Oil Prices and the Stock Market - Semantic Scholar · Oil Prices and the Stock Market Robert C....

Oil Prices and the Stock Market∗

Robert C. Ready†

First Version: September 1, 2012

This Draft: December 13, 2013

Abstract

This paper develops a novel method for classifying oil price changes as supply or de-

mand driven and documents several new facts about the relation between oil prices and

stock returns. Demand shocks are strongly positively correlated with market returns,

while supply shocks have a strong negative correlation. The negative effects of supply

shocks are concentrated in firms which produce consumer goods, and are also strongest

for oil importing countries. Demand shocks are identified as returns to an index of oil

producing firms which are orthogonal to unexpected changes in the VIX index. Supply

shocks are oil price changes which are orthogonal to demand shocks and changes in the

VIX. Theoretical and empirical evidence are presented in support of this strategy.

Keywords: oil prices, stock markets, supply and demand

JEL codes: G01, Q04, E02

∗I would like to acknowledge the helpful comments of Nikolai Roussanov, Ron Kaniel, Mark Ready, BobJarrow, and seminar participants at Cornell University, The University of Pennsylvania, and the EuropeanFinancial Association. All errors are my own.†The Simon School of Business - University of Rochester

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1 Introduction

Over the past three decades, oil prices have received substantial attention as an economic

indicator both from academics and the popular press.1 An extensive macroeconomic liter-

ature suggests a strong link between oil prices and economic output. Hamilton [2003] and

others find a strong negative relation between increases in oil prices and future GDP growth,

and Hamilton [2011] points out that 10 of the 11 postwar economic downturns have been

immediately preceded by a significant rise in oil prices.

Given the apparent importance of oil prices, it is natural to examine the relations between

oil prices and other traded assets, such as equities, to help better understand the link between

oil prices and the economy. However in doing this a puzzling fact emerges; oil price changes

and stock market returns seem to be unrelated! Indeed, from 1986 to 2012, a simple regression

of monthly aggregate U.S. stock returns on contemporaneous changes in oil prices suggests

essentially zero relation between the two variables. More simply put: Where is the Oil Price

Beta?2

This finding is particularly surprising when considered in the context of recent macrofi-

nance models featuring long-run risks, following the work of Bansal and Yaron [2004]. These

models have been successful in explaining empirical asset pricing facts using the premise that

expected future changes in personal consumption impact the utility of the consumer today,

and hence have a large contemporaneous effect on the aggregate wealth portfolio and stock

prices. The fact that oil is a strong empirical predictor of macroeconomic growth, including

growth in personal consumption, while having little relation with aggregate stock returns, is

potentially at odds with this intuition.

This paper attempts to address this puzzle by introducing a novel method of classifying

changes in oil prices as demand driven (demand shocks) or supply driven (supply shocks).

The simple intuition behind the identification strategy is that oil producing firms are likely

1As an illustration, a search for occurrences of the term ”oil price” in the Wall Street Journal from 1986to 2012 yields 10,821 articles; more than one article per day.

2This disconnect between the observed importance of oil prices for the macroeconomy and the lack ofstock market reaction to changes in oil prices has received little attention in the academic literature. Therelation between oil prices and stock markets is mostly studied in the context of Vector Autoregressions(VAR). There is some evidence for relations at various leads and lags, but uniformly weak results in terms ofcontemporaneous correlations. See for instance; Jones and Kaul [1996], Kilian and Park [2009], Chen, Roll,and Ross [1986], Huang, Masulis, and Stoll [1996] and Sadorsky [1999].

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to benefit from increases in oil demand, but may have a natural hedge against shocks to oil

supply. If oil becomes more difficult to produce, producers will sell less, but at higher prices.

If this is the case, oil producer returns can be used as a control to separate shocks to demand

and supply.

Following this intuition, demand shocks are defined as the portion contemporaneous re-

turns of an index of oil producing firms which is orthogonal to unexpected changes in the

VIX index, which are included to control for aggregate changes in discount rates.3 Supply

shocks are constructed as the portion of contemporaneous oil price changes which is orthog-

onal to demand shocks as well as to innovations in the VIX.4 By construction, innovations

to the VIX (henceforth risk shocks), demand shocks, and supply shocks, are orthogonal and

account for all of the variation in oil prices. Since the VIX has very low correlation with oil

prices, nearly all of the variance is captured by the demand and supply shocks, with supply

shocks accounting for 78.5% of the total variation and demand shocks another 20%.

When the supply and demand shocks are examined separately, it becomes clear that the

apparent lack of relation between oil prices and stock returns is an artifact of the conflicting

effects of the two types of shocks. Instead of no relation, both supply and demand shocks

are strongly correlated with aggregate stock returns over the sample period. Changes in

oil prices classified as supply shocks have a strong statistically significant negative relation

with stock returns, with oil supply shocks explaining roughly 4% of the monthly variation

in aggregate U.S. stock market returns over the 1986 - 2011 sample period. The presence

of large outliers of returns during the financial crisis increases this to roughly 9% when the

sample is restricted to the period prior to June 2008, with nearly all of the effect coming

from 1985 - 1992 and 2003 - 2008, two periods of high uncertainty in oil markets. Similar

results hold for international stock returns, with the impact of supply shocks being strongest

for oil importing countries.

In contrast, increases in prices classified as oil demand shocks have a strong positive

relation with stock returns, and explain roughly 10% of monthly U.S. stock market variation

over the time period. Demand shocks are significantly positively correlated with both U.S.

3Unexpected innovations are the residual from an ARMA(1,1) regression.4Returns to a position in short-term oil futures are used to focus on the unanticipated change in the oil

price.

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and world returns in all subperiods, with the exception of a weak correlation with U.S.

returns during the 2003-2008 period. However, during this period the demand shocks are

still strongly positively correlated with world returns, suggesting world wide growth as the

source of increases in demand over this period.

In addition to providing evidence for the importance of oil in explaining aggregate stock

returns, construction of these shocks also allows for an investigation of how oil shocks affect

different types of firms. Regressions of industry portfolios find that all industries, with the

exception of gold and coal producers, have significantly negative relations with oil supply

shocks. However, it is not the industries with the highest oil use that are the most negatively

affected by oil supply shocks. While some high oil use industries such as Airlines are near

the top of the list, in general producers of consumer goods and services (ie. Apparel and

Retail) tend to have greater exposures than high oil use manufacturing firms. This result

suggests that the main effect of an oil shock may be a reduction in consumer spending,

which in turn impacts firms that produce consumer goods, consistent with the hypothesis

of Hamilton [2003] that oil shocks act through a reduction in consumer demand. The least

impacted firms appear to be high tech and telecom firms, which have low oil use and may

be less directly dependent on consumer spending. All industries have positive betas on oil

demand shocks, with high oil use industries having the strongest loadings. High oil use firms

tend to be manufacturing firms, suggesting that increases in manufacturing activity may be

the primary driver of oil demand shocks.

The interpretations here rely crucially on the validity of the identification strategy. To

understand the logic, it is important to understand the potential reason for the lack of

correlation between oil prices and stock prices. If an exogenous increase in oil prices is,

ceterus paribus, bad news for stock prices, what possible cause is there for the negligible

relation between the two variables? One potential explanation is an omitted variable, with

an obvious candidate being shocks to aggregate oil demand, which would intuitively be

associated with rising oil prices and positive equity returns.5 In order to account for this

5This logic has been emphasized by several authors, most notably Kilian [2009]. A different logic, suggestedin recent work by Casassus and Higuera [2013], builds on the fact (se Driesprong, Jacobsen, and Maat [2008]and Casassus and Higuera [2012]) that high oil prices predict low future aggregate market returns. If high oilprices mean low future cash flows but also lower expected future returns, the two effects can also counteracteach other when examining stock market returns. It is shown here that the identified supply and demand

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and sign the causal relation between oil prices and stock returns, one might potentially find

instruments for oil price changes that are exogenous with respect to the rest of the economy,

such as a time series of events affecting oil production.6 However these events tend to be rare

so this technique is less suited to answering the question of how much the variance in stock

prices is driven by oil supply shocks.

Another potential issue with directly classifying shocks lies in the definition of a supply

shock. Although oil supply shocks are typically thought of as discrete events, such as a war

or hurricane which disrupts oil production, there is the possibility for more subtle effects.

For instance, how would one classify a month where oil prices rise slowly day by day, as the

amount of oil produced fails to meet expectations? The resulting change in prices is as much

a supply shock as a one time major disruption in production from a natural or political event,

but is much more difficult to identify from news reports.

Perhaps the most direct technique to account for this problem is to examine data on

oil production. Kilian [2009] attempts to disentangle demand and supply shocks using a

structural vector autoregression (SVAR) with data on oil production and shipping activity

as proxies for supply and demand, and Kilian and Park [2009] extend this methodology

to examining different shocks’ impact on the U.S. stock market. However, they find very

little contemporaneous explanatory power (less than 2% combined), mostly concentrated in

changes in oil prices related to neither supply nor aggregate demand.

