Oil price shocks and stock market returns: Evidence for some European countries

Energy Economics 42 (2014) 365–377

Contents lists available at ScienceDirect

Energy Economics journal homepage: www.elsevier.com/locate/eneco

Oil price shocks and stock market returns: Evidence for some European countries☆ Juncal Cunado ⁎, Fernando Perez de Gracia Universidad de Navarra, Spain

a r t i c l e

i n f o

Article history: Received 7 August 2013 Received in revised form 17 October 2013 Accepted 19 October 2013 Available online 18 November 2013 JEL classification: G12 Q43

a b s t r a c t In this paper we examine the impact of oil price shocks on stock returns in 12 oil importing European economies using Vector Autoregressive (VAR) and Vector Error Correction Models (VECM) for the period 1973:02–2011:12. We propose an alternative oil price shock specification that takes into account both world oil production and world oil prices in order to disentangle oil supply and oil demand shocks. We find that the response of the European real stock returns to an oil price shock may differ greatly depending on the underlying causes of the oil price change. The results suggest the existence of a negative and significant impact of oil price changes on most European stock market returns. Furthermore, we find that stock market returns are mostly driven by oil supply shocks. © 2013 Elsevier B.V. All rights reserved.

Keywords: Oil demand shock Oil supply shock European real stock returns

1. Introduction In his 1983 seminal work, Hamilton initiated a well-known line of research in economics focusing on the macroeconomic impact of oil price shocks. He noted that at that time, 7 out of the 8 postwar US recessions had been preceded by a sharp increase in the price of crude petroleum. In a new recent study, Hamilton (2011) pointed out that 10 out of the 11 postwar US recessions had been preceded by a rise in oil prices. Empirical studies find a negative impact of oil price shocks on economic growth for the US (Hamilton, 1983, 1996, 2003, 2011), European countries (Cunado and Perez de Gracia, 2003), Asian economies (Cunado and Perez de Gracia, 2005) and other economies (Engemann et al., 2011). Furthermore, recent empirical studies also find that lagged oil price changes are also helpful in explaining real economic activity (Hamilton, 2011). Oil prices not only affect and help to predict relevant macroeconomic variables (i.e., real economic activity, trade balance or inflation rates, among others) but they may also exert an impact on financial variables

☆ We would like to thank James Hamilton (University of California in San Diego) for helpful comments and suggestions on earlier versions of this paper. The paper was completed while the authors were Visiting Scholars at the Economics Department in the University of California in San Diego. We also thank the editor and two referees for helpful comments and suggestions. The authors acknowledge financial support from the Spanish Ministry of Education (through project ECO2011-25422). ⁎ Corresponding author at: Universidad de Navarra, School of Economics and Business Administration, Campus Universitario, 31080 Pamplona, Spain. Tel.: +1 34 948 425625. E-mail address: [email protected] (J. Cunado). 0140-9883/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eneco.2013.10.017

(i.e., stock market returns and exchange rates). Previous studies document mixed evidence on the connection between oil price movements and stock returns. For example, initial papers by Jones and Kaul (1996) find that oil price increases have a significant negative impact on stock returns while Chen et al. (1986) and Huang et al. (1996) do not find a significant relationship. Furthermore, Diesprong et al. (2008) show that oil price changes help to forecast stock returns. In a recent study, Narayan and Sharma (2011) find that oil prices affect firm returns differently depending on their sectoral location, detecting strong evidence of impact of lagged oil prices. The aim of this paper is to investigate the effects of oil shocks – expressed in both world real prices and local real prices – on stock returns in some European economies using Vector Autoregressive (VAR) and Vector Error Correction Models (VECM) for the period 1973:02–2011:12.1 This paper contributes to the literature of oil prices and stock returns in the following ways. First, the paper covers four decades that include the significant increases of oil prices during the 1970s and the recent record peak of US$145 per barrel reached in July 2008 for some European economies. Other authors that examine European stock markets are Park and Ratti (2008), who cover the period 1986:01–2005:12 using aggregate stock indices, and Scholtens and Yurtsever (2012), who analyze the period 1983:08–2007:11 using stock industry indices instead of aggregate stock indices. Second, we

1 The beginning of the sample period is dictated by the availability of the variable world oil production (see Table 1 for a detailed description of the data).

366

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

propose an alternative specification for oil price shocks. Previous empirical studies on oil price and stock returns use different oil price specifications, such as nominal oil price variations (i.e., Driesprong et al., 2008; Jones and Kaul, 1996; Narayan and Sharma, 2011), real oil price variations (i.e., Lee et al., 2012; Park and Ratti, 2008; Sadorsky, 1999), net oil price increases (i.e., Park and Ratti, 2008; Scholtens and Yurtsever, 2012), oil price volatility (i.e., Park and Ratti, 2008; Scholtens and Yurtsever, 2012), oil future price increases (Ciner, 2001) and decomposition of oil price shocks into three components – oil supply shocks, global demand shocks and specific demand shock – (i.e., Apergis and Miller, 2009; Güntner, 2013; Kilian and Park, 2009). In this paper, and following the idea that “not all oil price shocks are alike” (Kilian, 2009), we propose an alternative oil price shock specification to disentangle demand and supply shocks. However, instead of following the methodology of imposing restrictions in the VAR model, we use the informational content on both world oil production and world oil prices to identify demand and supply oil price shocks. Our proposed oil shocks are very much related with the recent study by Rapaport (2013), who identifies demand and supply shocks based on the sign of the correlation between oil price changes and stock market returns. In our case, we will identify demand and supply shocks based on the sign of the correlation between oil price changes and world oil production variations. The structure of the paper is as follows. Section 2 reviews the literature on the nexus between oil price shocks and stock returns. Section 3 describes the relevant variables and presents the time series properties of the variables used in the empirical analysis. Section 3 also includes an alternative specification for oil price shocks. Section 4 covers the empirical analysis, in which we estimate the impact of oil price changes on 12 oil importing European stock market returns. Finally, Section 5 concludes. 2. Literature review on stock returns and oil shocks The relationship between oil prices and stock returns has been investigated extensively (i.e., Jones and Kaul, 1996; Huang et al., 1996; Sadorsky, 1999; Ciner, 2001; Diesprong et al., 2008; Apergis and Miller, 2009; Kilian and Park, 2009; Elyasiani et al., 2011; Narayan and Sharma, 2011; Lee et al., 2012; Scholtens and Yurtsever, 2012, among others). Some recent papers focus on an industry or sectoral level (i.e., Elyasiani et al., 2011; Scholtens and Yurtsever, 2012; Lee et al., 2012, among others). For example, Elyasiani et al. (2011) examined the impact of changes in oil returns and oil return volatility on excess stock returns and return volatilities of different industry sectors in the US economy using daily data from December 11, 1998 to December 29, 2008. Their results support that oil price fluctuations constitute a systematic asset price risk at the industry level in nine out of the thirteen industries. In a recent study, Scholtens and Yurtsever (2012) examine the response of stock market indices for some European industries to oil price shocks in the period 1983:08–2007:11. They find that the impact of oil price shocks substantially differs along the different industries and that the significance of the results also differs along the various oil price specifications (oil price variation, net oil price increase, scaled oil price increase). In another recent paper, Lee et al. (2012) analyze the effects of changes in oil prices on different sector stock indices in the G7 economies using monthly data from 1991:01 to 2009:05. Their results suggest that oil price shocks have a significant impact on some sector indices for some G7 countries. In this paper, instead of analyzing stock returns in industries (sectors), we focus on aggregate (or national) stock indices. Others papers that also study national stock indices are Jones and Kaul (1996), Sadorsky (1999), Park and Ratti (2008), Kilian and Park (2009), Apergis and Miller (2009) and Güntner (2013), among others. Jones and Kaul (1996) find that changes in oil prices that Grangerprecede most economic series have a detrimental effect on output and

