katrina and Rita. The equal-weighted and exposure-weighted CAR–1,+1results for Katrina all reveal a negative response over the three-day eventwindow surrounding the August 29 landfall. The equal-weighted CARsshow a larger negative response for the exposed firms, possibly indicatingthat while investors believed all property-liability insurers were negativelyaffected by Katrina, the exposed insurers were more severely affected.

5This window is four days prior to landfall through 10 days after landfall.6Two versions of CAR values are reported in Table 3, equal-weighted and weighted bydegree of exposure.


The exposure-weighted CARs are larger in absolute value than their equal-weighted counterparts, also implicating the degree of exposure as animportant factor to investors.

Katrina’s consistently negative empirical results, daily and CAR–1,+1,give strong support for the damage hypothesis. The daily share pricereactions closely mirror the release of new (more severe) loss estimates. Theday 0 insured loss estimates were as high as $25 billion. These estimatesgrew to as large as $60 billion by day 8. As Katrina approached Louisianaand made landfall, the reports started out bad and became worse. Itappears investor uncertainty increased with regard to insurer loss reservesbeing large enough to absorb the damage claims.

Rita: Daily and Cumulative Abnormal Returns

The daily abnormal returns window for Rita (as reported in Table 2) is(–5, +10). The exposed firms experienced significantly negative dailyabnormal returns on days –5, –3, –2, and +7, but these same firms experi-enced significantly positive returns on days –4, –1, and +3. These oscillatingresults can be explained by press releases detailing the changing featuresof Rita.

On September 21 (day –3) Rita intensified from Category 2 to Category5 and early on September 22 reached a peak intensity of 180 mph, withGalveston and Houston in its predicted path (Figure 3). The negativeabnormal returns on days –3 and –2 probably reflect heightened investoranxiety over the intensification of the hurricane on September 21 and 22.Later in the day on September 22, Rita began to weaken and continued todo so after landfall on September 24. The positive abnormal returns onSeptember 23 (day –1) reflect the investors’ adjustment on their previousreaction to the expected potential loss. The unexposed firms had signifi-cantly negative returns only on September 22 (day –2), and significantlypositive results on days –1, 0, and +3. Compared with hurricane Katrina,which produced only one positive daily abnormal return, Rita resulted infour days with positive abnormal returns (for all firms). The estimated$5.97 billion in insured losses caused by Rita were much less than the $43.6billion in insured losses for Katrina.

The equal-weighted CAR–1,+1 results for Rita (Table 3) reveal a positivecumulative return for all firms, with a larger positive result for the exposedfirms. Since Rita became weaker on day –1, the positive accumulatedabnormal return over days –1 to 1 is consistent with an efficient market andinvestors’ making a quick adjustment to changing news. As with Katrina,the exposure-weighted CAR–1,+1 results (exposed firms) concur with theequal-weighted results and provide evidence that investors consider thedegree of exposure an important factor.


The oscillating empirical results for Rita do not clearly support thedamage or the revenue hypothesis. These results do, however, demonstrateefficient market reactions to the changing features of Rita. The negativereturns for days –3 and –2 provide some initial support for the damagehypothesis, especially considering that Galveston and Houston were (tem-porarily) in Rita’s path. The weakening and altering of Rita’s path, andthe subsequent positive abnormal daily returns, as well as the positiveCAR–1,+1, is probably, in part, indicative of investors’ reassessment of Rita’spotential damage. The share prices simply bounced back as damage pro-jections became smaller.

A Closer Look at the CARs

Table 4 reports the estimation results of equations 3a and 3b.7 Theseestimates provide additional evidence indicating that investors recognizedthe importance of an insurer’s degree of exposure to hurricanes Katrinaand Rita. For Katrina the mere existence of exposure (the EXP variable) issignificant at the 1% level, whereas for Rita it is significant at the 10% level.The reaction based on the insurer’s degree of exposure (the DEXP variable)is significant at the 1% level for both hurricanes. The negative DEXPcoefficient for Katrina (–.213) reveals that as the degree of insurance expo-sure increased, the CAR–1,+1 decreased, i.e., became more negative. Thepositive DEXP coefficient (.175) for Rita indicates that investors reactedpositively to the weakening and track change of Rita just prior to landfall.