One weakness of this framework is that the data included in the SVAR needs to corre-

late with contemporaneous or future changes in oil prices to effectively identify shocks. For

instance, the identified supply and demand shocks in Kilian [2009] explain only 2% of the

contemporaneous variation in oil prices from 1987 to 2011, and less than 1% one percent when

the sample is truncated in 2008 to remove effects of the financial crisis. Of the remaining

variation in oil price changes, 30% is classified as predictable by the VAR, suggesting overfit-

ting, and the remaining 68% is classified as ”precautionary demand shocks”.7 Unfortunately,

there is no way to ascertain if these changes in precautionary demand are driven by concerns

shocks both predict negative future market returns, suggesting that their difference in correlations withcontemporaneous market returns is not driven by discount rate effects.

6This strategy is pursued by several authors, including Hamilton [1983], Hamilton [2003], Kilian [2008],and Cavallo and Wu [2006].

7See Web Appendix section ??.

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over supply or expectations of changes in demand. For example, an increase in oil prices

driven by concerns over a supply constraint which never materializes will not be captured

in the VAR, since production will not respond, and will thus be classified as precautionary

demand. In contrast, an increase in prices due to expected increases in demand receive the

same classification, and presumably the two events will have markedly different effects on

aggregate returns. An identification technique, such as the one presented here, which relies

upon prices of traded assets can make use of the forward looking nature of prices to avoid

these issues.

To provide motivation for the identification scheme used here, a theoretical model of a

competitive commodity producing sector is introduced, similar to the exhaustible resource

models of Carlson, Khokher, and Titman [2007] and Casassus, Collin-Dufresne, and Rout-

ledge [2005]. The model provides evidence that certain characteristics of commodity pro-

duction, namely the depletable nature commodity resources, the highly inelastic demand,

and the significant difficulties in developing new reserves, give oil producers a natural hedge

against shocks to the aggregate productivity of the sector. This in turn will yield producer

stock returns that are unresponsive to changes in the productivity of the sector. This lack of

response to these supply shocks makes producer returns an effective control for identification.

The supply and demand shocks in the model are essentially cash flow shocks, in that they

effect the earnings of oil producers. In order to examine discount rate effects, the model also

includes exogenous changes in the price of risk associated with demand shocks, and shows

that these changes impact producer stock returns without impacting oil prices. Since these

shocks are likely to have an effect on aggregate stock prices, neglecting to control for them

can lead to a negative correlation of identified supply shocks with the market index even

in the absence of any relation between oil prices with aggregate returns. This motivates

the inclusion of innovations to the VIX index as a control in the empirical identification

technique, since these innovations have a strong negative correlation with both the aggregate

market and oil producer returns.8

The model shows that the identification technique is plausible, empirical evidence is also

8An extensive recent literature relates volatility and the variance risk premium embedded in the VIX todiscount rates and stock market returns. See ?, ?, Drechsler and Yaron [2011], and Bollerslev, Tauchen, andZhou [2009] as examples.

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provided to show that the constructed variables are effective proxies for supply and demand

shocks, and that the impacts on the aggregate market return are indeed driven by shocks to

oil prices. One test of this is to look at the comovement of the volatilities of the variables

used in the identification. A well known fact about oil prices is that their volatility is highly

correlated with the volatility of the stock market.9 This result is difficult to explain if shocks

to oil prices have no impact on aggregate stock returns. However, if the stock market’s

response to supply shocks is offset by its response to demand shocks, an increase in the

volatility of either shock will generate an increase in stock market volatility. It is also shown

here that the volatilities of oil producer stock returns appear to have little relation with oil

price volatilities. The fact that increases in the volatility of oil prices are associated with

rises in aggregate market volatility while having no effect on oil producer return volatility

suggests that oil producer returns are uncorrelated with an important component of oil price

changes, precisely consistent with the story here.

As a further test, the same exercise is also conducted with copper and aluminum, two

other commodities that are easily storable and are inputs to production, but whose prices

are presumably less likely to have a major impact on aggregate output. If the channel

for the results is unrelated to oil price shocks, one would expect similar results for these

commodities. Instead, while the demand shocks for these two commodities are highly related

to the aggregate stock market, the identified supply shocks have a negligible relation to

aggregate stock returns.

Finally, regressions are used to directly test the impact of the identified shocks on variables

relating to macroeconomic output and the U.S. oil supply, and the results are consistent with

the supply shock interpretation. The identified supply shocks are negatively correlated with

current and future economic output and as well as current levels of oil consumption and

production. Conversely the identified demand shocks positively impact current and future

aggregate output, and to the extent they impact levels of oil consumption and production it

appears to be in the form of predicting a delayed increase in both variables, consistent with

the behavior in the model.10

9See Sadorsky [1999] and Malik and Ewing [2009].10Though the model does not explicitly include storage, the supply shocks also correlate strongly with

inventories, resulting in marked decreases in U.S. inventory levels, which is intuitively consistent with the

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The rest of the paper is organized as follows: Section 2 introduces a basic model of

competitive oil producers and discusses the shock identification strategy in the context of the

model. Section 3 empirically implements the identification strategy, presents the relations

between the shocks and the aggregate stock market, and presents empirical support for the

validity of the identification technique. Section 4 presents detailed results on the relations

between the two constructed shocks and domestic and international stock markets. Section

5 concludes.

2 A Simple Model of Oil Production

The basic model introduced here is a model of atomistic competitive firms which take the price

as given and choose both investment in oil reserves (with very high adjustment costs) and the

level of a flow input (with low adjustment costs). The model is very standard when compared

with previous models of commodity production, such as Kogan, Livdan, and Yaron [2009],

Casassus, Collin-Dufresne, and Routledge [2005], Carlson, Khokher, and Titman [2007], and

Ghoddusi [2010]. The benchmark model views oil as a depletable resource as in Carlson,

Khokher, and Titman [2007] and Ghoddusi [2010]. The first important extension here is the

inclusion of exogenous supply shocks, in addition to the standard demand shocks, so that the

relative impacts on prices and producer stock returns can be examined. The supply shocks

are modeled as increases to the flow of oil produced by a given level of flow input and oil

wells. These shocks both increase current production as well as the speed with which oil

wells are depleted.

Given the highly inelastic demand for oil (Cooper [2003] and Ready [2013]), the model

shows that the depletable nature of oil can create an industry whose returns are unresponsive

to changes the exogenous supply shocks. This unresponsiveness is the property that allows

for the identification of the different shocks using producer returns.

The model also deviates from existing work in allowing for exogenous shocks to the ex-

pected rate of return on oil producing firms, which in the model is generated by an exogenous

shock to the price of risk associated with aggregate demand shocks. These shocks impact oil

explanation.

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producer returns without affecting oil prices, and therefore need to be controlled for in order

to effectively identify demand and supply shocks.

The model does not explicitly describe a claim to an aggregate stock market. While

it would be simple to include a reduced-form specification of an aggregate market return

which would match the observed correlations between aggregate returns on the shocks to the

oil industry, this would not provide any additional insight. A more complete model would

specify final goods producers which would use oil as an input to produce goods for sale to

consumers, but since the main goal here is to provide support for the identification technique,

such a model is beyond the scope of the current project.

2.1 Firms

The model consists of a continuum of competitive firms with standard Cobb-Douglas pro-

duction technology.

(1) Ot = ZtFνt W

1−νt

Oil wells Wt, and a flow input Ft, are used to produce oil output Ot. The level of

productivity is also affected by an oil industry production shock, Zt.11

In the context of the model, Wt represents oil reserves in the ground. This is an important

distinction which is unique to a commodity producer, and helps to capture the storable nature

of commodities. The costs of increasing production in a given period are not only the direct

costs to the producer of a higher level of the input Ft, but also the reduction of oil reserves

available to produce in future periods.12 This cost is reflected in the evolution of oil reserves

(2) Wt+1 = Wt(1− δ)− d×Ot + It

11For simplicity, it is assumed that there are no firm specific shocks, as well as no entry and exit.12This feature is central in the exhaustible resource model of Carlson, Khokher, and Titman [2007], and

also present in the production model of Casassus, Collin-Dufresne, and Routledge [2005]

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The producer chooses investment in new reserves (It) , which are depleted both from

depreciation, δ, and from the production of oil multiplied by a depletion rate d. The case

where d = 1 will be referred to as the ”Benchmark Model”, and the case where d = 0 as the

standard ”Neoclassical Model”.

Given an oil price, Pt, firms sell their output earning a profit Πt

(3) Πt = PtOt − cFFt − It − Φ(Ft, Ft−1, It,Wt)

Here Φ is a function representing costs to adjusting both the level of the flow input and

the levels of investment in oil wells, and has a standard quadratic form

(4) Φ(Ft, Ft−1, It, Kt) =aF2

(Ft − Ft−1

Ft−1

)2

Ft−1 +aW2

(I

Wt

− I

W

)2

Wt

Where I and W are the deterministic steady-state values of investment and oil well

stock, and aF and aW govern the level of adjustment costs for the flow input and oil reserves

respectively. The producers take the price as given and solve the maximization problem

(5) maxFt,It

=∑∞

t=0MtΠt

Where Mt is the stochastic discount factor.

2.2 Consumers

Since the model does not include a separate competitive storage sector (see Routledge, Seppi,

and Spatt [2000] and Williams and Wright [1991]), consumers consume all of the oil output in

each period. Instead the storable nature of oil is captured in the producers’ choice between

producing now and saving reserves for future production, and captures some of the same

intuition as a separate storage technology.

Following Kogan, Livdan, and Yaron [2009], consumers of oil are represented by an inverse

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demand curve, so that spot prices Pt are given by

(6) Pt = At(Ot)− 1α

The price is dependent upon At, representing the aggregate level of oil demand in the

economy, Ot, the total production of oil, and α, the elasticity of demand.

