Does crude oil move stock markets in Europe? A sector investigation

Economic Modelling 28 (2011) 1716–1725

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Economic Modelling j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c m o d

Does crude oil move stock markets in Europe? A sector investigation☆ Mohamed El Hedi Arouri ⁎ LEO-Université d'Orléans & EDHEC Business School, Rue de Blois, BP 267-39-45067 Orléans cedex 2, France

a r t i c l e

i n f o

Article history: Accepted 28 February 2011 JEL classification: G12 F3 Q43

a b s t r a c t The aim of this article is to investigate the responses of European sector stock markets to oil price changes. We use linear and asymmetric models and study the association of oil and stock prices. Our findings suggest that the strength of this association varies greatly across sectors. Moreover, for some sectors we find strong evidence of asymmetry in the reaction of stock returns to changes in the price of oil. © 2011 Elsevier B.V. All rights reserved.

Keywords: Oil prices Short-term analysis Sector stock markets

1. Introduction In the literature on financial markets, an abundance of theoretical and empirical works has focused on asset pricing (Fama and French, 1993, 2004; Balvers and Huang, 2009). However, there is no consensus about the nature and number of factors that affect stock prices. As oil prices have swung wildly in recent years, it is now quite opportune to add to the research on the impact of these prices on stock market returns. There are several channels through which oil price fluctuations may affect stock prices. Theoretically, the value of a stock equals the discounted sum of expected future cash flows. These discounted cash flows reflect economic conditions (inflation, interest rates, production costs, income, economic growth, investor and consumer confidence, and so on) and are then affected by macroeconomic events that may be influenced by oil price changes. Indeed, oil price fluctuations may have effects on the basic production input availability and investment costs (supply-side effects), on the terms of trade and wealth transfer from oil consumers to oil producers, on firms' production structures and unemployment, on monetary policies, on interest rates and inflation, and on consumption opportunities, costs and consumer demand and confidence (demand-side effects), see Hamilton (1983) and Jones et al. (2004). The literature contains a large body of work on the links between oil prices and macroeconomic variables. The findings of these studies are not homogenous, but numerous studies (Hamilton, 2003; Cuñado ☆ We thank anonymous reviewers for helpful comments and suggestions on an earlier version of the paper. The usual disclaimer applies. ⁎ Tel.: +33 2 38 49 24 10; fax: +33 2 38 41 73 80. E-mail address: [email protected]. 0264-9993/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.econmod.2011.02.039

and Pérez de Gracia, 2005; Balaz and Londarev, 2006; Gronwald, 2008; Cologni and Manera, 2008; Kilian, 2008; Álvarez et al., 2011) have reported negative effects of oil price fluctuations on economic activity for several net oil-importing developed and emerging countries. In contrast, the work done on the relationship between oil price variations and stock markets is relatively scant, probably because there is no unequivocal evidence of a relationship between oil price changes and economic activity. Jones and Kaul (1996) investigate the reaction of stock returns in four developed markets (Canada, Japan, the UK, and the US) to oil price fluctuations on the basis of the standard cash flow dividend valuation model. They find negative links between oil price changes and stock returns in the US and Canada. The results for Japan and the UK were inconclusive. Using an unrestricted vector autoregressive (VAR) model, Huang et al. (1996) find no evidence of a short-term relationship between oil prices and the S&P500 market index. Sadorsky (1999), however, applies an unrestricted VAR model with GARCH effects to American monthly data and shows a negative short-term effect of oilprice volatility on the aggregate stock returns. Park and Ratti (2008) show that oil prices have a negative impact on stock returns in the US and in twelve European countries; stock markets in Norway, an oilexporting country, on the other hand, respond positively to rises in the price of oil. More recently, Apergis and Miller (2009) have examined whether structural oil-market shocks affect stock prices in eight developed countries. They find that international stock market returns do not respond significantly to oil price shocks. Very few studies have looked at the impact of oil price changes on the stocks of individual sectors. In addition, most of these studies are country specific and thus do not provide a global perspective. For instance, Sadorsky (2001) and Boyer and Filion (2007) show that

M.E.H. Arouri / Economic Modelling 28 (2011) 1716–1725

increases in the price of oil affect the stock returns of Canadian oil and gas companies positively. El-Sharif et al. (2005) reach an equivalent conclusion for oil and gas returns in the UK. However, the authors show that non-oil and gas sectors are weakly linked to changes in the price of oil. More recently, Nandha and Faff (2008) study the shortterm link between oil prices and thirty-five DataStream global industries and show that price rises have a negative impact on all but the oil and gas industries. Finally, Nandha and Brooks (2009) look into the reaction of the transport sector to oil prices in thirty-eight countries and find that, in developed economies, oil prices have a negative influence on the returns to the sector. However, there appears to be no evidence of a significant role for oil in Asian and Latin American countries. Taken together, the results from the works on the relationships between oil prices and sector stock returns differ from country to country and from sector to sector. Our article attempts to broaden understanding of the short-term relationship between oil prices and stock prices at the disaggregated sector level in Europe using different econometric techniques. Studying the effects of oil price fluctuations sector by sector is important for several reasons. First, any market-wide consequence may mask the performance, not necessarily uniform, of any one sector. Sector sensitivities to changes in the price of oil may be asymmetric; some sectors may be more severely affected by these changes than others. Sector sensitivities depend on whether oil is an input or an output, on the indirect effect of oil prices on the sector, on the degree of competition and concentration, and on the capacity of the sector to transfer oil price shocks to its consumers and thus to minimise the impact of these shocks on its profitability. Second, the industrial base varies from one European market to another. Large, mature markets such as France and Germany are more diversified, whereas small markets such as Switzerland usually concentrate on a few industries. Thus, the results of studies—such as those by Park and Ratti (2008) and Apergis and Miller (2009)—based on national stock market indices should be examined with caution. Indeed, it is interesting to know whether sector indices rather than national indices are sensitive to oil price fluctuations. Finally, from the point of view of portfolio management, identifying the heterogeneity of sector sensitivities to oil shows that there are sectors that can still provide a means of diversification during large swings in oil prices. The rest of the paper is organized as follows. Section 2 presents the data and some preliminary analysis. The methodology and empirical results are presented in Section 3. Section 4 discusses our main findings. Summary conclusions and policy implications are provided in Section 5. 2. Data and preliminary analysis Our objective is to investigate the short-term links between oil prices and sector stock returns in Europe. We use weekly stock market indices over the period from 01/01/1998 to 06/30/2010. Over this sample period, the relationship between oil prices and economic activity was ambiguous. Increases in oil prices were indicative of higher production costs and inflation pressure and, at the same time, synonymous with greater expected economic growth and greater consumer and investor confidence. Weekly data may adequately capture the interaction of oil and stock prices. We study the DJ Stoxx 600 and twelve European sector indices: Automobile & Parts, Financials, Food & Beverages, Oil & Gas, Health Care, Industrials, Basic Materials, Personal & Household Goods, Consumer Services, Technology, Telecommunications, and Utilities. Stock market data are obtained from the DataStream database. Introduced in 1998, the Dow Jones Stoxx 600 sector indices attempt to represent the largest European companies in each of the most important industries and currently covers Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the