The SIZE variable in equations 3a and 3b is insignificant for bothKatrina and Rita. The estimated coefficients on the MB variable are positiveand significant for both Katrina and Rita, indicating that investors had astronger response to firms that had high market-to-book ratios.

Robustness Checks

We perform a number of exercises to examine the robustness of ourresults. We conclude that our main results are robust to reasonable varia-tions in the methodology used to estimate the CARs. These results aresummarized as follows.

CARs weighted by degree of exposure: Table 3 reports CAR estimates forthree different event windows. The results for the exposure-weightedCARs are very similar to the results for the equally-weighted CARs. Theevent-window CARs are significantly negative for the exposed firms dur-ing Katrina and significantly positive during Rita.

7Due to limited availability of market-to-book ratio and market capitalization data, only 47companies are used in this analysis. The 47 companies are noted in Table 1 by asterisks.


Table 2. Reaction of Stock Prices (%) of Exposed and Unexposed P&L Insurance Companies to Hurricanes Katrina and Rita

Katrina Rita

Date (Event date)

Exposed firms

Unexposed firms

Date (Event date)

Exposed firms

Unexposed firms

9/19/05(–5)

–0.66**(–2.08)

–0.54(–1.24)

8/23/05(–4)

0.17(0.85)

0.47(1.03)

9/20(–4)

0.45*(2.03)

–0.01(–0.04)

8/24(–3)

–0.37**(–2.51)

0.04(0.18)

9/21(–3)

–1.75***(–4.51)

–0.72(–1.30)

8/25(–2)

0.20(0.72)

0.41(1.64)

9/22(–2)

–0.53*(–1.88)

–0.98***(–3.23)

8/26(–1)

–0.18(–0.86)

–0.28(–1.43)

9/23(–1)

2.00***(4.84)

0.88*(1.80)

8/29(0)

–0.79**(–2.49)

–0.46**(–2.28)

9/26(0)

0.52(1.57)

0.61**(2.48)

8/30(+1)

–0.76***(–3.14)

–0.51**(–2.60)

9/27(+1)

0.21(0.53)

–0.30(–0.98)

8/31(+2)

–0.59*(–1.99)

–0.06(–0.18)

9/28(+2)

–0.05(–0.18)

–0.06(–0.17)

9/1(+3)

0.04(0.08)

0.09(0.29)

9/29(+3)

0.85***(4.02)

0.85***(3.26)

9/2(+4)

–0.38**(–2.33)

0.15(0.47)

9/30(+4)

–0.46(–1.68)

–0.21(–0.50)

9/6(+5)

0.18(0.53)

–0.04(–0.15)

10/3(+5)

–0.91(–1.53)

–0.30(–0.85)

9/7(+6)

–0.21(–0.75)

–0.63(–1.63)

10/4(+6)

–0.19(–0.59)

0.57(1.41)

9/8(+7)

–0.56**(–2.10)

–0.33(–1.01)

10/5(+7)

–0.41*(–1.83)

–0.22(–0.80)

9/9(+8)

0.18(0.43)

0.42(1.57)

10/6(+8)

–0.36(–1.10)

0.37(1.21)

9/12(+9)

0.58(1.33)

0.23(0.88)

10/7(+9)

–0.24(–0.87)

0.28(1.04)

9/13(+10)

0.65**(2.73)

0.38(1.34)

10/10(+10)

–0.41(–0.89)

0.28(1.28)

Note: ***Significant at 1% (two-tailed). **Significant at 5% (two-tailed). *Significant at 10%(two-tailed). Abnormal returns are estimated from a market model regression (equations(1) and (2)) using daily return data for each stock, estimated over 34 months during the“quiet period” of January 2002 through July 2004 (excluding September and October of2003). Hurricane Isabel (a Category 5 hurricane) hit the coast of North Carolina on Septem-ber 18, 2003. Thus we exclude the data for September and October 2003 from our marketmodel parameter estimation period. t-ratios are reported in parentheses.