real stock returns using quarterly data for the US, Canada, Japan and the UK for the post war period. They detect that stock markets generate a rational response only for the US and Canada (i.e., the reaction of stock returns to oil shocks can be completely accounted for by their impact on current and expected future real cash flows). In another study, Sadorsky (1999), using monthly data for the period 1947:01–1996:04, finds that both oil prices and oil price volatility play important roles in affecting US real stock returns. Park and Ratti (2008) estimate the effects of oil price shocks (and oil price volatility) on stock returns over the period 1986:01–2005:12 using monthly data for the US and 13 European economies. They find that alternative specifications of oil price shocks (i.e., oil price variation, scaled oil price and net oil price increase) have a significant impact on real stock returns. Diesprong et al. (2008) are the first that document evidence showing that changes in oil prices forecast stock returns for 18 economies using monthly data for the period 1973:10–2003:04. Kilian and Park (2009), using monthly data for the US economy over the period 1973:01 to 2006:12, relate stock returns to measures of demand and supply shocks in the world crude oil market. They find the following three results: a negative response of stock returns to an oil-market specific demand shock; a positive effect of a global demand shock on stock returns, and a not significant effect of oil supply shocks on stocks returns. Apergis and Miller (2009), using monthly data for 8 economies during the period 1981:01–2007:12, find that oil supply shocks, aggregate demand shocks, and oil market idiosyncratic demand shocks contribute significantly to explain stock returns. Recently, Güntner (2013), using monthly data from 1974 to 2011 for 6 OECD countries, examines the differences and commonalities of stock price responses in oil exporting and importing economies. His results support that unexpected reductions in world oil supply do not affect stock returns in any of the selected economies. Instead of focusing on previous definitions of oil shocks (i.e., oil price variations, real oil price variations, net oil price increases, oil price volatility, oil future price increases and decomposition of oil price shocks into three components – oil supply shocks, global demand shocks and specific demand shocks – ), in this paper we propose an alternative oil price shock specification that takes into account both world oil production and world oil prices in order to disentangle oil supply and oil demand shocks. Specifically, we will identify demand and supply shocks based on the sign of the correlation between oil price changes and world oil production variations. Thus, when oil prices and world oil production vary in the same direction, we will identify this as an oil demand shock, and when the sign of the correlation between oil price and world oil production changes is negative, we will identify this as an oil supply shock. 3. A first look at the data In this section we initially describe the sample period and the variables. Then we analyze the time series properties of the relevant variables for each of the selected economies by means of conducting unit root and cointegration tests. Finally, we review previous specifications of oil price shocks and we propose an alternative specification for the oil price shock that we use in the empirical analysis. 3.1. Data We use monthly data to analyze the impact of oil price shocks on 12 European stock markets for the period 1973:02–2011:12.2 The starting date of the sample period is determined by the availability of monthly world crude oil production data that corresponds to the beginning of the first oil price shock (October 1973). The countries included in the analysis are Austria, Belgium, Denmark, Finland, France, Germany, Italy, Luxembourg, Netherlands, Spain, Portugal and the UK. Others 2

The sample period for Denmark runs from 1974:01 to 2011:12.

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

367

Table 1 Variable description and sources. Name

Description

Source

Stock returns

Share prices, end of period

Industrial production Interest rate

Industrial production index, seasonally adjusted Short-term interest rate

Exchange rate Oil price CPI Oil production

Number of units of national currency per US dollar, period average UK Brent nominal oil price in US dollars per barrel Consumer price index for all items, seasonally adjusted Production expressed in thousands of barrels per day

International Financial Statistics (International Monetary Fund) and Global Financial Data International Financial Statistics (International Monetary Fund) International Financial Statistics (International Monetary Fund) and Global Financial Data International Financial Statistics (International Monetary Fund) International Financial Statistics (International Monetary Fund) International Financial Statistics (International Monetary Fund) US Energy Information Administration

papers that also use monthly data are Sadorsky (1999), Park and Ratti (2008), Diesprong et al. (2008) and Lee et al. (2012), among others. The data for the selected economies are obtained from the International Financial Statistics (International Monetary Fund) and the Global Financial Data, while oil prices and oil production data are obtained from the International Financial Statistics (International Monetary Fund) and the US Energy Information Administration, respectively (see Table 1 for variable definition and sources). Following previous empirical studies on oil price changes and stock returns, we include the following variables in the analysis: stock prices, real output, oil prices and short-term interest rates (i.e., Sadorsky, 1999; Park and Ratti, 2008, among others). That is, we collect the following time series data: - Oil prices. In this paper we use the real national price for each country, defined as the product of the nominal oil price (UK Brent nominal oil price) and the exchange rate (number of units of local currency per one US$) deflated by the consumer price index of each country. We also use the world real oil price, defined as the nominal oil price deflated by the US producer price index. UK Brent oil price is already used in other studies that analyze international evidence of oil shocks on macroeconomic variables (i.e., Cunado and Perez de Gracia, 2003, 2005; Engemann et al., 2011). - Oil production. The monthly world oil production data is obtained from the US Energy Information Administration (September 2012, Monthly Energy Review) and covers the period 1973:01–2011:12. The beginning of the sample period is dictated by the availability of world oil production data. We will use both oil price and oil production to decompose oil price shocks into two components: oil supply and oil demand shocks. World oil production is already used by Kilian (2009), Kilian and Park (2009) and Güntner (2013), among others. - Stock returns. We define real stock returns as the difference between continuously compounded returns on the stock price index and the inflation rate (proxied by the first logarithmic difference of the consumer price index). Our definition of real stock returns is in line with the previous empirical literature (see, for example, Park and Ratti, 2008). - Short-term interest rates. The inclusion of short-term nominal interest rates is based on Bernanke et al. (1997). In addition of a direct channel of oil price changes on economic activity, Bernanke et al. (1997) also take into account an indirect effect of oil price shocks on real economic activity due to central bank's response to higher oil prices. Sadorsky (1999), Park and Ratti (2008) and Lee et al. (2012) among others, also include interest rates when they analyze the impact of oil price changes on real stocks returns. - Industrial production. Real industrial production is defined as the nominal industrial production deflated by the consumer price index of each country. Other papers that also use real industrial production as a proxy variable for real economic activity are Sadorsky (1999) and Park and Ratti (2008).

