Natural gas prices and stock prices: Evidence from EU-15 countries

Economic Modelling 29 (2012) 1646–1654

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Natural gas prices and stock prices: Evidence from EU-15 countries Ali Acaravci a, Ilhan Ozturk b,⁎, Serkan Yilmaz Kandir c, 1 a b c

Faculty of Economics and Administrative Sciences, Mustafa Kemal University, Antakya-Hatay, Turkey Faculty of Economics and Administrative Sciences, Cag University, 33800, Mersin, Turkey Faculty of Economics and Administrative Sciences, Cukurova University, Adana, Turkey

a r t i c l e Article history: Accepted 2 May 2012 JEL classification: C3 G1 O52 Q4 Keywords: Natural gas prices Stock prices Economic activity Cointegration EU-15 countries

i n f o

a b s t r a c t This study investigates the long-run relationship between natural gas prices and stock prices by using the Johansen and Juselius cointegration test and error–correction based Granger causality models for the EU15 countries. We employ quarterly data covering the period from 1990:1 to 2008:1. Empirical findings suggest that there is a unique long-term equilibrium relationship between natural gas prices, industrial production and stock prices in Austria, Denmark, Finland, Germany and Luxembourg. However, no relationship is found between these variables in the other ten EU-15 countries. Although we detect a significant long-run relationship between stock prices and natural gas prices, Granger causality test results imply an indirect Granger causal relationship between these two variables. In addition, we investigate the Granger causal relationship between stock returns, industrial production growth and natural gas price increase for Austria, Denmark, Finland, Germany and Luxembourg. As a result, increase in natural gas prices seem to impact industrial production growth at the first place. In turn, industrial production growth appears to affect stock returns. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The relationship between energy prices and economic activity has been examined by many researchers. Natural gas, petroleum (oil) and coal are the major energy resources of the world. The share of these three fossil fuels in the total energy use of the world was about 84% at the end of 2008. The figures are 22.5% for coal, 23.9% for natural gas and 37.5% for petroleum. Natural gas occupies the second place among the energy sources (EIA, 2009). At the end of 2008, natural gas consumption of the world exceeded 3 trillion m 3 (BP, 2009). Although alternative energy sources having been developed, initiations in the alternative energy area do not seem sufficient to meet rising energy demand of the world in the near future (Henriques and Sadorsky, 2008). Since the majority of energy need of the world is supplied by fossil fuels, the fossil fuel prices have allegedly been a significant factor for the economy, particularly for the energy importing countries. Although the effects of oil prices on stock prices is well documented in the literature, there are very limited studies on the effects of natural gas prices. Nevertheless, natural gas gains an increasingly

⁎ Corresponding author. Tel./fax: + 90 324 6514828. E-mail addresses: [email protected] (A. Acaravci), [email protected] (I. Ozturk), [email protected] (S.Y. Kandir). 1 Tel.: + 90 322 3387254/283. 0264-9993/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econmod.2012.05.006

prominent role in the world energy market. This importance arises from the fact that natural gas is used in a variety of areas: residential, commercial, industrial, power generation uses and finally it is used as vehicle fuel (EIA, Natural Gas Consumption by End Use). Although vehicle fuel usage seems rather insignificant, role of natural gas in LPG production cannot be ignored. Since more than 4 million vehicles use LPG it is suggested as the largest alternative fuel in EU-27. LPG has two origins: 66% comes from natural gas and 34% from crude oil refining. Moreover, LPG offers significant environmental advantages, particularly in terms of air quality. LPG emits 9% less CO2 than diesel fuel (AEGPL, The LPG Industry Roadmap). We observe a similar situation for natural gas. Natural gas release lower level of CO2 than oil and coal (EIA, Natural Gas Issues and Trends 1998). As a result, natural gas makes an increasing contribution to environmental issues and transportation in the world. We hypothesize either a direct or an indirect relationship between energy prices and stock prices. Direct channel concentrates on profits. Since energy is a significant cost item for companies, any increase in energy prices may cause a decline in profit margins. On the other hand, since oil intensity of production has declined in developed countries, this negative impact is more pronounced for developing countries than developed countries. The indirect channel is related with real economic activity, rather than financial statements of corporations. In this case, we expect energy prices to impact real economic activity. In turn, shifts in industrial production is hypothesized to impact stock returns (Huang et al., 1996; Mussa, 2000).