Using different estimation periods: We estimate the market model param-eters (equation 1) over three different estimation periods. The CARsreported in tables 2–4 are based on parameters that are estimated usingdaily return data over “quiet period” #1, which is January 2002–July 2004(September and October 2003 data are excluded because of hurricaneIsabel). As a robustness check we also estimate the market model param-eters over two additional “quiet periods.” “Quiet period” #2 includes allof “quiet period” #1 plus the eight months from November 1, 2004 through

Table 3. Cumulative Abnormal Returns of Exposed and Unexposed P&L Insurance Companies to Hurricanes Katrina and Rita

Equal-weighted event window CARs

Katrina Rita

Exposedfirms

Unexposedfirms

Exposedfirms

Unexposedfirms

CAR–1,0

(Z stat)–0.97**(–2.31)

–0.75(–1.60)

2.52***(6.59)

1.49***(3.18)

CAR0,1

(Z stat)–1.55***(–4.08)

–0.98**(–2.35)

0.73*(1.71)

0.31(0.23)

CAR–1,1

(Z stat)–1.73***(–3.53)

–1.26**(–2.26)

2.73***(5.83)

1.19*(1.79)

Event window CARs weighted by degree of exposure

Katrina Rita

Exposedfirms

Unexposedfirms

Exposedfirms

Unexposedfirms

CAR–1,0

(Z stat)–1.24***(–3.09)

na 2.64***(6.55)

na

CAR0,1

(Z stat)–1.77***(–4.65)

na 1.41***(3.05)

na

CAR–1,1

(Z stat)–2.15***(–4.42)

na 3.38***(6.76)

na

Note: ***Significant at 1% (two-tailed). **Significant at 5% (two-tailed). *Significant at10% (two-tailed).CARj(t1,t2) is the cumulative abnormal return on firm j’s stock over the event window(t1,t2). Day 0 is the date of landfall or the first trading day after landfall for each hurricane.The Z statistics (unit normal) for the CARs are computed using the methodologyoutlined in Liu, Smith, and Syed (1992). na = not applicable.


June 30, 2005. Return data for August–October 2004 are excluded from“quiet period” #2 because several major Atlantic hurricanes (Charley,Frances, Ivan, and Jeanne) made landfall during that time. “Quietperiod”#3 includes the 250 trading days before hurricane Katrina (April2004–June 2005). We exclude data for August, September, and October 2004(the 2004 hurricane season) and July 2005 from “quiet period” #3. July 2005is excluded because hurricane Dennis (a Category 4 hurricane) hit the coastof Florida in July 2005.

We find that our results are robust to using different estimation peri-ods. We report the results for “quiet period” #3 in panel A of Table A of theappendix. As can be seen, the results are very similar to those reported inTable 3 for “quiet period” #1.

Table 4. Reaction of Stock Prices of Property and Casualty Insurance Companies to Hurricanes Katrina and Rita Based on Exposure

Reaction based on exposure (EXP)

Katrina –0.028***(–3.66)

–0.009***(–2.56)

–0.001(–0.60)

0.016***(4.98)

Rita 0.015(1.10)

0.012*(1.92)

–0.002(–1.19)

0.011*(1.93)

Reaction based on degree of exposure (DEXP)

Katrina –0.025***(–3.29)

–0.213***(–3.40)

–0.001(–1.39)

0.017***(5.15)

Rita 0.018(1.37)

0.175***(3.21)

–.002(–1.36)

0.009(1.60)

***Significant at 1% (two-tailed). **Significant at 5% (two-tailed). *Significant at 10%(two-tailed).These regression results are based on equations (3a) and (3b), where the dependentvariable is CAR for days (–1, 0, +1); the independent variable EXPj, is a dummy variablethat takes on a value of 1 if firm j has direct premiums written in the affected states, and0 otherwise; DEXPj is the ratio of the dollar amount of direct premiums written in theaffected states and the total direct premiums written for firm j; SIZEj is the logarithm ofmarket capitalization of firm j in June 2005 and MBj is the market-to-book ratio of firmj in June 2005. t-ratios are reported in parentheses. Due to limited availability of market-to-book ratio and market capitalization data, only 47 companies are used in this analysis.The 47 companies are noted in Table 1 by asterisks.