3.2. Time series properties For each economy we test for unit roots in all the variables (in natural logs, except for the short-term interest rates). We report the Augmented Dickey–Fuller (ADF, Dickey and Fuller, 1981) and the Phillips and Perron (PP, Phillips and Perron, 1988) unit root tests. Table 2 shows the ADF and PP unit root tests for each of the variables (and their first differences). The results show that all the four variables – real oil prices, real stock prices, nominal interest rates and real industrial production – are integrated of order one variables (i.e., stationary in first differences). One problem that faces conventional ADF and PP unit root tests is that they may fail to reject the null hypothesis when structural breaks are present. Perron (1989) proposed to allow for an exogenous break in the ADF unit root test. Several authors, such as Christiano (1992), Perron and Vogelsang (1992) or Zivot and Andrews (1992) have developed methods to endogenously search for a break point and test for the presence of a unit root when the process has a broken constant or trend and have demonstrated that their tests are robust and more powerful than the conventional unit root tests. These last procedures have also been criticized in the literature, since these type of tests derive their critical values assuming no breaks under the null, so that, in the presence of a unit root with break, these tests will tend to reject the null hypothesis suggesting that the time series is stationary around a break when it is nonstationary with a break. In order to solve this problem, we use the endogenous one break LM unit root test proposed by Lee and Strazicich (2001) which is unaffected by breaks under the null. Following these authors, a unit root test statistic can be obtained by estimating the following model: ′ Δyt ¼ δ ΔZ t þ ϕe St−1 þ

Xp i¼1

γΔe St−i þet ;

ð1Þ

e −Z e where Zt reflects the deterministic components, e St ¼ yt −Ψ x tδ , e t = 2,3,…,T. δ is a vector of coefficients in the regression of Δyt on e ¼ y −Z e ΔZt and Ψ x 1 1 δ, where y1 and Z1 denote the first observations of yt and Zt, respectively. et is the contemporaneous error term and is assumed independent and identically distributed with zero mean and finite variance. ΔSt − i are added to eliminate possible serial correlation. When Zt = {1,t}, we have the statistic proposed in Schmidt and Phillips (1992). If we want to account for some structural breaks, we can extend the models A (which allows for a one-time change in level) and C (which allows for a change in both the level and trend) considered by Perron (1989) and define Zt in the following ways: Zt = {1,t,D}′ for model A and Zt = {1,t,D,DT}' for model C, where Dj = 1 for t ≥ TBj + 1 and zero otherwise, DTj = t for t ≥ TBj + 1 and zero otherwise, and TBj are the date of the breaks. The unit root null hypothesis is described by ϕ = 0 and the LM test tstatistic is defined by: e τ = t-statistic for the null hypothesis ϕ = 0. To implement the test, the number of augmentation terms Δe St−i ; i ¼ 1; …; k that correct for serial correlation in Eq. (1) must be determined. At each

368

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

Table 2 ADF and PP unit root tests. Oil prices ADF Variables in levels Austria Belgium Denmark Finland France Germany Italy Luxembourg Netherlands Portugal Spain UK World

−2.38 −2.35 −2.01 −2.44 −2.34 −2.25 −2.24 −2.52 −2.50 −2.35 −2.40 −2.36 −2.44

Variables in first differences Austria −18.99*** Belgium −18.41*** Denmark −16.57*** Finland −19.26*** France −18.29*** Germany −18.06*** Italy −18.24*** Luxembourg −19.51*** Netherlands −19.19*** Portugal −18.57*** Spain −18.31*** UK −19.40*** World −16.99***

Stock prices PP −2.27 −2.22 −1.71 −2.32 −2.23 −2.31 −2.08 −2.43 −2.35 −2.14 −2.16 −1.16 −2.21 −18.82*** −18.05*** −16.39*** −19.07*** −18.06*** −18.83*** −17.95*** −19.42*** −19.08*** −17.12*** −17.71*** −18.36*** −16.97***

ADF −2.26 −2.01 −1.82 −1.71 −1.00 −0.90 −2.03 −1.55 −0.90 −2.40 −1.49 −1.39

−17.36*** −18.10*** −17.81*** −17.14*** −19.65*** −20.41*** −20.22*** −17.43*** −19.68*** −19.83*** −18.59*** −16.57***

Interest rates

PP

ADF

−2.44 −2.03 −1.76 −1.85 −2.51 −1.02 −2.31 −1.53 −0.92 −1.47 −1.47 −1.21

−3.20 −2.34 −2.98 −2.00 −2.58 −2.65 −2.71 −1.65 −2.94 −2.45 −2.22 −2.44

−17.84*** −18.09*** −18.60*** −17.21*** −19.65*** −20.41*** −20.22*** −17.71*** −19.76*** −19.83*** −18.59*** −16.57***

−10.51*** −16.51*** −29.32*** −24.75*** −17.34*** −20.39*** −20.90*** −20.89*** −16.52*** −6.64*** −19.85*** −20.00***

PP −3.01 −2.28 −3.07 −1.96 −2.96 −2.62 −1.92 −1.87 −2.25 −2.70 −2.11 −3.04

−15.16*** −16.07*** −30.30*** −24.65*** −17.29*** −20.51*** −20.74*** −21.00*** −16.25*** −16.11*** −19.71*** −19.82***

Real industrial production ADF −1.97 −0.95 −2.48 −1.84 −1.35 −2.39 −1.51 −2.63 −2.18 −0.38 −1.78 −2.06

−22.92*** −16.51*** −6.91*** −23.21*** −5.90*** −26.12*** −6.27*** −21.87*** −21.53*** −21.81*** −13.41*** −23.65***

PP −2.25 −1.20 −5.09*** −1.87 −1.08 −2.53 −1.16 −2.68 −2.09 −0.53 −8.22*** −2.01

−33.44*** −37.95*** −40.72*** −26.81*** −28.47*** −26.08*** −29.24*** −31.57*** −35.00*** −33.70*** −101.4*** −24.25***