A. Acaravci et al. / Economic Modelling 29 (2012) 1646–1654

Several studies have examined the effect of oil prices on stock prices. While some studies find negative effects, the others detect positive and even no effects. Nevertheless, there is no consensus about the effects of oil prices on stock prices. Sadorsky (1999) tests the effect of oil prices on stock prices. By using monthly US data, he finds that oil prices impact economic activity and thus stock market prices. Hammoudeh and Li (2005) compare the oil sensitivity of equity prices of two oil-exporting countries with that of two oil-sensitive transportation and oil industries of USA. Empirical results suggest that the oil price growth leads the stock prices of oil-exporting countries and the US oil-sensitive industries. Basher and Sadorsky (2006) study the linkages among oil prices and emerging stock market prices. By employing a multi-factor model, they assert a positive impact of oil prices on stock market prices. Nandha and Hammoudeh (2007) analyze the linkages among domestic beta risk, stock prices, oil prices, and exchange rate sensitivities of fifteen countries in the Asia–Pacific region. Empirical findings suggest that while Philippines and South Korea show oil price sensitivity, Indonesia, Malaysia, Singapore, Taiwan and New Zealand indicate sensitivity to changes in the exchange rates. Nandha and Faff (2008) investigate the indirect relationship between oil prices and equity prices for 35 global industries. They find that oil prices negatively affect economic activity. In turn, economic activity transfers this unfavorable impact to stock prices. Kandir (2008) reports that oil price changes are not priced in the Turkish stock market. O'Neill et al. (2008) find that oil price increases lead to reduced stock prices in the U.S., the UK and France. Park and Ratti (2008) demonstrate that oil price shocks have a statistically significant negative impact on real stock returns in the U.S. and 12 European oil importing countries. Cong et al. (2008) find that oil price shocks do not show statistically significant impact on the real stock returns of most Chinese stock market indices, except for manufacturing index and some oil companies. Apergis and Miller (2009) investigate the effect of oil price shocks on returns of eight stock markets. Although they cannot observe long-run relationship between stock prices and oil price changes, a short-run relationship seems to exist. Miller and Ratti (2009) find that although there occurs a significant relationship between oil prices and stock prices during relatively stable periods, this relationship cease to exist during the fluctuations in the stock markets. Chen (2010) finds evidence of oil price impact on the switches in the stock market. Arouri (2011) investigated the responses of European sector stock markets to oil price changes. His findings suggest that the strength of this association varies greatly across sectors. Moreover, for some sectors he found strong evidence of asymmetry in the reaction of stock returns to changes in the price of oil. On the other hand, the effect of natural gas prices on the stock market prices has not been exhaustively examined. Thus, the aim of this study is to fill this gap. A very limited literature concentrates exclusively on stock prices of energy companies. Boyer and Filion (2007) examine the factors that affect Canadian oil and gas companies. They find that oil and natural gas prices along with stock market prices determine the stock returns of Canadian energy companies. Likewise, Oberndorfer (2009) focuses on the linkage between energy prices and energy stock prices. Their findings suggest that oil and coal prices appear to be significant factors for energy stocks, whereas natural gas prices do not seem to share a relationship with energy companies' equity prices. Although empirical studies are restricted with energy stocks, the linkage between natural gas prices and aggregate stock market prices is worth examining. Furthermore, natural gas plays an important role in the world energy market and it accounts for nearly one quarter of the world primary energy consumption and European countries have no exception. By reaching 27%, the share of natural gas in total energy consumption of EU-15 countries even exceeds the figure for the world (BP, 2009). Projections reveal that by 2030, global gas consumption will increase more rapidly than total energy demand

