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, Bob
Jarrow, and seminar participants at Cornell University, The University of Pennsylvania, and the European Financial Association. All errors are my own.
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 1986 to 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 of stock market reaction to changes in oil prices has received little attention in the academic literature. The relation 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 of contemporaneous 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].
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.
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, suggested in 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 oil prices mean low future cash flows but also lower expected future returns, the two effects can also counteract each other when examining stock market returns. It is shown here that the identified supply and demand
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 with contemporaneous 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].
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 to discount rates and stock market returns. See?,?, Drechsler and Yaron [2011], and Bollerslev, Tauchen, and Zhou [2009] as examples.
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
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.
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 =ZtFtνW
1−ν t
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,Wtrepresents 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]
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) = aF 2 Ft−Ft−1 Ft−1 2 Ft−1 + aW 2 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) max
Ft,It
=X∞
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.
demand curve, so that spot prices Pt are given by
(6) Pt=At(Ot)−
1
α
The price is dependent upon At, representing the aggregate level of oil demand in the
economy, Ot, the total production of oil, andα, 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 supplyOt. Though producers of final goods and household consumers
are omitted for parsimony, simple intuition suggest that rises in the oil price from increases
inAt reflect positive economic news, while rises in price from a reduction inOt 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 eitherea,t+1 orez,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)
(10) λa,t+1 = ¯λa+ρλ(λa,t−λ¯a) +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
−1
α
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 =ν
Ot
Ft
(Pt−d×qt)
Whereqtis 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 pricePtwill come along with a rise in the value ofqt. 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
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
with demand shocks while being nearly uncorrelated with supply shocks, making them an effective control for identifying the two unobserved sources of variation.
[Table 2 about here.]
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 producer indices, oil prices, and risk levels, and the effects of controlling for aggregate levels of prices and exchange rates. There is no significant impact on the results.
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.
[Table 3 about here.]
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 shocksst, demand shocks dt, and risk shocks vt are
orthogonal and defined using the the system
∆pt RP rod t 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 theV IX explain on 1% of the total variance
14The CBOE changed methodology in construction of the VIX in 1990. The series here is constructed using the new ”Model Free” method going back to 1990, and the older method prior to 1990.
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) RM ktt =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.
[Table 4 about here.]
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
[Figure 2 about here.] 15Since the risk shocksv
t, are simply a scalar multiplied times ξV IX,t,ξV IX,t is used in the regression in order 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 the financial crisis. Doing so provides a marked increase in theR2of the supply shocks, from 4% to 9%.
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.
[Figure 3 about here.]
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.
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 5 about here.]
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
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.
[Table 6 about here.]
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
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
[Table 7 about here.]
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 = XN n=1β y n∆yt−n+ XN n=0 β s n∆st−n+βnd∆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 consumpconsump-tion). 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 which ranges between 2 and 5% of GDP over the sample.
18Hamilton [2003] estimates a more general form allowing for nonlinearities in the relation, the focus here is on the simpler linear relation, as there appears to be little evidence of any nonlinearity in the relation between oil prices and stock returns. See Figure 2.
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 anincreasein the oil price.
[Figure 4 about here.] [Figure 5 about here.]
19International oil production is lagged one month as lagged production seems to correlate most strongly with 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 with a 3-month lag in the ”Monthly Energy Report”.
20Recent work by Bekaert and Hoerova [2013] shows that the volatility portion of the VIX correlates most strongly with macroeconomic aggregates, suggesting that this control is appropriate for this analysis. See Web Appendix Section 1 for the relation of this construction method to stock returns.
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
[Figure 6 about here.]
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
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.
[Table 8 about here.] [Figure 7 about here.]
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 movemove-ments 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.
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.
[Table 9 about here.]
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.
[Figure 8 about here.]
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
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.
[Figure 9 about here.]
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.
[Figure 10 about here.]
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 equity index.
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.
[Figure 11 about here.]
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.
[Figure 12 about here.]
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.
[Table 10 about here.]
23Passenger car ownership is likely to be an imperfect measure of consumer oil demand. Millard-Ball and Schipper [2011] reports that Japanese drivers drive roughly half as many miles as U.S. drivers, consistent with their difference in supply shock betas.
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.