Notes. ADF: Augmented Dickey–Fuller unit root tests. PP: Phillips–Perron unit root tests. *** Means significant at the 1% level. Oil prices and real industrial production are in logs. In all the cases, the unit root tests have been applied using both the model with constant and the model with constant and a linear trend, and the results are the same. The values in the cells correspond to the statistic obtained with the model with constant and a linear trend for those cases in which the linear trend was significant. The lag length in all the tests has been selected according to the Akaike Information Criteria (AIC), although a robustness analysis suggests that the results of these tests are robust to the chosen lag length.

combination of break points, k is determined by following the general to specific procedure suggested by Perron (1989). The procedure begins with a maximum number of lagged first-differenced terms (k = 8) and examines the last term to see if it is significantly different from zero at the 10% level. If it is insignificant, the maximum lagged term is dropped and the model is reestimated with k = 7 terms and so on, until either the maximum term is found or k = 0. After determining the “optimal” number of k, the unit root test statistic is estimated using e τ . The process is repeated for each λ, to determine the LM test statistic with the minimum t-value.3 The results of applying the one-break LM unit root tests are shown in Table 3. As expected, we find significant structural breaks in most of the series. It is worth mentioning that the break found in 1986:01 for world and national oil prices, coincides with the oil price collapse of 1986 (see the oil price behavior in Fig. 1). When allowing for a change or a break in the intercept (Model A), the null hypothesis of a unit root is only rejected in a few cases (stock prices in Finland, and interest rates in France and the UK). When allowing for a change in both the intercept and the linear trend, the null hypothesis of unit root is rejected in more cases, such as oil prices in several countries (Austria, Finland, Italy, Netherlands and the UK, although at a 10% significant level only), stock prices in Austria, Denmark, Finland, France and Portugal, interest rates in Austria, Denmark, France and the UK and real industrial production in France, Italy, Netherlands, Portugal and Spain. However, since at the 1% and 5% level of significance we cannot reject the null hypothesis in most of the series, we proceed under the assumption that each of the 3 See Lee and Strazicich (2001) for a more detailed description of the test. The computation of the LM unit root test statistic has been carried out using the Gauss codes provided by Junsoo Lee and available on the web site http://www.cba.ua.edu/~jlee/gauss.

series can be described as difference stationary, as in most of the related literature (i.e., Sadorsky, 1999; Park and Ratti, 2008; Miller and Ratti, 2009 among others). Once we assume that all the variables contain a unit root, we test for cointegration (Johansen and Juselius, 1990) using both the trace and the maximum Eigenvalue tests. According to the results presented in Table 4, we reject the null hypothesis of no cointegration among the variables in all the countries except for Germany, where there is no evidence of cointegration at a 5% significance level. Furthermore, the results (at a 1% and 5% significance level) suggest the existence of one cointegrating vector in the rest of the cases except for Portugal (when using both national and world oil prices) and Denmark and the UK (when using world oil prices), where we find two cointegrating vectors. That is, the results suggest the existence of a long-run relationship in most of the European countries between the four variables analyzed in the paper: real stock prices, real industrial production, interest rates and real oil prices (both national and world prices). In order to interpret better these results, and given the evidence in favor of a one cointegrating vector, we estimate this vector normalizing on the stock prices for all the countries in our sample (except Germany) and find significant coefficients for industrial production and interest rates, and insignificant coefficients for oil prices. That is, the results suggest the existence of a long-run relationship among stock prices, industrial production and interest rates, while the effect of oil prices on stock returns is limited to the short-run. We will take into account this result when analyzing the short-run linkages between the variables by means of estimating VAR and VECM models in Section 4. The same approach has been used in Apergis and Miller (2009) and Lee et al. (2012) to analyze the relationship between oil prices and stock markets.

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

369

Table 3 One-break LM unit root tests (Lee and Strazicich, 2001). Oil prices

Oil prices

Stock prices

Stock prices

Model A (change in the intercept)

Model C (change in the intercept and trend)

Model A (change in the intercept)

Model C (change in the intercept and trend)

LM stat Variables in levels Austria Belgium Denmark Finland France Germany Italy Luxembourg Netherlands Portugal Spain UK World

Variables in levels Austria Belgium Denmark Finland France Germany Italy Luxembourg Netherlands Portugal Spain UK

−1.68 −1.70 −2.07 −1.73 −1.67 −1.72 −1.66 −1.69 −1.72 −1.64 −1.95 −1.97 −1.76

Tb

LM stat

Tb

LM stat

Tb

LM stat

Tb

1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01 1986:01

−4.17* −4.06 −3.31 −4.16* −3.88 −3.76 −4.32* −4.03 −4.13* −3.99 −3.34 −4.34* −3.32

1986:01 1986:01 n 1986:01 1985:12 1986:01 1986:01 1986:01 1986:01 1986:01 1985:12 1985:12 1985:12

−2.58 −2.18 −3.12 −3.50* −2.40 −2.76 −1.92 −2.75 −2.11 −1.46 −1.16 −1.74

n n n 2001:02 2001:08 1991:12 n 2008:01 2001:10 n 2002:09 n

−4.15* −3.79 −4.48** −5.31*** −4.67* −4.10 −3.88 −3.88 −3.60 −4.17* −2.78 −3.29

2005:04 1997:09 2005:04 1998:10 1998:07 1997:09 1998:08 n 1992:10 1978:01 1985:08 1992:09

Interest rates

Interest rates

Real industrial production

Real industrial production

Model A (change in the intercept)

Model C (change in the intercept and trend)

Model A (change in the intercept)

Model C (change in the intercept and trend)

LM stat

Tb

LM stat

Tb

LM stat

Tb

LM stat

Tb

−3.11 −2.23 −2.99 −1.85 −3.38* −3.04 −1.76 −2.24 −2.92 −1.43 −1.52 −3.80**

1981:12 1995:03 1978:09 1993:12 1993:03 1979:11 1992:09 1980:01 1980:01 n 1978:09 1979:08

−4.09* −3.23 −4.10* −3.59 −4.33* −3.51 −4.03 −3.42 −3.63 −3.13 −3.59 −4.56**

1997:03 1983:03 1983:12 1993:12 1983:02 n 1984:03 n 1982:11 1992:12 1984:07

−1.89 −1.49 −0.48 −1.17 −1.47 −2.15 −2.70 −1.80 −1.19 −1.96 −1.42 −0.91

n n 1978:06 1977:02 1978:05 1991:12 1978:12 1976:11 1986:03 1977:02 1981:07 1979:06

−3.96 −3.38 −2.57 −2.32 −5.52*** −4.27* −8.80*** −4.00 −4.41* −5.14*** −4.70** −3.47

1999:01 1984:12 1983:09 1986:04 1984:11 1986:01 1984:01 1985:01 1983:11 1984:02 1982:07 1982:06

Notes. The 10%, 5% and 1% critical values have been obtained from Lee and Strazicich (2001). Tb denotes the estimated break point. n denotes that the identified break point was not significant at the 10% level. *, ** and *** mean significant at the 10%, 5% and 1% level, respectively. Oil prices and real industrial production are in logs. When applying the unit root tests to the variables in first differences, we reject the null hypothesis in all the cases.