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(2.1% per year versus 1.8% per year). In both OECD and EU, gas is expected to be the fastest growing conventional energy source (Kavalov et al., 2009). Nevertheless, a critical aspect of European gas consumption is its dependence on imports. At the end of 2007, gas imports of European countries amount to 260 billion m 3. Moreover, natural gas imports of European countries are projected to exceed 500 billion m 3 by 2020. Furthermore, a significant portion of natural gas is imported from Russia. In 2007, more than 40% of European gas imports were originated from Russia (European Commission Statistical Pocketbook, 2010). Diversification efforts are expected to decrease this ratio. These efforts include importing gas from Algeria by constructing pipelines under Mediterranean and by using ships to bring LNG from Algeria and Qatar (Hayes, 2004). Nabucco Project is also a crucial part of these efforts that is projected to bring annually 31 billion m 3 natural gas from Caspian basin and middle-east region to Europe (Giuli, 2008). Since natural gas plays an increasingly vital role in the European energy generation and consumption, examining the effect of natural gas prices on European stock prices will highlight a potentially significant relationship. Therefore, the contribution of this study to the energy literature arises from its originality. This is a pioneering study that examines natural gas prices–stock prices nexus for EU-15 countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and the UK). Aim of this study is to investigate the long-run relationship between natural gas prices and stock prices for EU-15 countries. We employ the Johansen and Juselius (1990) cointegration test and error–correction based Granger causality models by using quarterly data covering the period 1990:1–2008:1 (except Luxembourg, its data period is 1999:1–2008:1). The rest of the paper is organized as follows. The next section presents the methodology and data. The third section reports the empirical results. The last section concludes the paper. 2. Methodology and data Natural gas prices may have either a direct or an indirect effect on stock prices. First, energy is a significant cost item for companies that have a direct potential to deteriorate profit margins. Second, natural gas prices may impact stock prices through macroeconomic channel. We hypothesize a relationship between natural gas prices and macroeconomy. In turn, macroeconomy would affect stock prices (Huang et al., 1996; Mussa, 2000). Since natural gas price is not the mere factor that would affect stock prices, controlling for macroeconomic variables in the empirical models is a widely accepted procedure. In the light of empirical studies about the relationship between stock prices and macroeconomic variables, economic activity is added to the model beside natural gas prices. The standard log-linear functional specification of long-run relationship between the stock prices, economic activity and natural gas prices may be expressed as: spt ¼ θ1 þ θ2 ipt þ θ3 ng t þ εt

ð1Þ

Where sp is the stock price index (2005 = 100), whereas ip is the industrial production index (2005 = 100) as a proxy of real economic activity. Stock price index and industrial production index data are obtained from OECD Statistics Portal. Economic activity is added to the analysis as a control variable. ng is the natural gas price index (2005 = 100). Natural gas prices (in US dollars) are proxied by the Russian Natural Gas border price in Germany and obtained from International Monetary Fund. Since natural gas prices are expressed in US dollars, natural gas price index (2005 = 100) in Euros depend on our calculations. The EU-15 countries' stock price indices and natural gas price index are presented in Fig. 1.

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5.5 5.0

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden UK Natural Gas Prices

4.5 4.0 3.5 3.0 2.5 2.0 90

92

94

96

98

00

02

04

06

Fig. 1. The natural gas price index and EU-15 countries' stock market price indexes (2005 = 100, in log levels).

All of the series are seasonally adjusted to remove the seasonal effects by using Census X-12 seasonal adjustment method. Then, they are transformed with their natural logarithms to reduce heteroscedasticity and to obtain the growth rate of the relevant variables by their differenced logarithms. This study employs quarterly data and sample period that spans from the first quarter of 1990 to the first quarter of 2008. The only exception is Luxembourg. The sample period for Luxembourg extends from the first quarter of 1999 to the first quarter of 2008. Table 1 provides the descriptive statistics of stock returns and industrial production growth series for all EU-15 countries. The parameters, θ2and θ3, indicate the long-run elasticity estimates of stock prices with respect to industrial production and natural gas prices, respectively. The positive long-run elasticity estimates of stock prices with respect to industrial production indicate that increase in industrial production results in an increase in stock prices, vice versa is true. The positive long-run elasticity estimates of stock prices with respect to natural gas prices indicate that increase in natural gas prices results in an increase in stock prices, vice versa is true. On the other hand, if there exist negative long-run elasticity estimates of stock prices with respect to industrial production and natural gas prices, increase in industrial production or natural gas prices will result a decline in stock prices, vice versa is true. In both cases, there may be a direct or indirect impact of natural gas prices on stock prices. Increases in costs of corporations will accompany any increase in natural gas prices. Thus, we may observe a decline in profits and stock prices. On the other hand, increases in natural gas prices may