λ̂j η̂j γ̂j δ̂j

λ̂j η̂j γ̂j δ̂j


Using a different estimation model: We re-estimate the CARs using thepopular four-factor Carhart model (1997). The Carhart model adds amomentum factor to the three well-known Fama and French factors (1995).Thus the four factors are SMB (small minus big), HML (high minus low),the excess market return (Rm – Rf), and MOM (momentum factor).8

The CARs from the Carhart model (1997) are reported in panel B ofTable A. Again, the results are qualitatively similar to the results reportedin Table 3. All the conclusions remain the same whether we use the single-factor market model or the four-factor Carhart model (1997).

Size-weighted CARs: We compute the CARs by weighting the abnormalreturns by the relative market value of each firm in the sample. The resultsare in panel C in Table A. A comparison with the results in Table 3 showsthat the size-weighted CARs are generally smaller than the equally-weighted CARs. However, as in Table 3, the CARs are significantly negativefor Katrina and significantly positive for Rita. Thus the results hold bothfor the big firms and the small firms in the sample.

SUMMARY

The effects of natural disasters on insurer share prices reflect the twocounterbalancing forces of the damage and revenue hypotheses. Thedamage hypothesis is tied to uncertainty whether an insurer’s loss reservesare large enough to absorb loss claims, while the revenue hypothesis is tiedto a post-disaster potential increase in demand for insurance and/orincrease in insurance rates. The event study presented here reveals areasonable and consistent connection between events, investor uncertainty,and share prices. The results of this study add to the accumulating empir-ical evidence indicating efficient adjustment of stock prices to the arrivalof new information.

The historic and catastrophic damage inflicted by hurricane Katrinaresulted in negative returns on share prices for the property-liability insur-ance industry. These negative returns grew worse in the days following thehurricane as the information coming out of New Orleans grew worse. Theseone-sided, negative results are consistent with the damage hypothesis, indi-cating investor uncertainty regarding the ability of insurers’ loss reserves toabsorb the damage claims from Katrina. The timing of the negative abnor-mal returns coincides closely with press releases announcing higherdamage estimates, thereby giving support to notion of efficient markets.

8The data for the four factors are available from Professor Kenneth French’s data library atmba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.


The abnormal returns found in our evaluation of hurricane Rita alsoshow the market adjusting quickly to press releases covering the hurricane.The positive cumulative return surrounding landfall is consistent with theweakening of hurricane Rita, the actual landfall shifting away from apopulated area, and investors recovering from the negative price reactionprior to landfall. Just prior to landfall, Rita appeared to be headed for theGalveston and Houston area. Given the fact that Katrina blew into NewOrleans only three weeks earlier, a negative response to the possibility of ahurricane hitting a populated and developed area, on days –3 and –2, iscertainly reasonable. While the oscillating behavior of the results fromhurricane Rita does not provide clear support for either the damage hypoth-esis or the revenue hypothesis, these results do show efficient marketreactions to the changing features of the hurricane’s intensity and track.

When the cumulative abnormal return results are evaluated as afunction of the degree of insured exposure, the results are significant,indicating that market investors do make a distinction between a low levelof exposure and a larger degree of exposure. We do find some impact onthe share prices of unexposed firms, indicating some contagion effects.

REFERENCES

Aiuppa, TA, RJ Carney, and TM Krueger (1993) An Examination of Insurance StockPrices Following the 1989 Loma Prieta Earthquake, Journal of Insurance Issues andPractice 16: 1–14.

Aiuppa, TA and TM Krueger (1995) Insurance Stock Prices Following the 1994 LosAngeles Earthquake, Journal of Insurance Issues 18(1): 23–36.

Angbazo, LA and R Narayanan (1996) Catastrophic Shocks in the Property-Liabil-ity Insurance Industry: Evidence on Regulatory and Contagion Effects, Journalof Risk and Insurance 63 (4): 619–637.