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United Kingdom. The sector indices offer an alternative view of the performance of the European stock markets. The main industries are Automobile & Parts (automobiles, auto parts, and tires), Financials (banks, insurance, reinsurance, real estate, and financial services), Food & Beverages (beverages and food producers), Oil & Gas (oil and gas producers, oil equipment and services, distribution, and alternative energy), Health Care (health care equipment and services, pharmaceuticals, and biotechnology), Industrials (construction and materials, industrial goods and services), Basic Materials (chemicals and basic resources), Personal & Household Goods (household goods, home construction, leisure goods, and personal goods and tobacco), Consumer Services (retail, media, travel, and leisure), Technology (software and computer services and technology hardware and equipment), Telecommunications (fixed-line and mobile telecommunications), and Utilities (electricity, gas, water, and multi-utilities). Each sector index represents a capitalization-weighted portfolio of the largest European companies in this sector. For oil, we use the weekly Brent oil crude price obtained from the Energy Information Administration (EIA). We express Brent oil prices in euros and use exchange rates from DataStream. To determine the order of integration of our series, we apply three standard unit root tests: Augmented Dickey–Fuller (ADF), Phillips– Perron (PP), and Kwiatkowski et al. (KPSS) tests. The ADF and PP tests are based on the null hypothesis of a unit root, while the KPSS test considers the null of no unit root. Results are shown in Table 1. All the price series (series in levels) appear to be integrated of order one, which is a standard result in the literature for such series. As expected, the return series (first log-difference series) are stationary. Due to the potential structural breaks induced for instance by the financial crisis, we also implement the Zivot–Andrews (Z&A) test to check for unit root.1 Similar to the ADF and PP tests, the Z&A test has, as its null hypothesis, that the dynamics of the respective series are characterised by a unit root. However, the Z&A test makes allowance for the possible existence of a one-off structural change under the alternative hypothesis. This is an attractive feature of the test since Zivot and Andrews (1992) have demonstrated that the ADF and PP tests have low power in the presence of a structural break. The Z&A test also has the advantage of not requiring the a priori specification of the possible timing of a structural break. The results of the implementation of the Z&A unit root test reported in Table 1 confirm those of the previous tests and show that the price series are integrated of order one while the return series are stationary. Descriptive statistics for return series (first log-difference series) are summarized in Table 2. Oil prices have, on average, increased more than European stock prices over our sample period. However, oil prices are more volatile than the sector stock prices we study, reflecting the great instability of oil prices in recent years. Technology stocks have the highest sector volatility. This finding is not surprising. Indeed, it is argued that Technology firm values are very volatile and often very offensive (Pierdzioch and Schertler, 2008). Skewness is negative in most cases and the Jarque–Bera test statistic (JB) strongly rejects the hypothesis of normality. Correlations between oil price changes and European sector returns are weak on average and, surprisingly, positive, except for two sectors: Food & Beverages and Health Care. However, for these two sectors correlations are not statistically different from zero. This suggests that, over the last decade, increases in the price of oil were seen as indicative of higher expected economic growth and earnings. The sector Oil & Gas has the highest correlation with oil (36%) and is followed by Basic Materials (19%). This result is expected. Indeed, a particularity of these two sectors is that most of their value is driven by the price of the commodity they produce (oil, metals, etc.). Correlations between the European market index (DJ Stoxx 600) and 1 We are very grateful to the anonymous referee for very interesting comments and valuable suggestions that have greatly motivated this part of the paper.

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sector returns are, on average, high. Financials has the highest correlation (92%) and Food & Beverages the lowest (57%). 3. Empirical analysis We investigate the short-term relationships between oil prices and sector stock market in Europe for the recent years of turmoil based on return series.2 We begin our analysis by estimating a multifactor asset pricing model to investigate the sensitivities of the sector stock returns to oil prices and global European market changes. We then test for non-linearity in the relationship between oil and stock price changes. Finally, we perform the Granger causality test to examine their short-term causal links. 3.1. Sector returns, oil price changes, and market sensitivities The multifactor model we use to examine whether the European sector stock returns are sensitive to oil price and global European market changes takes the following form [Model (1)]3: rit = a + b × roilt + c × rdjot + d × Dt + εit εit →Νð0; hit Þ 2 hit

q

= α + ∑ βk × k=1

2 εi;t−k

ð1Þ

p

+ ∑ γl × l=1

2 hi;t−l

where: rit roilt Dt rdjot

is the weekly stock return in sector i (first log-difference of sector market index); is the oil price return (first log-difference of oil price); is a dummy variable equals to 1 during the current international financial crisis (August 2007–June 2010) and 0 before; represents the global European market returns (first logdifference of DJ Stoxx 600 index) filtered by the oil price returns. More precisely, rdjot is the residual of the OLS regression of the European market returns (Rdjt) on the oil price changes (roilt): 0

rdjt = α + β roilt + rdjt

The return innovations (εit) are assumed to be normally distributed with zero mean and conditional variance governed by a standard GARCH(q,p) process.4 The quasi-maximum likelihood (QML) method is used to estimate the model. Table 3 contains parameter estimates. The coefficients relating the sector return series to the European filtered returns (coefficients c) are highly significant for all sectors. They vary from 0.50 (a defensive sector) for Food & Beverages to 1.37 (an offensive sector) for Technology. The coefficient d is significantly negative in most cases, reflecting the negative impact of the 2007–2010 international financial crisis on stock returns. More interestingly, the coefficients relating the return series to oil price changes (coefficients b) are significant in nine of twelve cases, indicating significant short-term 2

Note that given the I(1) property of all price series, cointegration tests have been applied to series in level and show that cointegration is found for only three sectors: Automobile & Parts, Food & Beverages, and Oil & Gas. As the aim of the paper is to deal with the short-term links between oil and stock markets in Europe, the long-term relationship investigation results are not reported here, but available upon request from the corresponding author. 3 The suitability of two-factor “market-and-oil” pricing models similar to the model we use in this paper has been studied in several papers; see Faff and Brailsford (2000). We have also used larger models including lagged returns, interest rates, inflation, and other external variables (money supply and industrial production) and obtained similar results. 4 Optimal lags in our different models are determined according to information criteria.