impact economic activity and economic activity would impact stock prices. In this case, however, the result of natural gas price increases may vary according to industries. Oil and gas sector companies seem to benefit from increases in energy prices (Cong et al., 2008; Faff and Brailsford, 1999; Oberndorfer, 2009). However, many other sectors seem to suffer from the increases in energy prices (Lee and Ni, 2002; Nandha and Faff, 2008). Another relationship worth examining is the one between stock prices and economic activity. Stock prices are expected to reflect future economic activity. Although there is no guarantee that stock prices fully reflect the innovations in the industrial production, predictability of real economic activity is enhanced by using stock prices (Choi et al., 1999). On the contrary, there is another strand of literature suggesting that economic activity leads shifts in stock prices (Domian and Louton, 1997; Nasseh and Strauss, 2000). One possible explanation of this lead–lag effect may be the relationship between stock prices and investments. Barro (1990) reveals the existence of a clear relationship between current stock market prices and future investment levels. Moreover, corporate profits and production are simultaneously determined by the investment level (Barro, 1990:116). Another explanation arises from the common stock valuation principles. The current value of a firm's common stock is the present value of the sum of the future dividends (Gordon, 1962:38). If dividend policy is held constant, dividend would be accepted as a function of the firm's cash flows. Thus, cash flows become the major determinant of the value of the common stock. There is a strong relationship between cash flows and production capacity of the firm. Innovations in future levels of production are expected to affect current value of cash flows. Therefore, changes in productivity of the firm will influence stock prices through their impact on cash flows (Chen et al., 1986:385). Nevertheless, it does not seem possible for a single macroeconomic variable to accurately forecast future stock prices. Some of the future production is unpredictable. So, we cannot expect that current prices fully reflect the information about the future economic activity (Fama, 1990:1107). As a result, we cannot accurately anticipate the direction of the relationship between stock prices and economic activity. The long-run relationship between the stock prices, economic activity and natural gas prices will be examined in two steps. First, we will define the order of integration in series by using unit root test and then explore the long run relationships between the variables by using Johansen and Juselius (1990) cointegration test. Second, we will test Granger causal relationships between variables by using error–correction based Granger causality models.

Table 1 Descriptive statistics of variables (%). Stock returns

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden UK Natural gas inflation

Industrial production growth

Mean

Median

Max

Min

S.D.

Mean

Median

Max

Min

S.D.

1.00 1.61 1.85 2.59 1.35 0.99 2.58 1.77 1.16 1.59 1.74 1.83 2.35 2.09 1.31

0.72 2.32 3.06 3.25 2.49 2.47 1.36 2.45 1.98 3.22 3.36 2.42 3.21 3.75 2.31

18.10 17.91 20.50 40.65 17.56 25.19 75.15 19.86 24.37 21.57 13.75 25.51 23.77 25.80 12.78

− 19.81 − 18.20 − 16.36 − 48.93 − 25.11 − 32.74 − 27.38 − 26.79 − 20.00 − 28.40 − 24.45 − 25.64 − 18.03 − 20.71 − 14.17

8.03 7.29 8.04 15.42 9.06 10.59 15.36 8.97 9.53 12.55 8.42 9.69 9.02 11.00 6.29

0.96 0.34 0.52 0.96 0.20 0.47 0.23 2.14 0.23 0.65 0.49 0.13 0.40 0.72 0.11 2.15

1.41 0.38 0.21 1.25 0.22 0.65 0.09 2.32 0.26 0.75 0.54 − 0.15 0.43 0.75 0.34 2.41

5.48 11.35 11.49 9.23 2.98 4.78 19.67 12.21 4.44 13.76 6.04 8.60 8.67 4.47 3.15 39.86

− 4.36 − 5.35 − 14.22 − 10.74 − 1.82 − 5.19 − 18.17 − 12.39 − 4.20 − 10.72 − 6.58 − 6.86 − 4.33 − 3.53 − 3.16 − 24.65

2.08 2.47 3.92 3.22 0.98 1.64 4.42 4.85 1.81 4.45 2.72 2.87 1.97 1.73 1.14 11.11

Notes: Quarterly data periods are 1999:2–2008:1 for Luxembourg and 1990:2–2008:1 for the rest of countries.

A. Acaravci et al. / Economic Modelling 29 (2012) 1646–1654

2.1. Integration and cointegration analysis In order to overcome the low power problems associated with conventional unit root tests, especially in small samples, we employ the weighted symmetric ADF test (ADF-WS) of Park and Fuller (1995). Park and Fuller assert that the weighted symmetric least squares estimator of the autoregressive parameters generally have smaller mean square error than that of the ordinary least squares estimator, particularly when one root is close to unity in absolute values. For the model with an estimated intercept, the one-sided weighted symmetric least squares test is the most powerful test. Leybourne et al. (2005) have recently noted that ADF-WS has good size and power properties when it is compared with the other tests. Therefore, it requires much shorter sample sizes than conventional unit root tests to attain the same statistical power. Johansen (1988) and Johansen and Juselius (1990; hereafter JJ) maximum likelihood (ML) procedure is a popular conventional cointegration method. The model is based on the error correction form given by: ΔZ t ¼ ΠZ t−1 þ