Born, P and WK Viscusi (2006) The Catastrophic Effects of Natural Disasters onInsurance Markets, Journal of Risk Uncertainty 33: 55–72.

Cagle, JAB (1996) Natural Disasters, Insurer Stock Prices and Market Discrimina-tion: The Case of Hurricane Hugo, Journal of Insurance Issues 19 (1): 53–68.

Carhart, MM (1997) On Persistence in Mutual Fund Performance, Journal of Finance52: 57–82.

Cummins, JD and CM Lewis (2003) Catastrophic Events, Parameter Uncertaintyand the Breakdown of Implicit Long-Term Contracting: The Case of TerrorismInsurance, Journal of Risk and Uncertainty 26 (2): 153–178.

Ewing, BT, SE Hein, and JB Kruse (2006) Insurer Stock Price Responses to HurricaneFloyd: An Event Study Analysis using Storm Characteristics, American Meteoro-logical Society 21: 395–407.

Fama, EF and KR French (1992) The Cross-Section of Expected Stock Returns,Journal of Finance 47: 427–465.


Fama, EF and KR French (1995) Size and Book-to-Market Factors in Earnings andReturns, Journal of Finance 50: 131–155.

Fama, EF and KR French (1996) The CAPM Is Wanted, Dead or Alive, Journal ofFinance 51: 1947–1958.

King, OR (2008) Hurricane Katrina: Insurance Losses and National Capacities forFinancing Disaster Risks, CRS Report for Congress, Order Code RL33086.

Knabb, RD, JR Rhome, and DP Brown (2005) Tropical Cyclone Report: HurricaneKatrina 23–30 August 2005, National Hurricane Center.

Knabb, RD, DP Brown, and JR Rhome (2006) Tropical Cyclone Report: HurricaneRita 18–26 September 2005, National Hurricane Center.

Kunreuther, H, R Ginsberg, L Miller, P Sagi, P Slovic, B Borkan, and N Katz (1977)Limited Knowledge and Insurance Protection Implications for Natural HazardPolicy, Pre-Publication Manuscript, NSA-RANN #ATA73-03064-A03, P.5–20.

Lai, G, M McNamara, and H Oppenheimer (2002) A Further Analysis of LargeLosses’ Impact Upon Firm Value, Journal of Insurance Issues 25 (1): 63–84.

Lamb, R (1995) An Exposure-Based Analysis of Property-Liability Insurer StockValues around Hurricane Andrew, Journal of Risk and Insurance 62: 111–123.

Lamb, R (1998) An Examination of Market Efficiency around Hurricanes, FinancialReview 33: 163–172.

Lamb, RP and WF Kennedy (1997) Insurer Stock Prices and Market EfficiencyAround the Los Angeles Earthquake, Journal of Insurance Issues 20 (1): 10–24.

Liu, P, SD Smith, and AA Syed (1992) The Impact of the Insider Trading Scandalon the Information Content of the Wall Street Journal’s ‘Heard-on-the-Street’Column, Journal of Financial Research 15: 181–188.

Mayers, D and CW Smith (1982) On the Corporate Demand for Insurance, Journalof Business 55:281–296.

Risk Management Solutions (2005a) “RMS Announces Preliminary Insured LossEstimate of $10–$25 Billion for Hurricane Katrina,” www.rms.com/newspress/pr_082905_huk_2ndlandfall.asp, (August 29 press release).

Risk Management Solutions (2005b) “RMS Increases Insured Loss Estimate forHurricane Katrina to $20–$35 Billion,” www.rms.com/newspress/pr_090205_hukatrina_insured.asp (September 2 press release).

Risk Management Solutions (2005c) “Great New Orleans Flood to ContributeAdditional $15–$25 Billion in Private Sector Insured Losses for HurricaneKatrina, Bringing Estimated Insured Losses to $40–$60 Billion,” www.rms.com/newspress/pr_090205_hukatrina_insured_update.asp (September 9 pressrelease).