effects of oil price fluctuations on European sector stock prices. Oil price increases negatively affect sector returns in six cases (Financials, Food & Beverages, Health Care, Personal & Household Goods, Technology, and Telecommunications) and positively in three cases (Oil & Gas, Basic Materials, and Consumer Services). Finally, our results suggest that there is no relationship between oil price changes and stock returns for two European sectors (Industrials, and Utilities) and that there is a weak negative link relationship for the Automobile & Parts industry. The model we estimated seems to fit the data satisfactorily. The ARCH and GARCH coefficients are significant. We also observe that, in most cases, conditional volatility does not change very sharply, as the ARCH coefficients are relatively small. By contrast, it tends to fluctuate gradually over time because of the large GARCH coefficients. Moreover, the estimated coefficients γ and β satisfy the stationary conditions. Finally, we test for the absence of autocorrelation and ARCH effects in the residual series. The results indicate that the model specification we use is flexible enough to capture the dynamics of returns. To sum up, our analysis shows strong links between oil price changes and most European sector returns over the period under consideration. The sign and intensity of these links differ from one sector to another. In the following section, we test for asymmetries in the responses of European sector returns to oil price shocks. 3.2. Asymmetric reaction to oil shocks Some recent papers have shown that the link between oil and economic activities is not entirely linear and that negative oil price shocks (price increases) tend to have a greater impact on economic growth than positive shocks do (Hamilton, 2003; Zhang, 2008; Lardic and Mignon, 2008; Cologni and Manera, 2009). Authors have focused mainly on three possible explanations of the asymmetric responses of macroeconomic variables to oil price shocks: counter-inflationary monetary policy responses to oil price increases, sector shock transmission mechanisms, and investment uncertainty. So, we should also expect oil prices to affect stock markets in an asymmetric fashion. To test for asymmetry in the reaction of European sector returns to oil price shocks, we follow Park and Ratti (2008) and estimate the three leading non-linear approaches to the effects of oil price changes on macroeconomic and financial variables: asymmetric specification (Mork, 1989; Hamilton, 2003), scaled specification (Lee et al., 1995; Cologni and Manera, 2009), and net specification (Hamilton, 1996; Jiménez-Rodríguez and Sánchez, 2005). 3.2.1. Asymmetric specification In this specification, increases and decreases in the price of oil are considered separate variables according to Eq. (2) [Model (2)]. The intuition is that oil price increases and decreases may have different effects on stock returns (Mork, 1989; Hamilton, 2003). That is: − − o rit = a + bþ × roilþ t + b × roilt + c × rdjt + d × Dt + εit

εit →Νð0; hit Þ h2it

q

= α + ∑ βk × k=1

2 εi;t−k

p

+ ∑ γl × l=1

ð2Þ 2 hi;t−l

− where roil+ t and roilt are positive and negative oil price returns respectively:

þ

−

roilt = max ðroilt ; 0Þ; and roilt = min ðroilt ; 0Þ: Thus, b+ and b− are the coefficients corresponding to increases and decreases in oil price respectively. There is no asymmetry if b+ and b− are not statistically different from each other. We then test for this null hypothesis: b+ = b−. Moreover, we test for the hypothesis of non-asymmetry and null sensitivities to oil price increases and decreases: b+ = b− = 0.

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3.2.2. Scaled specification This specification takes into account the volatility of oil prices. The basic intuition is that oil price increases after a long period of price stability may have more dramatic effects on stock returns than those that are merely corrections to greater oil price decreases during the previous weeks. We follow Lee et al. (1995) and Jiménez-Rodríguez and Sánchez (2005) and take an AR(m)-GARCH(q,p) model to compute the − scaled oil price increase (Sop+ t ) and the scaled oil price decrease (Sopt ): m

roilt = γ0 + ∑ γi × roilt−i + ξt i=1

q

2

2

p

2

ht = λ0 + ∑ ϕk × ξt−k + ∑ φl × ht−l k=1

l=1

and   qffiffiffiffiffi qffiffiffiffiffi þ ˆ = h2 ; and Sop− = min 0; ξ ˆ = h2 : Sopt = max 0; ξ t t t t t We then estimate the model given by Eq. (3) [Model (3)]: − − o rit = a + bþ × Sopþ t + b × Sopt + c × rdjt + d × Dt + εit

εit →Νð0; hit Þ q

2

p

ð3Þ 2

h2it = α + ∑ βk × εi;t−k + ∑ γl × hi;t−l : k=1

l=1

Again, there is no asymmetry if b+ and b− are not statistically different from each other (b+ = b−) and the hypothesis of nonasymmetry and null sensitivities of stock returns to oil price increases and decreases implies that b+ = b− = 0. 3.2.3. Net specification This specification is a different non-linear transformation of oil price changes. The intuition is that the most relevant oil price changes may be the difference between the current oil price and the maximum value of oil prices in previous periods. Thus, we use as an explanatory variable the net oil price increase (Nop+ t ), the amount by which oil prices in log in period t, pt exceed the maximum value over the four previous weeks; and 0 otherwise: þ

Nopt = maxð0; pt − maxðpt−1 ; pt−2 ; pt−3 ; pt−4 ÞÞ: We then estimate the model given by Eq. (4) [Model (4)]: o rit = a + b × Nopþ t + c × rdjt + d × Dt + εit

εit →Νð0; hit Þ 2 hit

q

= α + ∑ βk × k=1

2 εi;t−k

p

+ ∑ γl × l=1

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The net specification results show that the net oil price increases have significant impacts on sector stock returns in Europe in eight cases. The signs of these effects are similar to those reported in Table 3. More interestingly, our findings suggest that net oil price increases affect stock returns in the Automobile & Parts industry negatively. 3.3. Causality tests To further examine the relationships between oil price changes and sector stock returns in Europe, we precede to Granger causality tests on the return series. Results are reported in Table 5. The results show that there is a bidirectional causality between oil price changes and DJ Stoxx returns. Indeed, DJ Stoxx returns Grangercause changes in oil prices at the 10% level and oil price shocks Granger-cause changes in DJ Stoxx returns at the 1% level. Similar results are obtained for Automobile & Parts, Food & Beverages, Oil & Gas, Basic Materials, Personal & Household Goods, Consumer Services, and Utilities. However, there are only unidirectional Granger causalities from oil to stock returns for the Financials, Health Care, Industrials, Technology, and Telecommunications industries. Causality tests based on our non-linear definitions of oil price changes (the asymmetric, scaled, and net specifications) show very similar results: there are significant unidirectional or bidirectional causalities between European sector returns and oil price changes. However, in some cases the intensity of the obtained causality changes significantly, depending on how oil price changes are defined. Taken together, our causality test results corroborate our previous findings and suggest significant short-term interaction of oil prices and most sector stock prices in Europe. Our causality test results are interesting for at least two reasons. First, they imply some predictability in oil and European stock price dynamics. Second, in contrast to the findings of several studies, which assume exogeneity of oil prices with respect to macroeconomic and financial variables, our findings suggest that there may be a reverse relationship. Indeed, changes in some European sector returns do significantly affect world oil prices (Ewing and Thompson, 2007; Lescaroux and Mignon, 2008). In short, our empirical results show significant short-term relationships between most sector returns in Europe and oil price changes. However, there is strong evidence of asymmetry in the reaction of stock returns in some sectors to oil price shocks, and in all cases the value and sign of the sensitivities of stock returns to oil price changes vary significantly across sectors. In Appendix B, we present the results of some robustness checks that show that our empirical findings are robust. In the following section, we discuss our results. 4. Discussion of results

ð4Þ 2 hi;t−l :

Results of estimation of our non-linear models are summarized in Table 4. The asymmetric specification [Model (2)] and the scaled specification [Model (3)] lead to very similar results. Wald tests show that the hypothesis b+ = b− = 0 is rejected mostly at the 1% level in ten cases, which confirms the significance of oil price shocks as a factor affecting sector returns in Europe. These findings are consistent with those reported in Table 3. The only exception is for Utilities, for which we found no significant reaction to oil shocks using symmetric asset pricing model. Indeed, our findings show that stock returns in this sector react negatively to oil price increases and do not react significantly to oil price decreases. Wald tests confirm this result and show that the hypothesis b+ = b− is rejected for Utilities and for three other industries (Health Care, Basic Materials, and Personal & Household Goods). For these industries, reactions to oil price changes are asymmetric.