- Oil price variation. This variable is defined as the first log difference of real oil prices

3.3. Oil price shock specifications Previous empirical literature that relate oil prices to both macroeconomic and financial variables have proposed the following specifications for oil price shocks.

Δoilt ¼ oilt −oilt−1 ;

ð2Þ

76,000 72,000 68,000 64,000 1.2

60,000

1.0

56,000

0.8

52,000 48,000

0.6 0.4 0.2 0.0 1975

1980

1985

1990

1995

2000

2005

2010

Fig. 1. Real world oil price and oil production, 1973:01–2011:12. Note. World real oil prices are measured in the left axis, and oil production in the right hand axis.

370

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

Table 4 Johansen and Joselius cointegration tests (variables: oil prices, industrial production, interest rates and stock prices). r≤1

r=0

World oil prices Austria Belgium Denmark Finland France Germany Italy Luxembourg Netherlands Portugal Spain UK

National oil prices Austria Belgium Denmark Finland France Germany Italy Luxembourg Netherlands Portugal Spain UK

r≤2

r≤3

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat

58.96*** 37.63*** 77.99*** 47.87*** 59.10*** 29.04*** 75.71*** 49.14*** 94.22*** 70.51*** 45.76* 26.91* 75.34*** 53.97*** 57.48*** 37.53*** 65.36*** 45.48*** 142.57*** 85.22*** 148.60*** 125.45*** 90.24*** 64.42***

82.75*** 48.08*** 86.91*** 48.37*** 79.41*** 33.19** 88.61*** 52.08*** 122.79*** 80.53*** 53.97 28.30 97.48*** 55.63*** 70.74*** 37.53*** 78.40*** 49.48*** 156.29*** 86.09*** 163.64*** 126.94*** 113.79*** 69.40***

21.43 12.18 23.11 13.53 30.05** 22.75** 26.57 19.70* 23.71 15.01 18.85 13.30 21.37 13.39 19.94 15.05 19.87 14.53 57.36*** 46.49*** 23.14 13.98 25.83 15.56

34.68 17.07 38.53 17.86 46.22** 27.25** 36.52 20.42 42.26* 20.79 25.66 13.96 41.85* 23.83* 33.21 18.20 28.91 15.19 70.20*** 46.87*** 36.70 23.93* 44.39** 21.42

9.14 8.03 9.58 9.07 7.30 5.54 6.86 6.39 8.70 7.17 5.55 5.09 7.98 6.43 4.89 4.37 5.34 4.87 10.87 8.55 9.17 5.91 10.26 9.35

17.60 11.24 20.67 12.35 18.97 13.81 16.11 9.73 21.47 14.49 11.70 7.15 18.02 11.6 15.01 10.75 13.72 9.77 23.34 17.17 12.77 8.35 15.53 23.42

1.11 1.11 0.51 0.51 1.76 1.76 0.48 0.48 1.53 1.53 0.46 0.46 1.54 1.54 0.52 0.52 0.47 0.47 2.32 2.32 3.25 3.25 0.91 0.91

6.37 6.37 8.32 8.32 5.16 5.16 6.38 6.38 6.98 6.98 4.55 4.55 6.42 6.42 4.26 4.26 3.96 3.96 6.17 6.17 4.42 4.42 7.44 7.44

Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat Trace statistic Max-Eigen stat

57.67*** 36.30*** 68.37*** 46.89*** 48.15*** 25.66*** 71.22*** 45.84*** 86.92*** 64.43*** 43.83 26.89* 75.28*** 55.14*** 50.81** 29.84** 58.15*** 39.80*** 143.07*** 86.18*** 137.38*** 116.96*** 85.96*** 59.05***

82.74*** 49.22 82.86*** 49.15*** 71.31*** 34.22** 84.26*** 50.24*** 118.14*** 80.72*** 50.52 27.33 97.36*** 57.79*** 64.11** 29.90* 75.71*** 49.70*** 151.96*** 86.56*** 149.85*** 120.48*** 108.92*** 67.51***

21.37 12.90 21.48 12.69 22.49 16.98 25.39 18.24 22.48 14.10 16.95 12.19 20.13 12.21 20.97 15.89 18.35 12.81 56.89*** 47.11*** 20.42 13.39 26.91 16.23

33.51 16.36 33.70 13.33 37.09 19.12 34.02 18.25 37.41 18.28 23.19 12.41 39.58 23.63* 34.21 18.87 26.01 14.80 65.40*** 47.70*** 29.37 15.37 41.41* 17.99

8.48 7.58 8.79 8.13 5.50 4.46 7.15 6.40 8.38 7.24 4.75 4.68 7.92 6.13 5.08 4.26 5.54 5.00 9.78 7.08 7.03 4.32 10.69 9.59

17.15 11.50 20.38 12.53 17.98 13.59 15.77 9.40 19.13 11.95 10.78 7.07 15.95 9.91 15.34 11.08 11.21 7.18 17.70 12.47 14.00 9.69 23.42 15.68

0.90 0.90 0.66 0.66 1.04 1.04 0.75 0.75 1.14 1.14 0.07 0.07 1.79 1.79 0.81 0.81 0.54 0.54 2.70 2.70 2.71 2.71 1.10 1.10

5.65 5.65 7.85 7.85 4.39 4.39 6.37 6.37 7.18 7.18 3.71 3.71 6.04 6.04 4.26 4.26 4.03 4.03 5.24 5.24 4.31 4.31 7.74 7.74

Notes. (1) model with an intercept. (2): model with and intercept and a linear trend. r: number of cointegrating vector. *, ** and *** denote rejection of the null hypothesis at the 10%, 5% and 1% levels of significance, respectively. In column 3 (r = 0) we test the null hypothesis of no cointegration against the alternative of cointegration. In column 4 we test the null hypothesis of 0 or 1 cointegrating vector against the alternative of r = 2. The lag length in all the tests has been selected according to the Akaike Information Criteria (AIC), although a robustness analysis suggests that the results of these tests are robust to the chosen lag length.