2.3 Dynamics

From equation (6) it is clear that there are two possible channels in the model for generating

a change in the oil price. The first is a rise in the level of demand At, and the second is a

reduction in the level of supply Ot. Though producers of final goods and household consumers

are omitted for parsimony, simple intuition suggest that rises in the oil price from increases

in At reflect positive economic news, while rises in price from a reduction in Ot generated by

a decrease in productivity, Zt, would represent negative news for the aggregate stock market.

Both aggregate oil demand and oil productivity are stochastic and their logs (indicated

by lower case) evolve according to

at+1 = a0 + ρa(at − a0) + σaea,t+1(7)

zt+1 = z0 + ρz(zt − z0) + σzez,t+1(8)

Where ea,t+1 and ez,t+1 are independent normally distributed shocks with mean zero and

a variance of one. High realizations of either ea,t+1 or ez,t+1 correspond to ”good” times, and

therefore both command positive prices of risk. To capture this the stochastic discount factor

is given by

(9)Mt+1

Mt

= β exp(−λa,tea,t+1 − λzez,t+1)

Where λa is time varying and evolves according to

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(10) λa,t+1 = λa + ρλ(λa,t − λa) + σλeλ,t+1

2.4 Model Results

A competitive equilibrium is defined as a sequence of choices of It and Ft such that the

firms are maximizing firm value while taking Pt as given, and the market clearing condition

Pt = AtO− 1α

t is met.

The model is solved numerically, but the main intuition regarding the identification result

can be seen in the producers’ first order equation for the choice of the flow input, Ft

(11) cF = νOt

Ft(Pt − d× qt)

Where qt is the lagrangian multiplier associated with oil well accumulation, and represents

the marginal value of an extra oil well in time t+ 1.

In the standard neoclassical framework (d = 0), inelastic demand (α < 1), and high

adjustment costs on oil reserves Wt mean that a positive productivity shock is detrimental

to producers. The increase in output from higher well productivity Zt yields a decrease in

price and a drop in revenue. The competitive firms reduce the level of the adjustable input

in response, but the decrease in the input is not enough to counteract the fall in price and

therefore results in a reduction in profit.

In the Benchmark Model (d = 1), the competitive producers will take into account that

selling at the current low price is costly since prices are expected to increase in the future. This

effect is a standard feature of exhaustible resource models, dating back to Hotelling [1931].

Therefore the drop in price Pt will come along with a rise in the value of qt. Accordingly, when

faced with a positive productivity shock, the producers will respond with a more aggressive

decrease in the level of the adjustable input. The resulting additional decrease in output leads

to higher prices in equilibrium, and for reasonable parameters the fall in price will offset the

increase in productivity and oil productivity shocks will have little effect on producer stock

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returns.

2.5 Simulations

The model is solved for a benchmark case, with parameters given in Table 1, as well the

standard neoclassical case where there is no depletion of reserves from production (d = 0).

The important parameter for the identification technique is the elasticity of oil demand,

which is significantly less than one in the data. The calibration uses a value of α = 0.5,

consistent with estimates in the literature (See Kogan, Livdan, and Yaron [2009] and Ready

[2013]). The remaining parameters are calibrated to match volatilities and correlations of

prices and returns.

[Table 1 about here.]

To illustrate the mechanisms in the model, Figure figure 1 shows impulse response for the

two shocks in each of the two cases. The first column of plots represents the response of four

model variables to an oil demand shock. An increase in oil demand generates an increase in

oil prices, an increase in oil production, and increased use of the oil flow input by producers.

In both cases there is also a positive impact on the value oil producing firms and hence the

stock return.

[Figure 1 about here.]

The second column shows the outcome of a decrease in oil well productivity, Zt. Again,

the oil price rises along as production falls, however this time some of the fall in production is

offset by an increase in the flow input. This effect is much more pronounced in the benchmark

case, as producers compete to produce oil in the time of high prices, before reserves rise and

lower prices again. This increase in production is enough to prevent a rise in profitability or

value for producers, and hence there is no positive stock return.

Table ?? reports calibrated volatilities and the correlation of oil producers’ stock returns

and changes in oil prices for both the simulated model and the data. The table also reports

the correlations between the simulated price innovations and returns and the unobservable

supply and demand shocks. The oil producer stock returns are essentially perfectly correlated

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with demand shocks while being nearly uncorrelated with supply shocks, making them an

effective control for identifying the two unobserved sources of variation.


Though the model is meant to be stylized, it does provide some insight into how an

empirical identification technique should work. A positive supply shock should be reflected

in lower prices, and higher oil output, while a positive demand shock should be reflected in

higher prices, and potentially a rise in oil production, but not one as marked as the observed

decrease from the supply shock. The differential exposure of oil producer returns to the two

types of shocks also allows for identifying the source of changes in price.

3 Identification of Oil Shocks

3.1 Data and Shock Construction

The three variables necessary to construct the series of supply and demand shocks are an index

of oil producing firms, a measure of oil price changes, and a proxy for changes in expected

returns. To cover as much of the global oil industry as possible, the index used is the World

Integrated Oil and Gas Producer Index available from Datastream. This index covers the

large publicly traded oil producing firms (Exxon, Chevron, BP, PetroBrazil, PeMex, etc..),

but does not include nationalized oil producers such as Saudi Aramco. For changes in the

oil price, the one-month return on the nearest maturity NYMEX Crude - Light Sweet Oil

contract are used for the bulk of the analysis, in order to focus on unexpected changes to oil

prices. Aggregate U.S. stock market data is from the universe of CRSP stocks. 13

To proxy for changes int the discount rate, innovations to the VIX index are used. The

VIX is calculated from options data, and therefore provides a measure of the risk-neutral

expectation of volatility. Not only is the variable forward looking, an extensive literature

has shown that the changes in the variance risk premium captured in the VIX are both

strongly negatively correlated with stock returns and positively predict stock returns in the

13Appendix ?? discusses the data sources and explores the results using other sources of oil producerindices, oil prices, and risk levels, and the effects of controlling for aggregate levels of prices and exchangerates. There is no significant impact on the results.

14

time series, suggesting that it may be a reasonable proxy for changes in risk. The VIX is

published by the CBOE for going back to 1986, and this limits the data for the main portion

of the analysis.14 In order to isolate unexpected changes in the VIX, an ARMA(1,1) process

is estimated the residuals from this process are used as innovations. These innovations are

denoted ξV IX .


Panel A of Table 3 provides summary statistics of standard deviations and correlations

of the four variables for the sample period. This correlation matrix is at the heart of the

strategy pursued in this paper. The high correlation between oil producer returns and both

oil prices and aggregate market returns, along with a very low correlation between oil prices

and aggregate market returns, suggests that there may be some source of variation which

loads negatively on aggregate stock returns but positively on oil prices and is uncorrelated

with producers, perfectly consistent with the productivity (supply) shocks in the model.

Additionally changes in the VIX seem to have a strong impact on both aggregate and oil

producer stock returns, but a minimal impact on prices, suggesting that it is important to

control for these changes when exploring how changes in oil prices impact stock prices.

For the primary analysis the supply shocks st, demand shocks dt, and risk shocks vt are

orthogonal and defined using the the system

∆pt

RProdt

ξV IX,t

=

1 1 1

0 a22 a23

0 0 a33

st

dt

vt

Panel B of Table 3 shows the summary statistics for the constructed shocks. The an-

nualized volatility of changes in oil prices over the sample period is roughly 33.8% while

the annualized volatility of the supply and demand shocks is 30.5% and 15.6% respectively.

Equivalently, roughly 78% of the variance in oil prices is classified as supply shocks, 20% is

classified as demand shocks, while the shocks to the V IX explain on 1% of the total variance

14The CBOE changed methodology in construction of the VIX in 1990. The series here is constructedusing the new ”Model Free” method going back to 1990, and the older method prior to 1990.

15

in oil price shocks, though they are important for the identification since they are highly

correlated with producer returns.

3.2 Oil Shocks and Aggregate Stock Returns

Once the shocks are constructed, it is a simple matter to use them in a basic regression of

aggregate stock market returns.15 Table 4 reports regressions of the form

(12) RMktt = α + βddt + βsst + βV IXξV IX,t

as well as univariate regressions of the market return on each shock, for both the aggregate

CRSP stock index and a world wide stock index from Global Financial Data. On their own,

oil prices have little or no relation to aggregate market returns. However, when they are

decomposed into two series, which together explain nearly all of the variation in oil prices, oil

prices have a very clear and intuitive link to the stock market. High oil prices from supply

shocks are bad news for aggregate stock returns, and can explain 4% of the monthly variation

in the aggregate market return, and rises in prices from demand shocks are associated with

positive excess returns and can explain an additional 10% of the aggregate variance.


One potential issue with oil prices, as noted by Hamilton [2003], is that large movements

tend to be the drivers of statistical results. To address this Figure 2 shows scatter plots of

monthly returns against the realizations of the two shocks against U.S. stock market returns.

Although there are some outlying large supply shocks accompanied by negative returns (these

tend to be from the gulf war period), the most noticeable outliers are the highly negative

returns of the 2008 financial crisis, which go against the general pattern.16


15Since the risk shocks vt, are simply a scalar multiplied times ξV IX,t, ξV IX,t is used in the regression inorder to provide a more straightforward interpretation of the economic magnitude of the regression coeffi-cients.