Theoretically, oil price changes affect stock prices through their effects on both expected earnings and discount rate. In the last decade, researchers and market participants have attempted to find a practical framework that identifies how oil prices affect stock prices, but they have not come to a consensus. In this article, we contribute to the literature by investigating the short-term links between Brent oil prices and sector stock prices in Europe. The results for the Automobile & Parts sector show that oil price increases affect stock prices in this sector negatively. These results are not unexpected: higher oil prices are associated with lower automobile manufacturer returns. Indeed, increases in oil prices may lead drivers to drive less or to demand more efficient vehicles (demand-side effects). Moreover, oil price increases have a strong direct impact on the Automobile & Parts industry by affecting production costs and firm profitability (supply-side effects). However, we show that the negative effect of oil price increases on Automobile & Parts stock returns is only weak. This finding is consistent with effective short-term risk management strategies with respect to oil concerns, very frequent in the Automobile & Parts industry. The weak link between oil price

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changes and Automobile & Parts stock returns can also be explained partly by European legislation, which encourages more fuel-efficient vehicles, as well as by government aid to the automobile industry during crises (Cameron and Schulenburg, 2009). Our findings suggest that there is a strong negative relationship between oil price changes and stock returns in Financials. Increases in the price of oil significantly affect stocks of some non-oil-intensive industries, such as Financials, mainly through demand-side effects: price increases affect consumer and investor confidence and demand for financial products. Oil price increases also have a negative effect on stock returns in the Food & Beverages sector. This finding is unsurprising: food and other commodity markets are strongly influenced by oil and petroleumproduct markets. Oil and oil-related products are used in all phases of food production. Increases in the prices of oil directly affect food production costs as well as transportation and commercialization costs and so reduce the profitability of Food & Beverages firms. Alghalith (2010) shows empirically that a higher oil price increases food prices and that an increase in the oil supply reduces food prices. We should also mention that the response of the Food & Beverages to oil price changes depends not only on the possible increases in marginal cost (cost-side effects), but also on the possible shifts in consumer expenditures (demand-side effects). Indeed, the possibility of a future reduction in demand stemming from cutbacks in consumer expenditures alone is an important factor in determining industry sensitivities to oil price changes. Consequently, exuberance in oil and energy markets has often implied agricultural and food crises and crashes. Our results for the Oil & Gas sector show the existence of strong positive links between oil price changes and stock returns. This finding is expected and confirms those of previous work (Boyer and Filion, 2007; Mohanty et al., 2010). Indeed, a particularity of oil and gas firms is that their value is driven by oil prices. Lower oil prices mean lower profit margins and reduced capital expenditures, while higher oil prices mean higher profit margins and, possibly, increased capital expenditures. So an increase in oil prices raises the value of stock prices of oil and gas firms, which explains the positive responses of Oil & Gas stock returns to oil price changes. On Health Care stocks, oil price changes have a negative, asymmetric, short-term effect. Increased volatility in the oil markets is often seen as representing greater uncertainty in the aggregate economy. Moreover, swings in oil prices have asymmetric effects on the economy (Mork, 1989; Hamilton, 2003). In this sense, our finding provides evidence of the extent to which the health care sector is tied to overall uncertainty in the economy; accordingly, it appears that firm performance in the health care sector is asymmetrically dependent on oil prices. Decreases (increases) in the price of oil lead to decreases (increases) in production, manufacturing, and transportation costs, while investment (including R&D) in health care firms as well as consumer demand addressed to these firms may increase (decrease) (Malik and Ewing, 2009). The results for the Industrials sector suggest only a weak causality from oil to stock returns. These results are somewhat surprising since firms in the Industrials sector are major consumers of oil and oilrelated products. However, given the long and established relationship between oil use and Industrials, it is likely that these firms have developed strategies for effective hedges against the effects of oil price changes and that these firms manage their dependence on oil properly (Malik and Ewing, 2009). For Basic Materials, we report an asymmetric positive short-term reaction of stock returns to oil price changes. This sector includes the mining and refining of basic and precious metals, chemical producers, and forestry products and has similar characteristics to the Oil & Gas sector in terms of the nature of the underlying extractive activity. An increase in the price of oil may raise demand for these products and make Basic Materials firms more profitable. Effective hedging strategies may limit the effects of falls in the price of oil.

The Personal & Household Goods sector comprises products that include household goods, home construction, leisure goods, and personal goods and tobacco. Increases in prices of oil affect both demand for and supply of these products negatively. However, firms in Personal & Household Goods sector appear to be able to pass on a part of the effects of oil price increases to their consumers and thus to minimise their impact on their profitability. Our results confirm this view and show a negative asymmetric relationship between stock returns in this sector and oil price changes. Oil prices have both a direct and an indirect impact on the Consumer Services sector, given that energy expenditures by households and firms compete with purchases of retail products, entertainment, travel, and so on. Thus, it is expected that increases in oil prices negatively impact demand for Consumer Services. However, our empirical results show a positive short-term effect of oil price changes on stock returns. Hence, it seems more appropriate to interpret our results as evidence for the reverse causality put forward recently by Barsky and Killian (2004) and Killian (2009): the growth of Consumer Services stock indices may be associated with an increase in the oil price because of growing demand and consumer confidence. For the Technology and Telecommunications sectors, we show negative short-term links between stock returns and oil price changes. Investors in technology have a wide array of products to choose to invest in, including oil and new energy products. Higher oil prices and energy costs lead to greater demand uncertainty associated with Technology and Telecommunications investments. Increases in oil prices reduce the performance of Technology and Telecommunications firms and make their stock returns riskier (Henriques and Sadorsky, 2008; Pierdzioch and Schertler, 2008). Finally, the Utilities sector contains companies such as electric and water firms and integrated providers. These firms use oil and oil-related products as an input. Like firms in the Industrials sector, however, those in the Utilities sector make frequent use of futures and other derivatives to hedge against the effects of oil price increases on their profitability. The result is that oil price changes have a weak negative effect on Utilities stock returns, but increases and decreases have asymmetric effects. To sum up, we show that not all sectors are equally dependent on oil and that, in some cases, oil price increases and decreases affect stock prices asymmetrically. More precisely, our findings suggest the existence of a dual asymmetry in the relationship between oil prices and stock prices: the responses of stock prices to oil price changes depend both on the sector and on the sign of oil price changes. Indeed, we show empirically that, by affecting production costs and firm profitability (supply-side effects), oil price changes have a strong direct impact on the stocks of oil-intensive industries such as Food & Beverages and Oil & Gas. We show also that oil price changes significantly affect stocks of some non-oil-intensive industries such as Financials and Technology, mainly through demand-side effects: increases in the price of oil affect consumer confidence and demand for most products. However, our findings suggest that oil price increases and decreases have asymmetric effects on stock prices in some sectors, for instance Basic Materials and Utilities. We offer two possible explanations. First, firms in these sectors are able to pass the effects of oil price shocks on to their consumers and thus to minimise the impact of these shocks on their profitability. Second, these firms are more likely to hedge against the increases in oil prices than firms in other sectors. Thus, our findings are consistent with those of previous works on the asymmetric responses of economic growth to oil price changes and suggest that transmission channels from oil prices to financial variables partly reflect transmission channels from oil prices to the real economy (Mork, 1989; Davis and Haltiwanger, 2001; Lee and Ni, 2002; Cologni and Manera, 2009). 5. Conclusion and policy implications In this article, we examine the responses of stock markets in Europe to oil price changes. Unlike other empirical investigations, which have