where oilt is the log level of the real oil price at time. Oil price variation is extensively used in the empirical literature on oil shocks and macroeconomic variables (see, for example, Hamilton, 1983, 1996). - Oil price increases. Initially proposed by Mork (1989), this variable only considers increases in oil prices, that is

þ

Δoilt ¼ maxð0; Δoilt Þ:

environment where oil prices have been stable than in an environment where the oil price movement has been frequent and erratic because price changes in a volatile environment are likely to be soon reversed. They consider a Generalized Autoregressive Conditional Heteroskedasticity – ARCH – (1,1) model to estimate this variable:

ð3Þ Δoilt ¼ α þ

- Scaled oil price increases. Lee et al. (1995) focus on oil price volatility arguing that an oil shock is likely to have a greater impact in an

p q X X α i Δoilt−i þ βi zt−i þ εt ; εt jIt−1 eNð0; ht Þ; i¼0

i¼0

2

ht ¼ γ0 þ γ 1 εt−1 þ γ 2 ht−1 ;

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

where εt is an error term, {zt − 1:i ≥ 1} that denotes an appropriately chosen vector contained in information set It − 1. Scaled oil price increases (sopi) is defined as  qffiffiffiffiffi sopit ¼ max 0; εˆt = hˆt :

ð4Þ

- Net oil price increases. Hamilton (1996, 2003) suggests that it seems more appropriate to compare the current price of oil with where it has been over the previous periods instead of considering uniquely the previous period. Hamilton thus proposes to use the amount by which the oil price in period t exceeds its maximum value over the previous periods; if oil prices are lower than they have been at some point during the most recent years, an oil shock is said to have not occurred. The net oil price increase (nopi) is defined as

decrease in oil prices in 2009, which we identify as an oil demand shock since it occurs together with a decrease in the oil production. Following the existing literature, we also calculate oil demand and supply shocks using national real oil prices instead of the world real oil prices, that is, we calculate national oil demand and supply shocks using Eqs. (6) and (7) where Δoilt are now the growth rates of each of the national real oil prices. 4. Empirical analysis The empirical model estimated in this paper has already been used in the context of oil prices and economic activity by Hamilton (1983, 1996, 2011), Mork (1989), Lee et al. (1995), Bernanke et al. (1997) and Cunado and Perez de Gracia (2003, 2005) among many others, and it is based on the VAR methodology proposed by Sims (1980). A VAR model of order p, where the order p represents the number of lags, that includes k variables, can be expressed as: yt ¼ A0 þ

nopit ¼ max½0; ln ðoilt Þ−lnðmaxðoilt−1 ; …; oilt−12 Þ:

ð5Þ

- Decomposition of oil shocks. The recent studies by Kilian (2009) and Peersman and Van Robays (2009) distinguish between three different types of oil shocks: an oil supply shock, an oil demand shock driven by the global economic activity and an oil specific demand shock. To identify previous shocks, Kilian (2009) and Peersman and Van Robays (2009) impose sign restrictions on the estimated VAR models. This approach is already used to analyze the stock market response to oil shocks by Kilian and Park (2009), Apergis and Miller (2009) and Güntner (2013). In this paper, and taking into account that recent oil price increases are driven by both supply and demand conditions (see, for example, Hamilton, 2008), and to the extent that different oil price shocks may have different effects on the economy and, thus, on stock returns, we propose the following specification on both oil supply shocks (osst): osst ¼ Δoilt if signðΔoilt Þ≠signðΔyoilt Þ; and 0 otherwise;

ð6Þ

and oil demand shocks (odst), odst ¼ Δoilt if and 0 otherwise;

signðΔoilt Þ ¼ signðΔyoilt Þ;

ð7Þ

where Δoilt and Δyoilt are the growth rates of world real oil prices and world oil production, respectively, in time t. That is, an oil price change is identified as an oil supply shock if the sign of the oil price variation is different from the sign of the oil production variation, while it is identified as an oil demand shock if these signs are equal. For example, an oil price increase (decrease) together with an oil production increase (decrease) will be identified as a demand shock, while an oil price increase (decrease) followed by an oil production decrease (increase) will be identified as a supply shock. Fig. 1 shows the temporal evolution of both world oil prices and oil production and justifies the new specification we propose in this paper. For example, we observe that the increase in oil prices in 1979 corresponds to a period of decreasing oil production, which suggests the existence of an oil supply shock in that period. Furthermore, the decrease in the oil price in 1986 is followed by an increase in oil production, which suggests that this price change can be considered a positive oil supply shock. However, the increase in the oil prices in 2009 occurs in a moment of increasing oil production, which suggests that we can identify it as an oil demand shock. The same happened with the

371

Xp

Ay i−1 i t−i

þ ut ;

ð8Þ

where yt = [y1t ⋯ ykt]′ is a column vector of observation on the current values of all variables in the model (real stock prices, real industrial production, nominal interest rates and oil prices); Ai is k x k matrix of unknown coefficients; A0 is a column vector of deterministic constant terms; ut is a column vector of errors with the following properties, Eðut Þ ¼ 0 ∀t; Eðus u′t Þ ¼ Ω if s ¼ t; Eðus u′t Þ ¼ 0 if s≠t; where u′t are not serially correlated but may be contemporaneously correlated and Ω is the variance–covariance matrix with non-zero offdiagonal elements. Given a VAR(p) model of I(1) variables, there always exists an error correction representation of the form Δyt ¼ ∏yt−1 þ B0 þ

Xp−1 i¼1

ΔBi yt−i þ vt ;

ð9Þ

where Δ is the first difference operator; yt − i is a vector of error correction terms; Π is the matrix denoting the speed of adjustment toward the equilibrium and rank(Π) = r, the number of cointegration vectors, which in our case, and based on the previous results, we assume it is equal to 1 (except for the case of Germany, in which r = 04), B0 is a column vector of deterministic constant terms and the column vector of errors, vt, satisfy the same conditions as the ut in Eq. (8). Based on this model, we analyze the impact of oil price changes on stock market returns by examining generalized impulse response functions obtained by estimating the previous VECM. As explained above and following previous empirical studies on oil price changes and stock returns, we include the following variables in the model: stock prices, real industrial production indexes, short-term interest rates and different specification for oil price shocks. The main contribution in the paper is the use of different oil price shock specifications: (i) oil price variation as defined in (2), both in world and national prices; (ii) oil supply shocks, as defined in (6), in world and national prices; and (iii) oil supply shocks, as defined in (7), and also in world and national prices. Fig. 2 shows the generalized impulse response functions5 of real stock returns to a shock to the following variables: (i) world real oil price changes (in the first column); (ii) world oil supply shocks (in the second column); and (iii) world oil demand shocks (in the third column). The dashed lines represent the 95% confidence bounds for the response of stock returns to the shocks. In line with previous studies that also 4 For Germany, we estimate a VAR model where all the variables are included in first differences. 5 Generalized impulse response functions are presented since this analysis is not sensitive to the ordering of the variables. The lag length of the VECM is selected according to the Akaike Information Criteria (AIC).