16Table 8 reports results for the supply shock regressions restricting the sample to the period prior to thefinancial crisis. Doing so provides a marked increase in the R2 of the supply shocks, from 4% to 9%.

16

3.3 Evidence for Validity of Identification Technique

It is clear that decomposing changes in oil prices in the manner described in the previous

section leads to strong correlations with aggregate stock market returns. In order to defend

the proposed interpretation that this decomposition allows for identification of supply and

demand shocks, several methods are pursued. Section 3.3.1 examines the behavior of producer

returns and prices around a known supply shock, the first Gulf War, and finds them to be

consistent with the story. Section 3.3.2 looks at the relation between stock market volatility

and oil price volatility, and finds a strong positive correlation between oil price volatility

and aggregate stock market volatility, but a negligible relation between oil price volatility

and oil producer return volatility, suggesting that oil producer returns are unrelated to an

important component of oil prices. In Section 3.3.3, the same exercise is pursued with

two other commodities, aluminum and copper. There the identified supply shocks have

little relation with returns, providing evidence that the result is not an artifact of a purely

mechanical relation between producer returns and commodity prices. Finally, in Section

3.3.4 analysis is presented which shows that the two shocks relate to variables reflecting

macroeconomic output and the oil supply in ways which are consistent with the supply shock

interpretation.

3.3.1 Oil Producer Returns and Oil Prices

To defend the validity of this technique, as a first step it is instructive to study the behavior

of oil producer returns and oil prices around an observable shock to oil production. Figure 3

gives an example of this using the first gulf war.


In the first panel, the stock prices of several multinational oil producing corporations are

shown along with the spot price of oil during the first Gulf War. Companies active in the

Middle East, such as Ashland, Inc. and Occidental Petroleum, suffered drops in value due

to concerns about their ability to produce during the conflict. In contrast companies, such

as British Petroleum, which were not operating in the region, saw their valuations rise with

the higher price of crude.

17

The second panel shows the behavior of the Datastream World Integrated Oil and Gas

Producers index over this period, as well as the performance of the U.S. stock market. The

initial invasion of Kuwait saw a spike in oil prices, and a negative return for the aggregate

stock market, but very little response in the returns of oil producers.

Although some individual producers saw large changes in price, it appears that in aggre-

gate the lower total production and higher price offset, so that oil producers as an industry

enjoyed a natural hedge against the potential supply shock. At the same time, aggregate

stock values fell, suggesting a potential relation between changes in oil prices and stock

returns.

3.3.2 Stock Market and Oil Price Volatility

Another piece of evidence that supports the story put forth here is the relation between stock

market volatility and oil price volatility. If supply shocks and demand shocks have conflicting

effects on returns, yielding low correlation when considered together, there should still be a

strong positive correlation between the volatilities of oil prices and aggregate market returns.

If the volatility of either demand shocks or supply shocks increases, this will yield higher

volatility in both aggregate stock market returns and oil prices. This positive correlation

has been previously documented by Sadorsky [1999] and others, and is confirmed here. A

new fact, shown here, is that the relation between oil price volatility and oil producer return

volatility is negligible. This is potentially puzzling but easily explained in the context of

the identified shocks. If all of the time series variation in oil price volatility comes from the

supply shocks, then there will be no relation between oil producer stock return volatility and

oil price volatility, precisely consistent with the data.


Table Figure 5 the results from four pairs of regressions using monthly observations of

volatility for each of the three variables constructed using daily returns as in Schwert [1989].

The first two columns shows regressions of contemporaneous changes in market volatility

and oil producer return volatility on contemporaneous changes in oil price volatility over the

whole 1987 - 2012 sample period. One period lags of differences and levels are included to

18

control for predictable changes in volatilities. The second two columns repeat this for the

pre-crisis data sample. In both samples there is a strong contemporaneous relation between

spot price volatility and aggregate market volatility, but no relation between the volatility

of oil producer returns and oil prices. This is very strong evidence that a portion of oil price

changes has no effect on oil producer returns while having a significant effect on the aggregate

market. The last four columns repeat this in levels, and find qualitatively the same result.

The large increases in volatilities during the crisis create a mildly significant relation between

levels of oil price and oil producer returns volatilities, but this complete disappears once the

crisis period is dropped.

3.3.3 Copper and Aluminum Producers and Prices

The regression results in Table 4 provide evidence that there may be a strong relation between

oil supply shocks and aggregate stock returns. However one potential concern is that the

results may be driven by a shock common to aggregate stock returns and oil producer returns,

which has no effect on oil prices. However, if this is the case, it is reasonable to think that

such an effect would be generated regardless of the commodity being examined.

Table 6 shows summary data for variables and shocks constructed using the same method

as in Table 3, but substituting producer indexes and price changes for copper and aluminum.

These two commodities are chosen because they are storable inputs to production, and world

wide stock index data for the specific industry is available. The correlations and volatilities

are similar to those observed for oil prices and producer returns, except that the correlation

of commodity prices and aggregate market returns is more strongly positive for copper and

aluminum than for oil, potentially because supply shocks to these commodities have less of

an impact on the economy.


Tables 7 repeat the regressions of aggregate market returns on the constructed shocks

and the results suggest that this is the case. Second, loadings on the supply shocks are

not statistically significant, and the R2 increases are negligible. Since the primary difference

between oil and the two metal commodities is the unique status of oil as the most important

19

input commodity for economic output, this result suggests that the driving effect for the

patterns in the oil regressions are supply shocks and not some other omitted variable.17


3.3.4 Macroeconomic Variables and Oil Shocks

While the results from regressions of market returns is suggestive of the supply shock story

being proposed here, it is important that the identified shocks have an impact on real eco-

nomic variables, particularly those related to the oil market.

To quantify these impacts univariate vector autoregressions (VARs) are estimated for

macroeconomic variables, with the constructed supply and demand shocks entering as ex-

ogenous variables. Following Hamilton [2008], these VARs have the following form18

∆yt =∑N

n=1βyn∆yt−n +

∑N

n=0

(βsn∆st−n + βdn∆dt−n

)(13)

Where yt is the log of an economic variable of interest, and st and dt are the innovations

to the supply and demand indices as described in the previous section. These indices are

included contemporaneously as well as with N lags.

Since U.S. data for both aggregate output and oil use is readily available for the sample

at issue, I first focus on a set of five variables related to U.S. economic output and oil con-

sumption. The two variables designed to capture the level of aggregate economic activity

are real GDP and a Cobb-Douglas aggregate of durable and nondurable household consump-

tion (net of household gasoline consumption). As is shown in Ready [2013], when linking

economic variables to levels of the oil price, the closest relation comes from relative levels of

this measure of household aggregate consumption and household gasoline consumption. This

result motivates the choice of two oil consumption variables, the U.S. total oil consumption as

measured by U.S. Oil Production plus Imports minus Exports minus Changes in Inventories

17U.S. copper use is usually under 1% of GDP, and Aluminum under 0.5%, compared to oil use whichranges between 2 and 5% of GDP over the sample.

18Hamilton [2003] estimates a more general form allowing for nonlinearities in the relation, the focus hereis on the simpler linear relation, as there appears to be little evidence of any nonlinearity in the relationbetween oil prices and stock returns. See Figure 2.

20

all taken from the EIA, and U.S. household gasoline consumption. Though not explicitly

included in the model, inventories are also central to discussions in the of the oil price, so the

last variable is U.S. petroleum stocks (net of the Strategic Petroleum Reserve) provided by

the EIA. The last variable is the level of international oil production again from the EIA.19

All data is aggregated to the quarterly level, and regressions are done using 4 lags to be

consistent with the convention in the literature.

In addition, for this portion of the analysis, a longer sample is used to provide more

power with tests of quarterly data. The shocks are constructed in the same manner as the

benchmark analysis, but the log change of the WTI is used in place of futures returns, and

residuals from an ARMA(1,1) of aggregate U.S. stock market volatility are used in place of

ξV IX,t. The resulting supply shocks have a correlation of higher than 99% with the supply

shocks constructed using futures and the VIX. The demand shocks have a correlation of 93%

with the benchmark shocks.20

Figure 4 reports the impulse response functions of six variables to an oil supply shock and

Figure 5 reports the same for a demand shock. For an increase in oil prices from a supply

shock, there are significant decreases in all six variables, consistent with the hypothesis that

this shock measures a reduction in available oil, leading to lower oil consumption, which in

turn lowers aggregate economic activity. Conversely, for a demand shock, there are significant

positive increases in economic output and total consumption, and an increase in aggregate

economic oil use. There is no significant change in household oil consumption, and a delayed

increase in world oil production, consistent with the model. Also, supply shocks result in

a sharp decrease in inventories while demand shocks do not. The different responses of oil

production and consumption are notable, since both shocks create an increase in the oil price.



19International oil production is lagged one month as lagged production seems to correlate most stronglywith prices and U.S. quantities. This is possibly due to a delay in the availability of information. The U.S.data is released weekly in the ”Weekly Petroleum Status Report”, while international data is published witha 3-month lag in the ”Monthly Energy Report”.

20Recent work by Bekaert and Hoerova [2013] shows that the volatility portion of the VIX correlates moststrongly with macroeconomic aggregates, suggesting that this control is appropriate for this analysis. SeeWeb Appendix Section 1 for the relation of this construction method to stock returns.