M.E.H. Arouri / Economic Modelling 28 (2011) 1716–1725

focused largely on broad market indices (national and/or regional indices), ours adds to knowledge of the relationship between oil prices and the stock markets in Europe by testing for the short-term links in the aggregate as well as sector by sector. Our results show strong significant links between oil price changes and stock markets for most European sectors. However, the nature and sensitivity of the reaction of stock returns to oil price shocks change considerably across sectors. Moreover, for some sectors we find strong evidence of asymmetry in the reaction of stock returns to changes in the price of oil. Our findings should be of interest to researchers, regulators, and market participants. First, traders who are interested in investing in oil-sensitive stocks in Europe may, when oil prices are expected to remain high, select stocks from sectors, such as Oil & Gas, with high positive sensitivity to oil prices; on the contrary, when they expect falls in the price of oil they may select sectors, such as Food & Beverages, with negative sensitivity. They can also use oil-related derivatives. Thus, our results can be used to build profitable investment strategies. Second, that sectors in Europe have different sensitivities to oil price changes means great risk diversification

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possibilities across industries in Europe. Selecting portfolios across rather than within sectors would be more efficient. Finally, investors and portfolio managers should rebalance their portfolios in keeping with their views of the sign of coming changes in oil prices, and our findings suggest that diversification can be achieved across sectors in both oil price increases and decreases. There are several possible extensions of this article. First, evidence from international equity markets and other regions should be produced to examine the robustness of the findings. Second, the methods used in this article could serve to examine the effects of other energy products, such as natural gas. Third, it could be of interest to use panel unit root tests and dynamic panel methods to study the questions addressed by this article. Finally, our unit root tests show that all oil and stock price series we study in this paper are integrated of order one (I(1)). Moreover, our traditional cointegration analysis shows that there is cointegration only for three sectors. However, the long-term link between oil and stock market prices could be nonlinear. Thus, investigating the asymmetric cointegrating relationship between the two variables would also be of great interest.

Appendix A. Empirical results A.1. Data and preliminary analysis Table 1 Unit root tests. Levels

Oil Brent DJ Stoxx Automobile & Parts Financials Food & Beverages Oil & Gas Health Care Industrials Basic Materials Personal & Household Goods Consumer Services Technology Telecommunications Utilities

First difference

ADF

PP

KPSS

Z&A

ADF

PP

KPSS

Z&A

0.10a − 0.45a 0.12a − 0.45a 0.45a − 0.01a 0.78a − 0.45a 0.53a 0.59a − 0.65a 0.01a − 0.52a 0.46a

− 0.31a − 0.33a − 0.94b −-0.55c − 1.22c − 1.23b − 1.11b − 0.36a 0.11a 0.56a 1.05c 1.01b − 1.10b 0.63a

0.73⁎c 0.35⁎c 0.69⁎c 1.27⁎c 0.89⁎c 0.96⁎c 0.50⁎c 0.68⁎c 1.11⁎c 1.14⁎c 0.95⁎c 0.79⁎c 1.12⁎c 1.33⁎c

− 3.065 − 2.812 − 3.652 − 3.270 − 3.387 − 3.087 − 3.174 − 2.697 − 2.944 − 3.170 − 2.611 − 3.591 − 3.660 − 2.562

− 12.56⁎a − 8.12⁎a − 15.23⁎a − 7.23⁎a − 15.12⁎a − 8.12⁎a − 15.25⁎a − 7.25⁎a − 21.15⁎a − 15.25⁎a − 5.25⁎a − 7.58⁎a − 11.02⁎a 12.23⁎a

− 9.25⁎a − 25.00⁎a − 27.46⁎a − 18.41⁎b 11.03⁎a − 1801⁎a − 11.15⁎a − 23.25⁎a − 25.02⁎a − 18.14⁎a − 21.63⁎a − 20.55⁎a − 18.09⁎a 18.12⁎a

0.11⁎⁎b 0.32b 0.12⁎⁎⁎b 0.22b 0.09b 0.12b 0.21b 0.17b 0.15b 0.12b 0.20b 0.31b 0.28b 0.15b

− 12.855⁎ − 14.969⁎ − 16.539⁎ − 15.162⁎ − 15.028⁎ − 17.394⁎ − 15.914⁎ − 13.732⁎ − 15.380⁎ − 14.461⁎ − 14.396⁎ − 13.365⁎ − 15.133⁎ − 15.651⁎

Notes: Oil price and market indices are in natural logs. ADF is the augmented Dickey–Fuller test, PP the Phillips–Perron test, KPSS the Kwiatkowski–Phillips–Schmidt–Shin test and Z&A is the Zivot–Andrews test. (a) model without constant or deterministic trend, (b) model with constant without deterministic trend, and (c) model with constant and deterministic trend. ⁎ Denotes rejection of the null hypothesis at 1%. ⁎⁎ Denotes rejection of the null hypothesis at 5%. ⁎⁎⁎ Denotes rejection of the null hypothesis at 10%.

Table 2 Descriptive statistics of return series.

Oil Brent DJ Stoxx Automobile & Parts Financials Food & Beverages Oil & Gas Health Care Industrials Basic Materials Personal & Household Goods Consumer Services Technology Telecommunications Utilities

Mean

Std. errors

Skewness

Kurtosis

JB

0.25 0.01 0.02 − 0.04 0.08 0.04 0.02 0.04 0.14 0.08 − 0.05 − 0.07 0.00 0.07

4.77 2.67 4.50 3.69 2.20 3.32 2.55 3.31 3.64 2.58 2.64 4.93 3.53 2.37

− 0.40 − 0.59 0.34 − 0.62 − 0.48 − 0.39 − 0.09 − 0.52 − 0.72 − 0.70 − 0.45 − 0.24 − 0.08 − 1.37

4.80 5.81 15.39 7.60 5.55 4.77 5.04 6.61 6.90 6.28 5.16 4.78 4.11 10.62

101.93 543.77 4019.5 593.22 194.62 98.94 110.33 368.29 452.31 333.42 143.80 89.28 33.13 1713.3

Notes: JB is the Jarque–Bera test for normality based on excess skewness and kurtosis. For correlations. ⁎ Denotes that they are significant at 1% level. ⁎⁎ Denotes that they are significant at 5% level. ⁎⁎⁎ Denotes that they are significant at 10% level.