Shelor, RM, DC Anderson, and ML Cross (1990) The Impact of the CaliforniaEarthquake on Real Estate Firms’ Stock Value, Journal of Real Estate Research 5:335–340.

Yamori, N and T Kobayashi (2002) Do Japanese Insurers Benefit from a Cata-strophic Event? Market Reactions to the 1995 Hanshin-Awaji Earthquake, Jour-nal of the Japanese and International Economies 16: 92–108.

Yanase, N and Y Yasuda (2010) The Impact of the September 11 Terrorist Attack onthe Global Insurance Markets: Evidence from the Japanese Property-CasualtyInsurance Industry, Journal of Insurance Issues 33 (1): 85–107.


APPENDIX A

Note: CARj(t1,t2) is the cumulative abnormal return on firm j’s stock over the eventwindow (t1,t2). Day 0 is the date of landfall for each hurricane. The CARs (Table continues)

Table A. Cumulative Abnormal Returns (%) of Exposed and Unexposed P&L Insurance Companies to Hurricanes Katrina and Rita

Panel A: Equal-Weighted Market-Model Abnormal Returns

Katrina Rita

Exposedfirms

Unexposedfirms

Exposedfirms

Unexposedfirms

CAR–1,0

(Z stat)–0.97***(–2.92)

–1.02***(–2.63)

2.26***(6.89)

1.07***(3.97)

CAR0,1

(Z stat)–1.61***(–4.59)

–1.28***(–3.59)

0.55(1.62)

0.09(0.35)

CAR–1,1

(Z stat)–1.66***(–3.92)

–1.55***(–3.29)

2.54***(6.38)

0.70**(2.24)

Panel B: Equal-Weighted Abnormal Returns from the Four-FactorCarhart model

Katrina Rita

Exposedfirms

Unexposedfirms

Exposedfirms

Unexposedfirms

CAR–1,0

(Z stat)–0.79*(–1.92)

–0.42(–0.99)

2.37***(6.32)

1.12***(2.66)

CAR0,1

(Z stat)–1.66***(–4.47)

–1.18***(–2.63)

0.64(1.43)

0.12(0.05)

CAR–1,1

(Z stat)–1.71***(–3.59)

–1.11**(–2.46)

2.60***(5.61)

0.87(1.41)

Panel C: Size-Weighted Market-Model Abnormal Returns

Katrina Rita

Exposedfirms

Unexposedfirms

Exposedfirms

Unexposedfirms

CAR-1,0

(Z stat)–0.65*(–1.88)

–0.46(–0.86)

1.87***(5.34)

1.76***(3.44)

CAR0,1

(Z stat)–0.87**(–2.49)

–1.22**(–2.17)

0.23(0.55)

0.77(1.20)

CAR-1,1

(Z stat)–1.01**(–2.38)

–1.01(–1.47)

2.05***(4.79)

1.90***(2.86)


reported in Panel A are estimated from market model regressions using daily return datafor each stock, estimated over 250 trading days before hurricane Katrina (during the“quiet period” of April 2004 through June 2005, excluding August, September, andOctober of 2004—i.e., the 2004 hurricane season). Hurricane Dennis made landfall in July2005. Thus we also exclude data for July 2005 from the estimation period. CARs reportedin Panel B are estimated from the four-factor Carhart model (1997 ) using daily returndata for each stock, estimated during the “quiet period” of January 2002 through July2004 (excluding September and October of 2003). Hurricane Isabel (a Category 5 hurri-cane) hit the coast of North Carolina on September 18, 2003. Thus we exclude the data forSeptember and October 2003 from our model estimation period. The CARs reported inPanel C are estimated from market model regressions using daily stock return data overthe “quiet period” of January 2002 through July 2004 (same estimation period as in PanelB). The Z statistics (unit normal) for the CARs are computed using the methodologyoutlined in Liu, Smith, and Syed (1992).***Significant at 1% (two-tailed). **Significant at 5% (two-tailed). *Significant at 10% (two-tailed).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

An Examination of Share Price Behavior Surrounding the 2005

Documents

Transcript of An Examination of Share Price Behavior Surrounding the 2005