21

4 The Impact of Oil Shocks on the Stock Market

The high frequency of the identified supply and demand shocks allow for analysis of the

relations between oil prices and the economy that are difficult or impossible using standard

VAR techniques. This section explores several of these. The first is to examine the time-

varying nature of the relation between oil supply shocks and the economy. The second is

to look at the relations between oil shocks and different industries’ stock return to better

understand how an oil shock effects the market cross-sectionally. The relation between oil

shocks and different countries stock returns are examined, and finds that countries which

import oil and have high ownership of passenger vehicles have the strongest negative relation

with supply shocks.

Additionally, the predictive relation between oil prices and stock market returns doc-

umented by Driesprong, Jacobsen, and Maat [2008] and Casassus and Higuera [2012] is

examined in the context of supply and demand shocks. Curiously, the predictive power does

not seem to be concentrated in one or the other, so unfortunately the results here do not

provide substantial insight into this phenomenon, except to provide evidence that it is the

level of oil prices rather than the source of their increase which is important for future stock

returns.

4.1 U.S. Stock Market and Oil Prices by Sub Period


Several recent papers have called attention to the changes in oil futures market behavior

over the period from 2003 to 2008. (see Ready [2013], Baker and Routledge [2011], Hamilton

and Wu [2012]) Though the period of available data being used here is short, the monthly

frequency allows for enough observations to examine the relation between stock prices and

the two oil shocks over smaller sub samples, so in order to see if the relation has changed over

the sample period the 1987 - 2012 sample is split into four subperiods. These samples are

illustrated in Figure 6. The four samples examined are: 1) 1986 to 1991, covering the oil glut

of the mid 1980s as well as the first gulf war, 2) 1992 to 2003, a period of low stable oil prices

which also saw the dot-com boom and bust in U.S. markets. 3) 2003 to mid 2008, which saw

22

huge rises in prices as well as worries about the ”financialization” of commodities, and 4)

2009 to 2011, the period post-crisis. Figure 6 also plots the yearly percentage of articles in

the New York Times containing the phrase ”Oil Prices”. As one might expect, the first and

third periods were times when oil received the most media coverage, and potentially would

be the periods where supply shocks would have the most importance. As the regressions in

Table 8 show, this is indeed the case, again providing support for the identification technique,

which assigns the highest importance to oil supply shocks during times when the media, and

potentially consumers, were most focused on the price of oil.



Figure 7 shows scatter plots for the different subperiods. The first subperiod shows a

strong relation between supply shocks and market returns, seemingly driven by large move-

ments in prices, consistent with the large movements around the gulf war and the oil glut

of the 1980s. In contrast, the strong relation during the oil price run-up of 2003 to 2008 is

driven by a more consistent relation among smaller monthly movements in price.

Another interesting period to note is the period from 2009 to 2012 post the crisis. Though

this period was accompanied by a precipitous drop in oil prices, they have since risen back up

to near pre-crisis levels. Along with this rise has been an uptick in the news coverage relating

to oil prices. Finally, this period has also seen an unprecedented diversion in worldwide oil

markets. The European Brent crude index, which from its inception in the 1980s through

the crisis was nearly perfectly correlated with the U.S. WTI, has since the crisis been trading

at premiums of anywhere from 5% to 10% and higher. Additionally the two indices are no

longer highly correlated on a monthly basis, with historical correlations of near 0.9 prior to

the crisis dropping to below 0.6 post-crisis. Interestingly, though the supply shocks identified

using the WTI are negative but insignificant during this period, when the same exercise is

done with the Brent index the significance returns.21 This is in line with anecdotal evidence

of the WTI being more affected by local transport conditions since 2009, and being replaced

by the Brent index as the true barometer of world oil markets.

21Results are reported in Appendix ??.

23

4.2 Industry Portfolios and Oil Shocks

In order to further illustrate the relation between oil prices and stock returns, and to gain

more insight into the mechanisms by which oil price shocks impact the economy, this section

explores the relation between the oil shocks and industry returns. Using partitions of 10

industries and 48 industries as constructed in Fama and French [1997], the main takeaways

are that the industries related to consumer expenditure have the strongest negative loadings

on oil supply shocks, while the industries with high oil use have the strongest positive loadings

on demand shocks. This provides support to the hypothesis of ?, that oil prices shocks act

primarily on consumer expenditure rather than a direct effect from increased cost of inputs.

However, it is also interesting to note that increases in oil prices from high oil demand seem

to be driven by an increase in activity in high oil use industries, providing further support

for the identification strategy.

Table 9 shows the results for regressions of industry returns on supply and demand shocks

for each of the ten industry portfolios constructed by Fama and French [1997]. The industries

are sorted by their loading on the oil supply shock. What is immediately apparent is that

nearly all of the industries do load negatively on the shock, but that three of the top four

industries, Consumer Durables, Consumer Nondurables, and Retail, are highly dependent

on consumer spending. While the high oil use manufacturing industries have a significantly

lower beta.


To further illustrate this, Figure 8 plots the loading on the supply shock as a function

of the relative importance of oil as an input for each of 48 industries. The measure of oil

importance is calculated using the input output tables from the BEA, and the x-axis of this

graph represents the dollars of oil necessary to produce a dollar of output for each industry.


As mentioned, there does not seem to be a systematic pattern in terms of oil use and

oil supply shock beta. Instead, it is companies which are highly dependent on consumer

spending, such as clothing, entertainment, and restaurants that seem to feel the pain the

24

most when oil prices rise. Again providing evidence that oil shocks are shocks to consumer

spending, rather than a squeeze on oil-thirsty industries.

Figure 9 repeats this plot but this time for oil demand betas. This plot shows that all

companies load highly positively on the oil demand shock, and that there does seem to be

a relation between oil use and oil demand beta. This suggests that when the manufacturing

and oil intensive areas of the economy pick up in activity, the high demand translates to high

oil prices.


4.3 International Stock Returns

In this section international equity indices are studied to study the differential effects of

oil shocks across countries. Two measures of country equity returns are used. Roughly 30

countries are chosen which have aggregate equity indices available form Global Financial

Data from at least 1988 onward. Additionally, a smaller set of countries which have country-

specific consumer good industry indices available from Datastream are examined to control

for the effect of differential compositions of industries across countries. Regressions of equity

returns for the indices on the identified supply and demand shocks are performed for the

pre-crisis period.22

Figure 10 plots the supply shock beta of each country’s aggregate market index against

average oil imports as a percentage of GDP over the period. The first thing to notice is

that most of the betas are negative, suggesting that oil supply shocks are bad news for most

countries. However, the relation is not uniform, and tends to be much stronger for countries

which import a relatively large amount of oil.


Given the apparent importance of consumers for oil prices, it is interesting to look directly

at consumer goods companies in each country, and compare it to a measure of consumer

reliance on oil. To do this, Figure 12 plots the supply beta of each country’s consumer goods

22Section ?? in the Web Appendix Table ?? reports oil supply and demand betas for each countries equityindex.

25

industry the number of passenger cars / 1000 people of the country.23 The most exposure

is in countries with high dependence on cars for transportation, with the outliers beings

southeast asian countries which import a large amount of oil. This result provides further

evidence that the channel for oil supply shocks is through the squeeze it puts on household

consumers of oil.


There appears to be less of a relation between demand shock betas and oil use. However,

both for aggregate returns and consumer goods countries there is a strong relation between

demand shock beta and the beta of the country index on the world market return. Figure 11

plots the betas of each country’s aggregate stock index with respect to an oil demand shock

against the beta of each country with respect to a world stock market return.


4.4 Predicting future Stock Returns with Oil Prices

One of the more puzzling facts about the relation between oil prices and the stock market is

the predictive relation documented by Driesprong, Jacobsen, and Maat [2008]. Prior to 2008,

increases in oil prices significantly predict low future stock returns in the following month.

When examining this relation in the context of supply and demand shocks, it appears that

both shocks contribute to this effect. Table 10 shows regressions of aggregate U.S. market

returns on past changes in oil prices, as well as past oil prices decomposed into supply and

demand shocks. The predictability is no longer significant if the crisis period is considered,

but in the pre-crisis period the result seems to be equally prevalent in both supply and demand

shocks. To extent that this change in future returns represents changes to the discount rate,

as discussed by Casassus and Higuera [2013], this result potentially suggests that the level of

oil prices is more important than the source of the shock.


23Passenger car ownership is likely to be an imperfect measure of consumer oil demand. Millard-Ball andSchipper [2011] reports that Japanese drivers drive roughly half as many miles as U.S. drivers, consistentwith their difference in supply shock betas.

26

5 Conclusion

This paper presents a new, simple, method for identifying sources of oil supply shocks as the

increase in oil prices in excess of the change predicted by the contemporaneous return on

oil producers. Using this method, oil supply shocks are shown to have a highly significant

impact on U.S. and world stock prices, in contrast to the very small correlations observed

when using aggregate changes in oil prices.

Furthermore, this impact is highest on the international level among countries with a high

dependence on oil imports, and on the domestic level among firms that depend on consumer

expenditure rather than those which rely on oil as an input. These findings provide insight

into the way oil price effect the world economy, and suggest that the important effect of oil

price shocks may be pain at the pump for consumers rather than higher prices for oil using

firms.