Correlation with Oil 1.00 0.16⁎ 0.06 0.11⁎ − 0.05 0.36⁎ − 0.04 0.15⁎ 0.19⁎ 0.08⁎⁎⁎ 0.07⁎⁎⁎ 0.10⁎⁎ 0.01 0.13⁎

Correlation with DJ Stoxx 0.16⁎ 1.00 0.69⁎ 0.91⁎ 0.57⁎ 0.67⁎ 0.61⁎ 0.91⁎ 0.79⁎ 0.87⁎ 0.88⁎ 0.80⁎ 0.71⁎ 0.73⁎

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M.E.H. Arouri / Economic Modelling 28 (2011) 1716–1725

A.2. Short-term interaction analysis

Table 3 Estimation results—linear model Model (1). a Automobile & Parts

b

0.150 (0.265) 0.096 (0.086) 0.189 (0.150) − 0.189⁎⁎ (0.097) 0.109 (0.089) 0.167⁎⁎⁎

Financials Food & Beverages Oil & Gas Health Care Industrials

(0.101) 0.123⁎⁎⁎

Basic Materials Personal & Household Goods Consumer Services Technology Telecommunications Utilities

(0.071) 0.104⁎⁎ (0.044) − 0.031 (0.045) − 0.102 (0.101) 0.062 (0.093) 0.115⁎⁎⁎ (0.062)

c

α

d

γ1

β1

− 0.031 (0.020) − 0.028⁎

1.253⁎

− 0.105⁎⁎⁎

0.041⁎

0.184⁎

0.759⁎

(0.035) 1.117⁎

(0.059) − 0.133⁎⁎

(0.012) 0.011⁎⁎

(0.032) 0.118⁎

(0.008) − 0.055⁎

(0.015) 0.502⁎

(0.068) − 0.109⁎⁎

(0.006) 0.693⁎

(0.020) 0.141⁎

(0.038) 0.0851⁎ (0.015) –

(0.011) 0.181⁎ (0.016) − 0.078⁎ (0.013) 0.005 (0.010) 0.041⁎

(0.018) 0.823⁎ (0.032) 0.590⁎ (0.023) 1.103⁎

(0.055) 0.122 (0.209) − 0.351⁎⁎ (0.186) − 0.125⁎⁎

(0.114) 0.027⁎⁎ (0.013) 0.061⁎⁎ (0.024) 0.022⁎⁎

(0.040) 0.086⁎ (0.020) 0.083⁎ (0.019) 0.108⁎

0.899⁎ (0.022) 0.901⁎ (0.021) 0.881⁎

(0.015) 1.114⁎

(0.062) − 0.053⁎⁎

(0.010) 0.060⁎⁎⁎

(0.021) 0.102⁎

(0.022) 0.889⁎

(0.014) − 0.042⁎ (0.008) 0.028⁎⁎

(0.025) 0.896⁎ (0.014) 0.934⁎

(0.022) 0.025 (0.101) − 0.001⁎⁎⁎

(0.035) 0.033⁎⁎ (0.016) 0.025⁎⁎

(0.022) 0.101⁎ (0.017) 0.078⁎

(0.021) 0.878⁎ (0.023) 0.907⁎

(0.011) − 0.036⁎⁎

(0.019) 1.367⁎

(0.011) 0.145⁎

(0.015) 0.078⁎

(0.015) 0.905⁎

(0.017) − 0.040⁎⁎ (0.019) − 0.007 (0.013)

(0.036) 0.900⁎ (0.035) 0.613⁎ (0.022)

(0.001) − 0.096 (0.221) 0.175 (0.181) − 0.196⁎⁎ (0.098)

(0.050) 0.059⁎ (0.021) 0.216⁎ (0.072)

(0.022) 0.069⁎ (0.014) 0.133⁎ (0.028)

(0.023) 0.921⁎ (0.014) 0.785⁎ (0.048)

R2

AIC

ARCH

LB

0.480

− 4.583

6.251

5.360

0.828

− 6.197

2.501

7.126

0.346

− 5.238

7.286

7.258

0.516

− 4.833

4.160

8.787

0.390

− 5.163

3.096

5.318

0.830

− 6.083

1.441

2.373

0.624

− 5.040

2.102

3.389

0.764

− 6.117

6.315

4.496

0.782

− 6.079

1.668

3.708

0.638

− 4.400

1.096

3.603

0.506

− 4.769

2.397

8.049

0.533

–5.501

6.935

6.3001

Notes: This table reports the results from estimating Model (1). Numbers in parentheses are robust standard errors. LB is the Ljung–Box tests for autocorrelation of order 6 for the standardised residuals. ARCH test is the LM ARCH test for conditional heteroscedasticity of order 6. AIC is the Akaike Information Criterion. For Food and Beverages, Basic Materials, and Industrial sectors, the model is estimated with an AR(1) because the latter is significant. We also tested for GARCH effect in the mean equation, but the associated coefficients are not significant. The orders for the GARCH model are determined based on information criteria. ⁎ Indicates the significance of coefficients at the 1% level. ⁎⁎ Indicates the significance of coefficients at the 5% level. ⁎⁎⁎ Indicates the significance of coefficients at the 10% level.

Table 4 Estimation results—non-linear models Model (2), Model (3), and Model (4). Asymmetric (Model (2))

Automobile & Parts Financials Food & Beverages Oil & Gas Health Care Industrials Basic Materials Personal & Household Goods Consumer Services Technology Telecommunications Utilities

Scaled oil price (Model (3))

Net oil price (Model (4))

Roil+ t

Roil− t

− Roil+ t = Roilt = 0

− Roil+ t = Roilt

Sop+ t

Sop− t

− Sop+ t = Sopt = 0

− Sop+ t = Sopt

− 0.046 (0.037) − 0.007 (0.017) − 0.056⁎ (0.018) 0.191⁎

− 0.001 (0.042) − 0.048⁎ (0.016) − 0.032⁎ (0.018) 0.171⁎

− 0.047 (0.038) − 0.036⁎⁎

(0.028) − 0.119⁎ (0.024) 0.016 (0.018) 0.082⁎⁎ (0.026) − 0.009 (0.016) 0.035⁎⁎⁎

0.455 (0.499) 2.002 (0.157) 0.620 (0.430) 0.103 (0.748) 3.480 (0.062) 0.420 (0.516) 3.229 (0.072) 6.088 (0.013) 0.052 (0.818) 1.069 (0.301) 0.091 (0.762) 3.979 (0.046)