372

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

Fig. 2. Generalized impulse-response functions of real stock returns to world real oil shocks, oil demand shocks and oil supply shocks. Notes. Solid line represents the generalized response function of stock returns to a shock in oil price change, oil demand shock or oil supply shock. Dotted lines are 95% confidence bounds.

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

Fig. 2. (continued).

373

374

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

disentangle oil price shocks into different demand and supply components (see, for example, Kilian and Park, 2009; Apergis and Miller, 2009; Güntner, 2013), we also find that the response of the aggregate European real stock returns may differ depending on the nature of the oil price shock. The main results can be summarized as follows. First, we find significant negative effects of oil price shocks – see the first column in Fig. 2 – on stock market returns in several European countries such as Belgium, Denmark, Finland, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain, and the UK. Second, and in order to analyze the possible different effects of oil demand and oil supply shocks, we also estimate the previous VECM including each one of these oil price variables – see columns two and three respectively – . The results, also shown in Fig. 2, suggest that oil demand and oil supply shocks have different effects on real stock returns. Comparing columns (2) and (3) in Fig. 2, we find that oil demand shocks have a significant negative effect on stock returns only in Germany, Italy, Luxembourg and the UK, while they have a positive significant effect on stock returns in France. However, oil supply shocks have a significant negative effect on stock returns in Belgium, Finland, France, Germany, Italy, Netherlands, Portugal Spain and the UK. Therefore, we could conclude that oil supply shocks exert more negative effects on European stock returns than oil demand shocks. This result is in line from the finding by Kilian and Park (2009) for the US stock returns and by Apergis and Miller (2009) for 8 economies, who also find different effects due to oil supply and oil demand shocks. These authors also found that global demand exerts a positive impact on real stock returns while idiosyncratic oil price demand has a negative impact on stock returns. In this case, the results are not comparable since in our case we only have an oil demand shock. Our oil demand shock variable may be capturing the effects of both the global demand and the idiosyncratic oil demand variables. In summary, these results suggest that not all oil price changes have the same impact on stock market returns. In fact, we find that real stock returns are more sensitive to oil supply shocks than to oil demand shocks. We also examine the impact of oil shocks on real stock returns using local oil prices. In Fig. 3 we show the generalized impulse response functions of real stock returns to a shock to: (i) national oil price changes (in the first column); (ii) national oil demand shocks (in the second column); and (iii) national oil supply shocks (in the third column). The main results obtained in this case are the following. First, we also find significant negative effects of oil price shocks on stock market returns in most of the European countries (Belgium, Finland, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain and the UK). Among all European stock markets, Italy represents the case where oil price shocks generate the largest negative effect on stock returns. A similar result for the Italian stock market can be found in Apergis and Miller (2009). Second, and when decomposing oil price changes in demand and supply shocks – see columns (2) and (3) in Fig. 3 – , we also find that oil demand and oil supply shocks have different effects on real stock returns. Thus, the results suggest that oil demand shocks have a significant negative effect on stock returns only in Italy, Luxembourg, Portugal and the UK, while they have a positive significant effect on stock returns in Denmark and France. However, oil supply shocks have a significant negative effect on stock returns in Belgium, Finland, France, Germany, Italy, Portugal, Spain and the UK. Therefore, we could also conclude that oil supply shocks exert more negative effects on European stock returns than oil demand shocks. Again, Italy experiences the largest decrease in stock returns followed by Spain due to an oil supply shock. Some previous studies such as Apergis and Miller (2009), Kilian and Park (2009) and Güntner (2013) also observe a negative impact of oil supply shocks on real stock returns.

5. Concluding remarks The predictive power of oil price changes in predicting stock market returns is still an open question. In this paper we analyze whether or not oil price changes are able to predict stock market returns in 12 oil importing European countries using monthly data for the period 1973:02–2011:12 (except for Denmark where the sample period starts in 1974:01) by means of estimating several multivariate VECM. In order to estimate the impact of oil price shocks on stock returns, we include the following variables in the model: stock prices, industrial production indexes, short-term interest rates and different oil price specifications. The inclusion of these variables may help us understand the different channels through which oil prices could affect stock returns. Oil prices are measured in both world and national prices, but the definition of oil price shocks using national or world oil prices does not significantly change the main results of the analysis. One of the main contributions of the paper is the definition and identification of two alternative oil price specifications, oil demand and oil supply shocks, by means of using the behavior of both oil prices and oil production. The identification of these two variables is relevant assuming that stock returns may respond in different ways to supply and demand shocks. We find that the response of the European real stock returns to an oil price shock may differ greatly depending on the underlying causes of the oil price increase (or decrease). The main results obtained in the paper are the following. First, we find that oil price changes have a significant and negative impact on stock market returns in most of the countries (Belgium, Denmark, Finland, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain and the UK when using world oil prices, and Belgium, Finland, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain and the UK local oil prices). This negative effect is consistent with the results found in the previous literature and it is what we would expect for oilimporting economies such as the ones we analyze in this paper. Second, the results show that oil supply shocks tend to have a greater negative impact on stock market returns than oil demand shocks when using both world and local oil prices. Thus, oil supply shocks have a negative and significant impact on stock returns in most of the analyzed countries in both local (or national) and world oil prices, while oil demand shocks has a negative significant impact on stock market returns in only some countries (Germany, Italy, Luxembourg, Portugal and the UK) and a positive significant impact on stock returns in France and Denmark (when using national oil prices). Overall, the results suggest that the effect of oil price changes depend on the underlying cause of the change, and are in line with previous results in the literature (see, for example, Lippi and Nobili, 2012; Rapaport, 2013). According to these results, if oil prices increase because of a supply shock – such as the Iranian revolution in 1979 or the first Gulf war in 1990 – , the oil price change lowers economic activity in oil importing economies, as energy inputs are more expensive. However, if oil prices increase because of a demand shock – such as an increase in the oil demand by emerging economies in the last decade – ,economic activity in oil importing economies are subject to both a negative effect (due to higher energy costs) and a positive effect (due to an expected greater exports to those countries in a context of increasing world income and consumption). Thus, an oil price increase due to a supply shock is expected to have a more negative effect on stock returns than an oil price increase due to a demand shock. Following the same idea, Lippi and Nobili (2012), for example, build a model to explain the correlation between oil prices and US economic activity and obtain that the correlation between the US activity and oil prices varies with the type of oil shock. In the same line, Rapaport (2013) identifies oil supply and demand shocks by the sign of the correlation between oil price changes and stock market returns and obtains that not all oil price changes have the same effects in the growth rates of real GDP and aggregate dividends.