The construction of the shocks also provides an new technique for researchers studying the

effects of changes in oil prices, particularly those interested in studying impacts at frequencies

higher than quarterly data, or over short sample periods.

27

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Table 1: Model Parameters

Parameter Description Value

ν Share of Flow Input in Production 0.6α Elasticity of Demand 0.5

σa Volatility of Demand Shock 0.15σz Volatility of Productivity Shock 0.065ρa Persistence of Demand Shock 0.95ρz Persistence of Productivity Shock 0.95a0 Mean of Demand Shock 0z0 Mean of Productivity Shock 0

cF Cost of Flow Input 2.5

aF Flow Input Adjustment Cost 3aW Oil Well Adjustment Cost 15

d Depletion Rate (Benchmark) 1δ Deprecation of Oil Wells 0.01

λa Price of Demand Risk 2.0λz Price of Supply Risk 0.3β Discount Rate 0.995

31

Table 2: Model Simulated Prices and Returns

Annualized volatilities and monthly correlations of model shocks and simulated oil prices and oil producerreturns. ∆pt is simulated change in oil prices, Rprodt are simulated oil producer returns,ea,t are modelsimulated oil demand shocks , ez,t are model simulated producer productivity (supply) shocks, and, eλ,t aresimulated shocks to the price of risk associated with demand shocks. The model is simulated monthly for100,000 periods. Panel A reports simulation results for Benchmark model where oil wells are depleted byproduction (d = 1). Panel B reports simulation results for Neoclassical model where oil well depreciation isunaffected by production (d = 0).

Panel A) Benchmark Model

Observable VariablesCorrelation Matrix RProdt ∆pt

RProdt 1.00 Volatility 20.68 34.16∆pt 0.28 1.00 % of var. explained by:ez,t 0.01 -0.83 1.00 ez,t 0.0% 85.5%ea,t 0.96 0.32 0.00 1.00 ea,t 92.4% 13.0%eλ,t -0.28 0.11 0.00 0.00 1.00 eλ,t 7.6% 1.5%

Panel B) Neoclassical Model

Observable VariablesCorrelation Matrix RProdt ∆pt

RProdt 1.00 Volatility 11.29 60.10∆pt 0.83 1.00 % of var. explained by:ez,t -0.77 -0.95 1.00 ez,t 62.0% 95.2%ea,t 0.47 0.21 0.00 1.00 ea,t 23.4% 4.8%eλ,t -0.37 0.00 0.00 0.00 1.00 eλ,t 14.6% 0.0%

32

Table 3: Summary of Data and Identified Shocks

Panel A shows Annualized means, standard deviations, and monthly correlations. Oil Price Changes arethe returns to the NYMX second nearest maturity future. Oil producer returns are from the DatastreamWorld Integrated Oil and Gas Producer Index. U.S. Market return is the aggregate CRSP return. ξV IX isthe residual value from an estimate ARMA(1,1) for the log of the VIX index. Data are monthly and returnsare calculated in logs. Panel B shows decomposition of oil price changes into component shocks. The shocksdt, st and vt are orthogonal and satisfy the system ∆pt

RProdt

ξV IX,t

=

1 1 10 a22 a230 0 a33

stdtvt

By construction the shocks account for all variation in oil prices. Data are monthly with annualizedstandard deviations reported. Sample is January 1986 to December 2011.

Panel A) Summary of Data

Variable Description Mean Std. Dev. Correlation Matrix

RUSAt U.S. Market Return 0.044 0.162 1RProdt Oil Producer Index Returns 0.058 0.184 0.616 1ξV IX,t Innovations to VIX 0.000 0.596 -0.666 -0.452 1∆pt Changes in Oil Prices 0.060 0.338 0.066 0.454 -0.11 1

Panel B) Identified Oil Shocks

Variable Description Std. Dev % of Var. Correlation Matrix

∆pt Changes in Oil Prices 0.338 1st Supply Shocks 0.299 0.782 0.884 1dt Demand Shocks 0.153 0.206 0.454 0 1vt Risk Shocks 0.037 0.012 0.110 0 0 1

33

Table 4: Aggregate Stock Market Returns and Oil Price Shocks

Panel A shows regressions of Aggregate U.S. Stock Market Returns (return to the index of all CRSP stocks)on changes in oil prices and on constructed demand and supply shocks. Shocks are defined in Table 3. PanelB shows regressions of world aggregate stock market index on constructed shocks. World index is fromGlobal Financial Data. White [1980] standard errors in parentheses. Data are monthly from 1986 to 2011.

Panel A) U.S. Stock Market Returns and Oil Shocks

Description Variable U.S. Market Returns (RUSAt )

Oil Price Changes ∆pt 0.031(0.027)

Demand Shock dt 0.370** 0.370**(0.046) (0.063)

Supply Shock st -0.102** -0.102**(0.021) (0.037)

Innovation in VIX ξV IX,t -0.184** -0.184**(0.012) (0.016)

Constant 0.005 0.003 0.003 0.005 0.005*(0.003) (0.002) (0.003) (0.003) (0.002)

Observations 315 315 315 315 315R-squared 0.004 0.604 0.124 0.036 0.444

Panel B) World Stock Market Returns and Oil Shocks

Description Variable World Market Returns (RWorldt )

Oil Price Changes ∆pt 0.046(0.026)

Demand Shock dt 0.493** 0.493**(0.040) (0.057)

Supply Shock st -0.109** -0.109**(0.023) (0.040)

Innovation in VIX ξV IX,t -0.158** -0.158**(0.011) (0.015)

Constant 0.005 0.002 0.002 0.005* 0.005*(0.003) (0.002) (0.002) (0.003) (0.002)

Observations 315 315 315 315 315R-squared 0.010 0.619 0.231 0.043 0.345

34

Table 5: Oil Price and Stock Market Volatilities

Regressions of innovations and levels of market index volatilities on oil price volatility. Monthly volatilitiesof σmkt,t, σProd,t and σp, t are calculated as the standard deviation of daily observations for each month,using excess daily returns on the CRSP aggregate market index, excess daily returns on the datastream oilproducer index, and log of oil price changes calculated using the daily WTI index. Regressions ofdifferences include controls for the lag of the level and change of both the dependent volatility and theindependent volatility. Standard errors are Newey-West with 6 lags.

1986 - 2011 Pre-Crisis 1986 - 2011 Pre-Crisis∆σmkt,t ∆σProd,t ∆σmkt,t ∆σProd,t σmkt,t σProd,t σmkt,t σProd,t

∆σp,t 0.113** 0.044 0.063* -0.007(0.038) (0.041) (0.024) (0.036)

σp,t 0.175** 0.116** 0.064** 0.028(0.045) (0.042) (0.025) (0.028)

Constant 0.188* 0.361** 0.278** 0.496** 0.582** 1.197** 0.751** 1.261**(0.092) (0.100) (0.076) (0.118) (0.096) (0.092) (0.068) (0.071)

Controls YES YES YES YES NO NO NO NOObservations 315 315 269 269 315 315 269 269

R-squared 0.234 0.250 0.284 0.314 0.115 0.043 0.022 0.004

35

Table 6: Summary of Data and Identified Shocks: Copper and Aluminum

Panels A and C shows annualized means, standard deviations, and monthly correlations for copper dataand aluminum data respectively. Price changes are innovations to the log of spot prices on the CME.Producer returns are from the HSBC Global Copper Mining Index for copper, and the Datastream WorldAluminum Producer Index for aluminum. U.S. Market return is the aggregate CRSP return. ξV IX is theresidual value from an estimate ARMA(1,1) for the log of the VIX index. Panels B and D showdecomposition of oil price changes into component shocks. The shocks are constructed using theidentification strategy described in Table 3. Data are monthly.

Panel A) Summary of Copper Data


RUSAt U.S. Market Return 0.044 0.162 1RProdHG,t Copper Prod. Index Returns 0.056 0.357 0.580 1

ξV IX,t Innovations to VIX 0.000 0.596 -0.666 -0.486 1∆pHG,t Changes in Copper Prices 0.060 0.281 0.245 0.646 -0.201 1

Panel B) Identified Copper Shocks


∆pHG,t Changes in Copper Prices 0.281 1sHG,t Supply Shocks 0.211 0.566 0.752 1dHG,t Demand Shocks 0.176 0.394 0.628 0 1vHG,t Risk Shocks 0.056 0.040 0.201 0 0 1

Panel C) Summary of Aluminum of Data


RUSAt U.S. Market Return 0.044 0.162 1RProdAL,t Alum. Prod. Index Returns 0.041 0.338 0.713 1

ξV IX,t Innovations to VIX 0.000 0.596 -0.666 -0.452 1∆pAL,t Changes in Alum. Prices -0.005 0.218 0.237 0.413 -0.1449 1

Panel D) Identified Aluminum Shocks


∆pAL,t Changes in Alum. Prices 0.218 1sAL,t Supply Shocks 0.197 0.822 0.907 1dAL,t Demand Shocks 0.086 0.157 0.396 0 1vAL,t Risk Shocks 0.032 0.021 0.145 0 0 1

36

Table 7: Aggregate Stock Returns and Copper and Aluminum Shocks

Regressions of aggregate stock returns on constructed copper and aluminum price shocks. ∆pHG,t and∆pAL,t are log innovations are log innovations to spot prices of copper and aluminum respectively. dHG,tand dAL,t are identified demand shocks, sAL,t and sHG,t are identified supply shocks, and ξV IX,t areresidual innovations from an ARMA(1,1) of the VIX index. Data and construction for supply demandshocks for oil and aluminum are described in Table 6, and aggregate stock data are described in Table 4.Data is Monthly from 1986 to 2011. White [1980] standard errors in parentheses.