− 0.002 (0.040) − 0.013 (0.018) − 0.053⁎⁎ (0.021) 0.174⁎

(0.039) − 0.032 (0.029) − 0.005 (0.020) − 0.002 (0.029) − 0.077⁎

1.951 (0.376) 13.833 (0.001) 20.240 (0.000) 118.607 (0.000) 39.174 (0.000) 0.755 (0.685) 10.938 (0.004) 30.852 (0.000) 7.913 (0.019) 4.519 (0.095) 4.447 (0.098) 4.502 (0.093)

(0.042) − 0.040 (0.032) − 0.019 (0.021) − 0.006 (0.030) − 0.078⁎ (0.018) − 0.035 (0.022) − 0.007 (0.867) − 0.022 (0.041) − 0.039⁎⁎⁎

(0.028) − 0.126⁎ (0.024) 0.022 (0.017) 0.082⁎ (0.027) − 0.007 (0.015) 0.028 (0.019) − 0.065⁎⁎⁎ (0.036) − 0.075⁎⁎

1.924 (0.382) 9.918 (0.007) 14.868 (0.001) 122.856 (0.000) 45.405 (0.000) 1.858 (0.394) 10.228 (0.006) 22.651 (0.000) 7.645 (0.022) 3.969 (0.137) 8.066 (0.017) 4.219 (0.121)

0.467 (0.494) 0.612 (0.433) 0.314 (0.574) 0.214 (0.642) 3.094 (0.078) 1.654 (0.198) 3.445 (0.063) 6.110 (0.012) 0.041 (0.838) 0.686 (0.407) 0.730 (0.392) 3.111 (0.078)

(0.016) − 0.026 (0.021) − 0.001 (0.041) − 0.030 (0.039) − 0.043⁎⁎⁎ (0.022)

(0.020) − 0.072⁎⁎⁎ (0.039) − 0.049 (0.035) 0.028 (0.021)

(0.021)

(0.015) − 0.034⁎⁎⁎ (0.019) 0.202⁎

(0.032) 0.021 (0.021)

Nop+ t − 0.068⁎⁎⁎ (0.038) − 0.030⁎⁎⁎ (0.017) − 0.068⁎⁎⁎ (0.019) 0.268⁎ (0.044) − 0.065⁎⁎ (0.030) − 0.001 (0.020) 0.021⁎⁎⁎ (0.013) − 0.088⁎ (0.019) 0.037⁎⁎⁎ (0.021) − 0.054 (0.046) − 0.049 (0.039) − 0.018 (0.029)

Notes: For coefficients: robust standard errors are in parentheses. For hypothesis tests, P−values are reported into parentheses. We also tested for GARCH in mean effect, coefficients are not significant. Models are estimated with AR(1) term when the latter is significant. We do not report the estimates of other variables and the GARCH coefficients as they are very similar to those reported in Table 5. Bold indicates rejection of the null hypothesis. ⁎ Indicates significance of coefficients at the 1% level. ⁎⁎ Indicates significance of coefficients at the 5% level. ⁎⁎⁎ Indicates significance of coefficients at the 10% level.

M.E.H. Arouri / Economic Modelling 28 (2011) 1716–1725

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Table 5 Symmetric and asymmetric short-term Granger causality test results. 1 DJ Stoxx Europe ri → Roil 0.060 Roil → ri 0.100 Automobile & Parts ri → Roil 0.052 Roil → ri 0.047 Financials 0.230 ri → Roil Roil → ri 0.042 Food & Beverages ri → Roil 0.087 Roil → ri 0.166 Oil & Gas 0.036 ri → Roil Roil → ri 0.295 Health Care ri → Roil 0.584 Roil → ri 0.635 Industrials ri → Roil 0.068 0.219 Roil → ri Basic Materials ri → Roil 0.014 Roil → ri 0.257 Personal & Household Goods ri → Roil 0.068 Roil → ri 0.051 Consumer Services ri → Roil 0.032 Roil → ri 0.049 Technology ri → Roil 0.420 Roil → ri 0.231 Telecommunications ri → Roil 0.705 Roil → ri 0.500 Utilities ri → Roil 0.022 Roil → ri 0.187 DJ Stoxx Europe ri → Sop+ 0.191 Sop+ → ri 0.063 Automobile & Parts ri → Sop+ 0.347 Sop+ → ri 0.058 Financials + ri → Sop 0.295 Sop+ → ri 0.010 Food & Beverages ri → Sop+ 0.054 Sop+ → ri 0.425 Oil & Gas ri → Sop+ 0.133 Sop+ → ri 0.862 Health Care ri → Sop+ 0.532 Sop+ → ri 0.900 Industrials ri → Sop+ 0.247 Sop+ → ri 0.101 Basic Materials + ri → Sop 0.064 Sop+ → ri 0.094 Personal & Household Goods ri → Sop+ 0.249 Sop+ → ri 0.019 Consumer Services + ri → Sop 0.154 Sop+ → ri 0.020 Technology ri → Sop+ 0.925 Sop+ → ri 0.150