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Table 1: Model Parameters
Parameter Description Value
ν Share of Flow Input in Production 0.6
α Elasticity of Demand 0.5
σa Volatility of Demand Shock 0.15
σz Volatility of Productivity Shock 0.065
ρa Persistence of Demand Shock 0.95
ρz Persistence of Productivity Shock 0.95
a0 Mean of Demand Shock 0
z0 Mean of Productivity Shock 0
cF Cost of Flow Input 2.5
aF Flow Input Adjustment Cost 3
aW Oil Well Adjustment Cost 15
d Depletion Rate (Benchmark) 1
δ Deprecation of Oil Wells 0.01
λa Price of Demand Risk 2.0
λz Price of Supply Risk 0.3
Table 2: Model Simulated Prices and Returns
Annualized volatilities and monthly correlations of model shocks and simulated oil prices and oil producer returns. ∆ptis simulated change in oil prices,Rprodt are simulated oil producer returns,ea,t are model simulated oil demand shocks ,ez,t are model simulated producer productivity (supply) shocks, and, eλ,tare simulated shocks to the price of risk associated with demand shocks. The model is simulated monthly for 100,000 periods. Panel A reports simulation results for Benchmark model where oil wells are depleted by production (d= 1). Panel B reports simulation results for Neoclassical model where oil well depreciation is unaffected by production (d= 0).
Panel A) Benchmark Model
Observable Variables Correlation Matrix RP rodt ∆pt
RP rodt 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 Variables
Correlation Matrix RP rod
t ∆pt
RP rod
t 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%
Table 3: Summary of Data and Identified Shocks
Panel A shows Annualized means, standard deviations, and monthly correlations. Oil Price Changes are the returns to the NYMX second nearest maturity future. Oil producer returns are from the Datastream World Integrated Oil and Gas Producer Index. U.S. Market return is the aggregate CRSP return. ξV IX is the residual value from an estimate ARMA(1,1) for the log of the VIX index. Data are monthly and returns are calculated in logs. Panel B shows decomposition of oil price changes into component shocks. The shocks
dt,st andvtare orthogonal and satisfy the system ∆pt RP rod t ξV IX,t = 1 1 1 0 a22 a23 0 0 a33 st dt vt
By construction the shocks account for all variation in oil prices. Data are monthly with annualized standard deviations reported. Sample is January 1986 to December 2011.
Panel A) Summary of Data
Variable Description Mean Std. Dev. Correlation Matrix
RU SAt U.S. Market Return 0.044 0.162 1
RP rodt Oil Producer Index Returns 0.058 0.184 0.616 1
ξV 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 1
st Supply Shocks 0.299 0.782 0.884 1
dt Demand Shocks 0.153 0.206 0.454 0 1
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. Panel B shows regressions of world aggregate stock market index on constructed shocks. World index is from Global 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 (RU SA
t )
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 315 315 315 315 315
R-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 (RW orld
t )
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 315 315 315 315 315
Table 5: Oil Price and Stock Market Volatilities
Regressions of innovations and levels of market index volatilities on oil price volatility. Monthly volatilities
ofσmkt,t, σP rod,t andσp, tare 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 oil producer index, and log of oil price changes calculated using the daily WTI index. Regressions of
differences include controls for the lag of the level and change of both the dependent volatility and the independent volatility. Standard errors are Newey-West with 6 lags.
1986 - 2011 Pre-Crisis 1986 - 2011 Pre-Crisis
∆σmkt,t ∆σP rod,t ∆σmkt,t ∆σP rod,t σmkt,t σP rod,t σmkt,t σP rod,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 NO
Observations 315 315 269 269 315 315 269 269
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 data and 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 World Aluminum Producer Index for aluminum. U.S. Market return is the aggregate CRSP return. ξV IX is the residual value from an estimate ARMA(1,1) for the log of the VIX index. Panels B and D show
decomposition of oil price changes into component shocks. The shocks are constructed using the identification strategy described in Table 3. Data are monthly.
Panel A) Summary of Copper Data
Variable Description Mean Std. Dev. Correlation Matrix
RU SA
t U.S. Market Return 0.044 0.162 1
RP rod
HG,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
Variable Description Std. Dev % of Var. Correlation Matrix
∆pHG,t Changes in Copper Prices 0.281 1
sHG,t Supply Shocks 0.211 0.566 0.752 1
dHG,t Demand Shocks 0.176 0.394 0.628 0 1
vHG,t Risk Shocks 0.056 0.040 0.201 0 0 1
Panel C) Summary of Aluminum of Data
Variable Description Mean Std. Dev. Correlation Matrix
RU SA
t U.S. Market Return 0.044 0.162 1
RP rod
AL,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
Variable Description Std. Dev % of Var. Correlation Matrix
∆pAL,t Changes in Alum. Prices 0.218 1
sAL,t Supply Shocks 0.197 0.822 0.907 1
dAL,t Demand Shocks 0.086 0.157 0.396 0 1
![APPENDIX C. Job Descriptions for New Administrative Staff Positions [ ]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACH5BAEAAAAALAAAAAABAAEAAAICRAEAOw==)