p−1 X

Γ i ΔZ t−i þ μ 0 þ μ 1 t þ υt

t ¼ 1; …; T

ð2Þ

i¼1

where Zt is an (nx1) column vector of p variables, Γ and Π are matrices of coefficients, μ0 and μ1 are (nx1) column vectors of constant terms and trend coefficients, Δ is a difference operator, and υt is pdimensional Gaussian error with mean zero and variance matrix. The coefficient matrix Π is known as the impact matrix and it contains information about the long-run relationships. The vector error correction (VEC) method equation above allows for three model specifications: (a) if Π has full rank, then Zt is stationary in levels and a vector autoregression (VAR) in levels is an appropriate model. (b) If Π has zero rank, then it contains no long run information, and the appropriate model is a VAR in first differences (implies variables are not cointegrated). (c) If the rank of Π is a positive number, r is less than p (where p is the number of variables in the system), there exists matrices α and β, with dimensions (p, r), such that βαZ ′ = Π. In this representation, β contains the coefficients of the r distinct long run cointegrating vectors that render β'Zt stationary, even though Zt is itself non-stationary, and α contains the short-run speed of adjustment coefficients for the equations in the system (See Awokuse, 2003; and Johansen, 1995). Johansen's methodology requires the estimation of the VAR Eq. (2) and the residuals are then used to compute two likelihood ratios (LR) test statistics that can be used in the determination of the unique cointegrating vectors of Zt. The first test which considers the hypothesis that the rank of Π is less than or equal to r cointegrating vectors is given by the trace test below: Trace ¼ −T

p X

  ⌢ ln 1− λ i

ð3Þ

i¼rþ1

Table 2 The null hypotheses for Granger causalities. Short-run causality

Long-run causality

Variables Δsp

Δip

Δgp

ψi

Δsp

– π21, 1 = … = π21, k=0 π31, 1 = … = π31, k=0

π13, 1 = … = π13, k=0 π23, 1 = … = π23, k=0 –

ψ1 = 0

Δip

π12, 1 = … = π12, k=0 –

Δgp

π32, 1 = … = π32, k=0

ψ2 = 0 ψ3 = 0

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The second test statistic is known as the maximal eigenvalue test which computes the null hypothesis that there are exactly r cointegrating vectors in Zt and is given by:   ⌢ ð4Þ λ max ¼ −T ln 1− λ rþ1 The distributions for these tests are not given by the usual χ 2 distributions. The asymptotic critical values for these likelihood ratio tests are calculated via numerical simulations (see Johansen and Juselius, 1990; and Osterwald-Lenum, 1992). 2.2. Causality analysis Although cointegration relationship implies the existence of Granger causality, it does not point out the direction of the causality relationship. Granger (1988) emphasizes that a VEC modeling should be estimated rather than a VAR as in a standard Granger causality test, if variables in the model are cointegrated. Following Granger (1988), to test for Granger causality in the long-run relationship, we employ a two-step procedure: the first step is the estimation of the long-run model for Eq. (1) in order to obtain the estimated residual ε (error– correction term, ECT hereafter). The next step is to estimate the Granger causality model with the variables in first differences and including the ECT in the systems. As opposed to the conventional Granger causality method, the error–correction based Granger causality test allows for the inclusion of the lagged error–correction term derived from the cointegration equation. By including a oneperiod lagged ECT, the long-run information lost through differencing is reintroduced in a statistically acceptable way (See Lee et al., 2008; Narayan and Smyth, 2008, 2009; and Odhiambo, 2009). In our case, the VAR (k) model with a one-period lagged ECT follows at the following forms: 2 3 2 3 2 32 3 π11;1 π12;1 π13;1 μ1 Δspt Δspt−1 4 Δipt 5 ¼ 4 μ 2 5 þ 4 π21;1 π22;1 π23;1 54 Δipt−1 5 þ … π31;1 π32;1 π33;1 μ3 Δngt 2 32 3 2 3 Δngt−1 2 3 ð5Þ π11;k π 12;k π13;k Δspt−k ψ1 ε1t þ4 π21;k π 22;k π23;k 54 Δipt−k 5 þ 4 ψ2 5ECT t−1 þ 4 ε2t 5 π31;k π 32;k π33;k Δng t−k ψ3 ε3t Table 3 Weighted symmetric ADF (ADF-WS) unit root test results. Countries

sp in levels

sp in 1st differences

ip in levels

ip in 1st differences

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden UK Gas prices (gp) CV at 5%