2

4

1

2

4

1

2

4

0.132 0.008

0.115 0.018

ri → Roil+ Roil+ → ri

0.112 0.084

0.256 0.003

0.245 0.010

ri → Roil− Roil− → ri

0.078 0.282

0.133 0.124

0.238 0.274

0.067 0.053

0.087 0.054

ri → Roil+ Roil+ → ri

0.246 0.036

0.317 0.010

0.612 0.011

ri → Roil− Roil− → ri

0.021 0.146

0.033 0.018

0.039 0.061

0.353 0.025

0.193 0.038

ri → Roil+ Roil+ → ri

0.247 0.023

0.444 0.007

0.254 0.013

ri → Roil− Roil− → ri

0.295 0.221

0.371 0.270

0.475 0.433

0.101 0.046

0.105 0.065

ri → Roil+ Roil+ → ri

0.076 0.431

0.080 0.024

0.137 0.104

ri → Roil− Roil− → ri

0.293 0.129

0.049 0.203

0.004 0.097

0.094 0.026

0.085 0.092

ri → Roil+ Roil+ → ri

0.027 0.901

0.082 0.075

0.245 0.192

ri → Roil− Roil− → ri

0.070 0.093

0.119 0.059

0.086 0.125

0.327 0.067

0.174 0.324

ri → Roil+ Roil+ → ri

0.566 0.976

0.656 0.090

0.455 0.278

ri → Roil− Roil− → ri

0.695 0.624

0.193 0.192

0.176 0.472

0.114 0.009

0.105 0.014

ri → Roil+ Roil+ → ri

0.149 0.087

0.193 0.008

0.231 0.013

ri → Roil− Roil− → ri

0.079 0.671

0.167 0.063

0.198 0.132

0.026 0.053

0.049 0.054

ri → Roil+ Roil+ → ri

0.026 0.106

0.055 0.049

0.161 0.059

ri → Roil− Roil− → ri

0.037 0.725

0.066 0.162

0.079 0.150

0.064 0.034

0.086 0.073

ri → Roil+ Roil+ → ri

0.175 0.044

0.150 0.027

0.168 0.056

ri → Roil− Roil− → ri

0.081 0.178

0.096 0.186

0.218 0.356

0.064 0.019

0.110 0.039

ri → Roil+ Roil+ → ri

0.137 0.020

0.232 0.010

0.304 0.022

ri → Roil− Roil− → ri

0.028 0.286

0.055 0.154

0.093 0.313

0.718 0.058

0.451 0.145

ri → Roil+ Roil+ → ri

0.800 0.181

0.952 0.037

0.188 0.043

ri → Roil− Roil− → ri

0.235 0.474

0.401 0.286

0.512 0.530

0.666 0.046

0.451 0.128

ri → Roil+ Roil+ → ri

0.507 0.634

0.261 0.011

0.247 0.027

ri → Roil− Roil− → ri

0.934 0.514

0.988 0.422

0.735 0.599

0.072 0.053

0.140 0.091

ri → Roil+ Roil+ → ri

0.082 0.165

0.214 0.019

0.447 0.044

ri → Roil− Roil− → ri

0.030 0.389

0.064 0.369

0.160 0.522

0.453 0.001

0.500 0.007

ri → Sop− Sop− → ri

0.055 0.917

0.139 0.066

0.163 0.228

ri → Nop+ Nop+ → ri

0.075 0.037

0.089 0.030

0.129 0.014

0.587 0.020

0.819 0.039

ri → Sop− Sop− → ri

0.006 0.843

0.016 0.001

0.017 0.007

ri → Nop+ Nop+ → ri

0.369 0.036

0.136 0.024

0.135 0.073

0.613 0.001

0.465 0.005

ri → Sop− Sop− → ri

0.253 0.652

0.363 0.237

0.300 0.502

ri → Nop+ Nop+ → ri

0.129 0.024

0.214 0.045

0.116 0.019

0.075 0.021

0.125 0.107

ri → Sop− Sop− → ri

0.170 0.516

0.089 0.331

0.005 0.391

ri → Nop+ Nop+ → ri

0.321 0.399

0.465 0.383

0.300 0.545

0.313 0.088

0.608 0.186

ri → Sop− Sop− → ri

0.179 0.637

0.341 0.078

0.139 0.272

ri → Nop+ Nop+ → ri

0.007 0.923

0.002 0.121

0.027 0.421

0.552 0.791

0.389 0.340

ri → Sop− Sop− → ri

0.525 0.848

0.292 0.183

0.191 0.481

ri → Nop+ Nop+ → ri

0.922 0.682

0.961 0.380

0.815 0.502

0.448 0.004

0.408 0.009

ri → Sop− Sop− → ri

0.159 0.651

0.162 0.014

0.227 0.055

ri → Nop+ Nop+ → ri

0.177 0.053

0.151 0.035

0.150 0.008

0.174 0.031

0.398 0.026

ri → Sop− Sop− → ri

0.037 0.678

0.080 0.066

0.129 0.153

ri → Nop+ Nop+ → ri

0.010 0.185

0.006 0.247

0.018 0.242

0.277 0.009

0.211 0.029

ri → Sop− Sop− → ri

0.056 0.651

0.194 0.130

0.186 0.349

ri → Nop+ Nop+ → ri

0.283 0.017

0.086 0.057

0.106 0.035

0.348 0.002

0.532 0.008

ri → Sop− Sop− → ri

0.021 0.569

0.057 0.134

0.129 0.360

ri → Nop+ Nop+ → ri

0.206 0.003

0.178 0.012

0.392 0.013

0.950 0.028

0.255 0.039

ri → Sop− Sop− → ri

0.146 0.839

0.304 0.151

0.420 0.284

ri → Nop+ Nop+ → ri

0.581 0.017

0.362 0.025

0.504 0.005

(continued on next page)

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M.E.H. Arouri / Economic Modelling 28 (2011) 1716–1725

Table 5 (continued)

Telecommunications ri → Sop+ Sop+ → ri Utilities ri → Sop+ Sop+ → ri

1

2

4

0.645 0.604

0.427 0.007

0.610 0.048

0.105 0.067

0.301 0.004

0.708 0.020

1

2

4

ri → Sop− Sop− → ri

0.771 0.975

0.750 0.345

0.483 0.683

ri → Sop− Sop− → ri

0.031 0.701

0.160 0.260

0.167 0.518

1

2

4

ri → Nop+ Nop+ → ri

0.587 0.180

0.664 0.082

0.641 0.028

ri → Nop+ Nop+ → ri

0.165 0.507

0.225 0.487

0.356 0.205

Notes: This table provides the P-values of rejection of the null hypothesis. S → O is the null hypothesis of no causality from stock market returns to oil price changes. O → S is the null hypothesis of no causality from oil price changes to stock market returns. S is the sector i market return (ri), while O is the linear and non-linear oil price changes defined in this article: Roil, Roil+, Roil−, Sop+, Sop−, and Nop+. Bold indicates rejection of the null hypothesis.

Appendix B. Robustness checks Given previous evidence in the literature on the significance of world market risk and exchange rate risk in international asset pricing relationships, above all in developed stock markets such as European markets, we do the following robustness checks.5 Effects of exchange rates Oil prices are denominated in US dollars, so the dollar plays an important role in the energy industry, as exchange rates affect the amounts paid or collected by the consumers and producers of oil and related products. Therefore, it is interesting to take into account the links between the value of the dollar on the currency markets and oil prices. In the literature, the link between the dollar and oil prices was examined at a theoretical level by, among others, Krugman (1980) and Rogoff (1991), and at an empirical level by, among others, Zhou (1995), and Dibooglu (1995). The findings of these early studies suggest the existence of a positive relationship between the two variables: an increase in oil prices is linked to appreciation of the dollar. However, this relationship seems to have reversed in recent years. Indeed, the US dollar has been losing value against key currencies since 2001, while oil prices have undergone several sequences of increases and decreases over the same period (Bénassy-Quéré et al., 2007; Lizardo and Mollick, 2010). Moreover, recent papers show that exchange rate risk is internationally priced (De Santis et al., 2003). Thus, the statistical significance of oil price changes in Models (1), (2), (3), and (4) could be the result of the failure to include euro-dollar exchange rate changes. To shed light on this issue, we re-estimate the Models (1), (2), (3), and (4) with the euro-dollar exchange rate changes as a risk factor. On the whole, we find that exchange rate risk changes are significant for most European sectors. More interestingly, the relationships between oil price shocks and European sector returns are mainly unchanged. Effects of world factors European markets are integrated into world markets, so European stock returns are subject to global factor effects. Hence, as a robustness check, we test for the significance of oil price changes within the framework of a model that allows for global risk factors. The inclusion of global factors is motivated by the fact that, if oil prices and global factors are correlated, tests based on Models (1), (2), (3), and (4) may result in a spurious significance of the oil price changes because we fail to account for global factors. More precisely, we re-estimate augmented versions of Models (1), (2), (3), and (4), which include the MSCI world returns unexplained by the European market returns and Brent oil price changes. As a proxy of this global factor, we use the residuals of the OLS regression of the MSCI world returns on the DJ Stoxx returns and the oil price returns. We find that this global factor, independent of European and 5

The results of the robustness tests are available on request.

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