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

375

Fig. 3. Generalized impulse-response functions of real stock returns to national real oil shocks, oil demand shocks and oil supply shocks. Notes. Solid line represents the response function of stock returns to a shock in oil price change, oil demand shock or oil supply shock. Dotted lines are 95% confidence bounds.

376

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

Fig. 3. (continued).

J. Cunado, F. Perez de Gracia / Energy Economics 42 (2014) 365–377

In the light of the current results and given that oil price shocks have been previously formulated as a relevant variable for predicting macroeconomic and financial variables, further research on this topic is needed. For example, additional research could focus on the analysis of oil exporting countries. Second, this analysis could also be extended to the industry level instead of focusing only on aggregate stock indices. And finally, it could also explore the channels through which oil supply and oil demand shocks affect financial and macroeconomic variables.

References Apergis, N., Miller, S., 2009. Do structural oil-market shocks affect stock prices? Energy Econ. 31, 569–575. Bernanke, B.S., Gertler, M., Watson, M.W., 1997. Systematic monetary policy and the effects of oil shocks. Brook. Pap. Econ. Act. 1, 91–157. Chen, N., Roll, R., Ross, S.A., 1986. Economic forces and the stock market. J. Bus. 59, 383–403. Christiano, L.J., 1992. Searching for breaks in GNP. J. Bus. Econ. Stat. 10, 237–250. Ciner, C., 2001. Energy shocks and financial markets: non-linear linkages. Stud. Non-linear Dyn. Econom. 5, 203–212. Cunado, J., Perez de Gracia, F., 2003. Do oil price shocks matter? Evidence for some European countries. Energy Econ. 25, 137–154. Cunado, J., Perez de Gracia, F., 2005. Oil prices, economic activity and inflation: evidence for some Asian economies. Q. Rev. Econ. Financ. 45, 65–83. Dickey, D.A., Fuller, W.A., 1981. Likelihood ratio statistics for autoregressive times series with a unit root. Econometrica 49, 1057–1072. Driesprong, G., Jacobsen, B., Maat, B., 2008. Striking oil: another puzzle? J. Financ. Econ. 89, 307–327. Elyasiani, E., Mansur, I., Odusami, B., 2011. Oil price shocks and industry stock returns. Energy Econ. 33, 966–974. Engemann, K.M., Kliesen, K.L., Owyang, M.T., 2011. Do oil shocks drive business cycle? Some US and international evidence. Macroecon. Dyn. 15, 298–517. Güntner, J.H., 2013. How do international stock markets responds to oil demand and supply shocks? Macroecon. Dyn. (forthcoming). Hamilton, J.D., 1983. Oil and the macroeconomy since World War II. J. Polit. Econ. 91, 228–248. Hamilton, J.D., 1996. This is what happened to the oil price–macroeconomy relationship. J. Monet. Econ. 38, 215–220. Hamilton, J.D., 2003. What is an oil shock? J. Econ. 113, 363–398. Hamilton, J., 2008. Oil and the macroeconomy, In: Durlauf, Steven, Blume, Lawrence (Eds.), New Palgrave Dictionary of Economics, 2nd edition. Palgrave McMillan Ltd.

377

Hamilton, J., 2011. Nonlinearities and the macroeconomic effects of oil prices. Macroecon. Dyn. 15, 364–378. Huang, R.D., Masulis, R.W., Stoll, H.R., 1996. Energy shocks and financial markets. J. Futur. Mark. 16, 1–27. Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration with applications to demand for money. Oxf. Bull. Econ. Stat. 52, 169–210. Jones, C.M., Kaul, G., 1996. Oil and the stock market. J. Financ. 51, 463–491. Kilian, L., 2009. Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am. Econ. Rev. 99, 1053–1069. Kilian, L., Park, C., 2009. The impact of oil price shocks on the US stock market. Int. Econ. Rev. 50, 1267–1287. Lee, J., Strazicich, M.C., 2001. Break point estimation and spurious rejections with endogenous unit root tests. Oxf. Bull. Econ. Stat. 63, 535–558. Lee, K., Shawn, N., Ratti, R.A., 1995. Oil shocks and the macroeconomy: the role of price variability. Energy J. 16, 39–56. Lee, B.J., Yang, C.H., Huang, B.H., 2012. Oil price movements and stock market revisited: a case of sector stock price indexes in the G7 countries. Energy Econ. 34, 1284–1300. Lippi, F., Nobili, A., 2012. Oil and the macroeconomy: a quantitative structural analysis. J. Eur. Econ. Assoc. 10, 1059–1083. Miller, J.I., Ratti, R.A., 2009. Crude oil and stock markets: stability, instability and bubbles. Energy Econ. 31, 559–568. Mork, K.A., 1989. Oil and macroeconomy when prices go up and down: an extension of Hamilton's results. J. Polit. Econ. 97, 740–744. Narayan, P.K., Sharma, S.S., 2011. New evidence on oil price and firm returns. J. Bank. Financ. 35, 3253–3262. Park, J., Ratti, R.A., 2008. Oil price shocks and stock markets in the US and 13 European countries. Energy Econ. 30, 2587–2608. Peersman, G., Van Robays, I., 2009. Oil and the euro area economy. Econ. Policy 24, 603–651. Perron, P., 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57, 1346–1401. Perron, P., Vogelsang, T.J., 1992. Nonstationarity and level shifts with an application to purchasing power parity. J. Bus. Econ. Stat. 10, 301–320. Phillips, P.C.B., Perron, P., 1988. Testing for a unit root in time series regression. Biometrica 75, 335–346. Rapaport, A., 2013. Supply and demand shocks in the oil market and economy-wide shocks that affect asset prices. Chicago Booth Research Paper. Sadorsky, P., 1999. Oil price shocks and stock market activity. Energy Econ. 21, 449–469. Schmidt, P., Phillips, P.C.B., 1992. LM tests for a unit root in the presence of deterministic trends. Oxf. Bull. Econ. Stat. 54, 257–287. Scholtens, B., Yurtsever, C., 2012. Oil price shocks and European industries. Energy Econ. 34, 1187–1195. Sims, C.A., 1980. Macroeconomics and reality. Econometrica 48, 1–48. Zivot, E., Andrews, D.W.K., 1992. Further evidence on the great crash, the oil price shock and the unit root hypothesis. J. Bus. Econ. Stat. 10, 251–270.