Panel A) Copper Price Shocks and Market Returns

Variable U.S. Market Returns (RUSAt ) World Market Returns (RWorldt )

∆pHG,t 0.142** 0.169**(0.032) (0.031)

dHG,t 0.271** 0.271** 0.318** 0.318**(0.044) (0.069) (0.043) (0.063)

sHG,t -0.075* -0.075 -0.040 -0.040(0.035) (0.079) (0.035) (0.071)

ξV IX,t -0.184** -0.184** -0.158** -0.158**(0.012) (0.016) (0.012) (0.015)

Constant 0.004 0.003 0.003 0.005 0.005* 0.004 0.004 0.004 0.005* 0.005*(0.003) (0.002) (0.003) (0.003) (0.002) (0.002) (0.002) (0.003) (0.003) (0.002)

Observations 315 315 315 315 315 315 315 315 315 315R-squared 0.060 0.539 0.086 0.009 0.444 0.089 0.472 0.124 0.003 0.345

Panel B) Aluminum Price Shocks and Market Returns

Variable U.S. Market Returns (RUSAt ) World Market Returns (RWorldt )

∆pAL,t 0.179** 0.174**(0.041) (0.040)

dAL,t 0.812** 0.812** 0.876** 0.876**(0.078) (0.120) (0.073) (0.117)

sAL,t -0.026 -0.026 -0.031 -0.031(0.026) (0.048) (0.030) (0.046)

ξV IX,t -0.184** -0.184** -0.158** -0.158**(0.012) (0.016) (0.009) (0.015)

Constant 0.005 0.005** 0.005* 0.005 0.005* 0.005* 0.006** 0.006* 0.005* 0.005*(0.003) (0.002) (0.002) (0.003) (0.002) (0.003) (0.002) (0.002) (0.003) (0.002)

Observations 315 315 315 315 315 315 315 315 315 315R-squared 0.056 0.626 0.181 0.001 0.444 0.056 0.567 0.221 0.001 0.345

37

Tab

le8:

Subsa

mple

Reg

ress

ions

ofA

ggre

gate

Mar

ket

Ret

urn

son

Supply

and

Dem

and

Shock

s

Reg

ress

ion

sof

sup

ply

and

dem

and

shock

seff

ects

on

U.S

.an

dW

orl

daggre

gate

mark

etre

turn

sov

erp

erio

dsu

bsa

mp

les.

hock

sare

firs

tco

nst

ruct

edas

inT

able

3fo

rth

een

tire

sam

ple

,th

enco

nst

ruct

edsh

ock

sare

regre

ssed

on

mark

etre

turn

sov

erea

chsu

bp

erio

d.

Wh

ite

[1980]

stan

dard

erro

rsin

par

enth

eses

.

PanelA)U.S

.M

arketRetu

rnsand

Oil

Shocksby

Subperiod

Vari

ab

le01/1986-1

2/2011

01/1986-0

6/2008

01/1986-1

2/1991

01/1992-1

2/2002

01/2003-0

6/2008

01/2009-1

2/2011

dt

0.3

7**

0.2

8**

0.3

7**

0.2

8**

0.1

40.6

0**

(0.0

6)

(0.0

6)

(0.1

3)

(0.0

9)

(0.0

9)

(0.1

3)

s t-0

.10**

-0.1

6**

-0.2

0**

-0.1

0*

-0.1

6**

0.1

3(0

.03)

(0.0

3)

(0.0

5)

(0.0

5)

(0.0

5)

(0.1

6)

Con

stant

0.0

00.0

00.0

00.0

1*

0.0

00.0

00.0

00.0

00.0

00.0

1*

0.0

10.0

2(0

.00)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

1)

(0.0

1)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

1)

(0.0

1)

Ob

serv

ati

on

s315

315

270

270

73

73

132

132

65

65

36

36

R-s

qu

are

d0.1

20.0

40.0

70.1

00.1

00.1

90.0

70.0

30.0

40.1

30.3

60.0

2

PanelB)W

orld

MarketRetu

rnsand

Oil

Shocksby

Subperiod

Vari

ab

le01/1986-1

2/2011

01/1986-0

6/2008

01/1986-1

2/1991

01/1992-1

2/2002

01/2003-0

6/2008

01/2009-1

2/2011

dt

0.4

9**

0.3

9**

0.5

4**

0.3

7**

0.2

4**

0.7

1**

(0.0

5)

(0.0

6)

(0.1

2)

(0.0

8)

(0.0

8)

(0.1

3)

s t-0

.11**

-0.1

6**

-0.2

1**

-0.1

2**

-0.1

5**

0.1

2(0

.03)

(0.0

3)

(0.0

5)

(0.0

4)

(0.0

5)

(0.1

7)

Con

stant

0.0

00.0

1*

0.0

00.0

1**

0.0

00.0

10.0

00.0

00.0

10.0

1**

0.0

10.0

1(0

.00)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

1)

(0.0

1)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

1)

(0.0

1)

Ob

serv

ati

on

s315

315

270

270

73

73

132

132

65

65

36

36

R-s

qu

are

d0.2

30.0

40.1

60.1

20.2

30.2

30.1

30.0

50.1

10.1

10.4

50.0

1

38

Table 9: Fama-French Ten Industry Portfolios and Oil Shocks

Description Univariate Regressions

Supply Shock Demand Shock

Beta R2 Beta R2

Consumer Durables -0.235** 0.117 0.256** 0.036(0.040) (0.081)

Retail -0.232** 0.154 0.123 0.011(0.033) (0.071)

Other -0.220** 0.157 0.190** 0.031(0.031) (0.066)

Consumer Nondurables -0.205** 0.174 0.215** 0.050(0.027) (0.058)

Manufacturing -0.188** 0.121 0.315** 0.089(0.031) (0.062)

Healthcare -0.171** 0.096 0.161* 0.022(0.032) (0.065)

High Tech -0.139** 0.026 0.199 0.014(0.052) (0.103)

Telecommunications -0.134** 0.051 0.191** 0.027(0.035) (0.070)

Utility -0.117** 0.066 0.337** 0.143(0.027) (0.051)

Energy -0.017 0.001 0.862** 0.601(0.035) (0.043)

39

Table 10: Predicting Aggregate Stock Returns with Oil Prices

Regressions of monthly aggregate market return on previous months’ innovations to oil prices and oilshocks. Oil price changes are the return to the nearest maturity NYMEX oil future. Market return is theCRSP value-weighted index. Data are monthly. White [1980] standard errors in parentheses.

Variable RMktt (01/1986 to 12/2011) RMkt

t (01/1986 to 06/2008)

∆pt−1 -0.039 -0.090**(0.031) (0.027)

st−1 -0.044 -0.085**(0.032) (0.028)

dt−1 -0.055 -0.131*(0.065) (0.063)

Constant 0.005 0.006* 0.006* 0.006*(0.003) (0.003) (0.003) (0.003)

Observations 330 330 269 268R-squared 0.002 0.015 0.039 0.043

40

Figure 1: Model Impulse Response Functions

Plots for model impulse responses to positive demand shocks (positive realization of ea,t), and negativeproductivity shocks (negative realization of ez,t) for the two model cases. In the benchmark case oilproduction results in a depletion of oil wells (d = 1), and in the standard neoclassical case there is nodepletion of reserves (d = 0).

0 5 10 15 20-0.2

-0.1

0

0.1

0.2Oil Price - Benchmark Model

0 5 10 15 20-0.2

-0.1

0

0.1

0.2Oil Price - Neoclassical Model

0 5 10 15 20-0.1

-0.05

0

0.05

0.1q - Benchmark Model

0 5 10 15 20-0.1

-0.05

0

0.05

0.1q - Neoclassical Model

0 5 10 15 20-0.2

-0.1

0

0.1

0.2Flow Input - Benchmark Model

0 5 10 15 20-0.2

-0.1

0

0.1

0.2Flow Input - Neoclassical Model

0 5 10 15 20

-0.02

0

0.02

0.04

0.06

0.08Producer Value - Benchmark Model

0 5 10 15 20

-0.02

0

0.02

0.04

0.06

0.08Producer Value - Neoclassical Model

Risk Shock

Demand Shock

Supply Shock

41

Figure 2: U.S. Monthly Stock Returns and Oil Shocks

Scatter plots of monthly aggregate U.S. stock returns against realizations of oil supply shocks and demandshocks (as described in Table 3). Observations from the three months at the beginning of the FinancialCrisis (09/2008 - 11/2008) are circled.

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.4 -0.2 0 0.2 0.4

U.S

. S

tock

Ma

rke

t R

etu

rn

Monthly Supply Shock

01/1986 - 12/2011

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.2 -0.1 0 0.1 0.2

U.S

. S

tock

Ma

rke

t R

etu

rn

Monthly Demand Shock

01/1986 -12/2011

Financial Crisis (2008Q4)

42

Figure 3: Oil Producers Stock Prices, Oil Prices, and the Gulf War

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

6/1/1990 7/21/1990 9/9/1990 10/29/1990 12/18/1990 2/6/1991 3/28/1991 5/17/1991 7/6/1991

Ashland Inc.

Occidental Petroleum

Phillips Petroleum

British Petroleum

Mobil

Chevron

AMOCO

WTI Spot Price

Invasion of

Kuwait (8/2/1990)

Operation Desert

Storm

(1/17/1991)

Panel A) Oil Producer Returns

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

6/1/1990 7/21/1990 9/9/1990 10/29/1990 12/18/1990 2/6/1991 3/28/1991 5/17/1991 7/6/1991

WTI Spot Price

Oil Producer Index

U.S. Stock Market

Invasion of

Kuwait (8/2/1990)

Operation Desert

Storm

(1/17/1991)

Panel B) Index Returns

Oil producer index is the Datastream World Integrated Oil & Gas Producer Index. U.S. Stock Return isthe value weighted CRSP index.

43

Figure 4: Supply Shock Impulse Response Functions

VAR impulse response functions to a one standard deviation oil supply shock for the logs of U.S. real GDP,U.S. total oil use, aggregate household consumption (net of gasoline), household gasoline consumption, U.S.inventories, and world oil production. Regressions performed at quarterly frequency from 1979 to 2012 withfour lags.

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Total Oil Consumption

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Household Consumption (Non-

Oil)

-3.50%

-2.50%

-1.50%

-0.50%

0.50%

1.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Household Gasoline

Consumption

-4.50%

-3.50%

-2.50%

-1.50%

-0.50%

0.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Oil Inventories

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

World Oil Production

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. GDP

44

Figure 5: Demand Shock Impulse Response Functions

VAR impulse response functions to a one standard deviation oil demand shock for the logs of U.S. realGDP, U.S. total oil use, aggregate household consumption (net of gasoline), household gasolineconsumption, U.S. inventories, and world oil production. Regressions performed at quarterly frequencyfrom 1979 to 2012 with four lags.

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Total Oil Consumption

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Household Consumption (Non-

Oil)

-3.50%

-2.50%

-1.50%

-0.50%

0.50%

1.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Household Gasoline

Consumption

-4.50%

-3.50%

-2.50%

-1.50%

-0.50%

0.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. Oil Inventories

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

World Oil Production

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

1 2 3 4 5 6 7 8 9

Quarters

U.S. GDP

45

Figure 6: Oil in the News from 1987 - 2012

Chart of WTI spot prices in 1986 dollars and % of total NY Times articles containing the phrases ”oilprice” or ”oil prices” in a given year.

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

-20

0

20

40

60

80

100

Jan-85 Jan-90 Jan-95 Jan-00 Jan-05 Jan-10

Yearly % of NY Times articles on Oil Prices

Real Oil Prices

46

Figure 7: Oil Shocks and U.S. Stock Returns - Subsample Analysis

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.4 -0.2 0 0.2 0.4

U.S

. S

tock

Ma

rke

t R

etu

rn


01/1986-12/1991

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.4 -0.2 0 0.2 0.4

U.S

. S

tock

Ma

rke

t R

etu

rn


01/1992 - 12/2002

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.4 -0.2 0 0.2 0.4

U.S

. S

tock

Ma

rke

t R

etu

rn


01/2003-06/2008

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.4 -0.2 0 0.2 0.4

U.S

. S

tock

Ma

rke

t R

etu

rn


01/2009-12/2011

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.2 -0.1 0 0.1 0.2

U.S

. S

tock

Ma

rke

t R

etu

rn


01/1986- 12/1991

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.2 -0.1 0 0.1 0.2

U.S

. S

tock

Ma

rke

t R

etu

rn


01/1992 - 12/2002

-0.25

-0.15

-0.05

0.05

0.15

0.25

-0.2 -0.1 0 0.1 0.2

U.S

. S

tock

Ma

rke

t R

etu

rn


01/2003 - 06/2008

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.2 -0.1 0 0.1 0.2

U.S

. S

tock

Ma

rke

t R

etu

rn


01/2009 - 12/2011

Plots of monthly aggregate U.S. stock returns and constructed oil shocks.

47

Figure 8: Industry Oil Supply Betas and Oil Use

Plots of industry oil use and stock market beta against oil supply shock beta. The X-axis represents theamount of oil in dollars necessary to produce a dollar of output. This ratio is calculated as the equalweighted average by SIC code of the components of the Industry Portfolios. Data is from the BEAinput-output tables and Ken French’s website. White [1980] regression line standard errors in parentheses.

Steel

ElcEq

Ships

FabPr

MedEq

Guns

Chips

BldMt

Mach

LabEq

Autos

Hshld

Hardw

Aero Cnstr

Soda

Hlth

Smoke

Chems

Toys

Rubbr

Util

Trans

Beer

Paper

Telcm

Boxes

Softw

Coal

Txtls

Mines

Agric

BusSv

Food

PerSv

Meals

Clths

Gold

Whlsl

BooksFun

Rtail

RlEst

Banks

Fin

Insur

Drugs

Airlines

y = 0.44 x - 0.19**

(0.48) (0.01)

R² = 1.9%

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0 0.02 0.04 0.06 0.08 0.1

Oil

Su

pp

ly S

ho

ck B

eta

$ of Oil Input / $ of Output

48

Figure 9: Industry Oil Demand Betas and Oil Use

Plots of industry oil use and stock market beta against oil demand shock beta. The X-axis represents theamount of oil in dollars necessary to produce a dollar of output. This ratio is calculated as the equalweighted average by SIC code of the components of the Industry Portfolios. Data is from the BEAinput-output tables and Ken French’s website. White [1980] regression line standard errors in parentheses.

Steel

ElcEq

Ships

FabPr

MedEq

Guns

Chips

BldMt

Mach

LabEq

Autos

HshldHardw

Aero

Cnstr

Soda

Hlth

Smoke

Chems

Toys

Rubbr

Util

TransBeer

Paper

Telcm

Boxes

Softw

Coal

Txtls

Mines

Agric

BusSv

FoodPerSv

Meals

Clths

Gold

Whlsl

Books

Fun

Rtail

RlEstBanks

FinInsur

DrugsAirlines

R² = 0.1119

y = 2.33** x + 0.22**

(0.97) (0.02)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.02 0.04 0.06 0.08 0.1

Oil

De

ma

nd

Sh

ock

Be

ta

$ of Oil Input / $ of Output

49

Figure 10: Country Oil Supply Shock Betas

Plot of country scaled oil imports against oil supply shock beta. The X-axis represents the average ratio ofoil imports to GDP over the sample period. Data from the EIA and the IMF. Country indices areaggregate country stock index are from Global Financial Data.

Argentina

Australia

Austria

Canada

Chile

Czech Republic

Denmark

Europe

Finland

France

Germany

Greece

Hong Kong

India

Indonesia

IrelandItaly

Japan

Malaysia

Mexico

Norway

Netherlands

New Zealand

Peru

Philippines

Portugal

Spain

Sweden

Switzerland

Taiwan

Thailand

Turkey

United Kingdom

United States

World

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

-0.09 -0.07 -0.05 -0.03 -0.01 0.01 0.03 0.05 0.07 0.09

Be

ta o

f C

ou

ntr

y E

qu

ity

Re

turn

on

Oil

Su

pp

ly S

ho

ck

Net Imports of Oil as % of GDP

Oil Supply Shock Beta vs. Oil Imports

-0.18

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Figure 11: Country Oil Demand Shock Betas

Plot of world market beta against oil demand shock beta. The X-axis represents the countries’ equity betawith aggregate stock market. Country and world market indices are aggregate stock indices from GlobalFinancial Data.

Argentina

Australia

Austria

Canada

Chile

Czech Republic

Denmark

EuropeFinland France

Germany

Greece

Hong Kong

India

Indonesia

Ireland

Italy

Japan

Malaysia

Mexico

Norway

Netherlands

New Zealand

Peru

Philippines

Portugal

SpainSweden

Switzerland

Taiwan

Thailand

Turkey

United Kingdom

United States

World

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.2 0.4 0.6 0.8 1 1.2 1.4

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ity

Re

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Oil

De

ma

nd

Sh

ock

Beta of Country Equity on World Stock Market Return

Oil Demand Shock Beta vs. World Market Beta

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Figure 12: Country Oil Supply Shock Betas - Consumer Goods Industry

Plot of passenger cars per capita against oil supply shock beta of each country’s consumergood industry. The X-axis represents the average passenger cars per 1000 people from 2003to 2012 taken from the World Bank. Country indices are Datastream country indices ofconsumer goods firms.

Austria

Australia

China

Chile

Denmark

France

Germany

Greece

India

Indonesia

Ireland

Italy

Japan

Korea, South

Malaysia

Mexico

Netherlands

Hong Kong

Hungary

New Zealand

Philippines

Portugal

Singapore

South Africa

Spain

Sweden

Taiwan

Thailand

Turkey

United Kingdom

United States

Venezuela

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0 100 200 300 400 500 600 700

Oil

Su

pp

ly S

ho

ck B

eta

of

Co

nsu

me

r G

oo

ds

Pro

du

cers

Passenger Cars / 1000 People

Passenger Cars / 1000 People vs Oil Suppy Beta of Consumer Goods Producers

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Oil Prices and the Stock Market - Semantic Scholar · Oil Prices and the Stock Market Robert C....

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