− 0.1854 − 2.0990 − 2.6303 − 1.5408 − 1.8249 − 1.9199 − 2.5350 − 1.2766 − 1.4470 − 1.2954 − 1.2794 − 2.4827 − 1.8202 − 1.7562 − 1.4208 − 1.9706 − 3.2647 − 3.3136 − 3.2585 − 3.3055 − 3.3612

− 6.5931 − 6.5283 − 5.6623 − 7.3962 − 7.4175 − 7.2380 − 5.5558 − 7.2072 − 7.0567 − 3.8078 − 6.6620 − 3.6983 − 8.1302 − 6.9041 − 7.9153 − 4.9942 − 2.6379 − 2.5787 − 2.4686 − 2.5835 − 2.5295

− 0.8959 (1) − 2.6038 (0) − 2.2970 (2) − 2.2949 (0) − 2.4114 (2) − 0.5675 (0) − 1.8245 (2) − 1.0132 (1) − 2.4783 (0) − 1.4755 (4) − 3.2160 (1) − 2.0224 (0) − 1.9220 (0) − 1.7448 (0) − 1.9477 (3)

− 10.7553 (0) − 11.2539 (0) − 9.5261 (1) − 9.3722 (0) − 6.5814 (0) − 7.6645 (0) − 10.5296 (1) − 11.1616 (0) − 8.6135 (0) − 7.6345 (3) − 14.0636 (0) − 10.0808 (0) − 4.8515 (0) − 8.2819 (0) − 3.2529 (2)

− 3.2647 (0) − 3.3136 (1) − 3.2585 (2) − 3.3055 (3) − 3.3612 (4)

− 2.6379 (0) − 2.5787 (1) − 2.4686 (2) − 2.5835 (3) − 2.5295 (4)

(0) (1) (1) (0) (0) (0) (2) (0) (0) (0) (0) (2) (0) (0) (0) (0) (0) (1) (2) (3) (4)

(0) (0) (0) (0) (0) (0) (0) (1) (0) (4) (0) (1) (0) (0) (0) (0) (0) (1) (2) (3) (4)

Notes: The Dickey–Fuller regressions include an intercept and a linear trend in the levels, and include an intercept in the first differences. The numbers of optimal lags are based on Schwarz Bayesian Criterion (SBC). Numbers of lags are in (). 95% simulated critical values for several observations computed by stochastic simulations. For relevant numbers of lags are in () using 1000 replications. sp is the stock prices and ip is the industrial production.

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A. Acaravci et al. / Economic Modelling 29 (2012) 1646–1654

Table 4 Johansen and Juselius cointegration test results. Countries

k

r

Trace

CV

P-value

λ-max

CV

P-value

Austria

2

Belgium

2

Denmark

2

Finland

2

France

1

Germany

2

Greece

2

Ireland

2

Italy

1

Luxembourg

1

Netherlands

2

Portugal

1

Spain

1

Sweden

1

UK

1

r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1 r=0 r≤1

31.22 3.93 21.42 7.60 29.47 9.16 26.56 4.76 18.70 8.07 31.54 8.09 21.60 7.05 25.34 11.09 15.68 7.74 29.38 8.47 22.67 4.99 15.06 3.93 19.67 8.32 17.46 5.45 18.64 6.34

29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49 29.79 15.49

0.0340 0.9090 0.3318 0.5088 0.0545 0.3510 0.1127 0.8337 0.5149 0.4584 0.0312 0.4563 0.3212 0.5721 0.1497 0.2061 0.7343 0.4941 0.0558 0.4161 0.26424 0.8098 0.0340 0.9090 0.4452 0.4321 0.6061 0.7595 0.5192 0.6559

27.29 3.93 13.82 7.20 20.31 6.11 21.80 4.66 10.63 6.98 23.45 8.06 14.55 5.34 14.25 7.83 7.95 6.56 20.90 7.34 17.68 4.40 10.87 3.55 11.36 7.98 12.01 4.98 12.30 3.74

21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26 21.13 14.26

0.0060* 0.8667 0.3800 0.4657 0.0648*** 0.5992 0.0402** 0.7821 0.6840 0.4914 0.0232** 0.3727 0.3214 0.6982 0.3448 0.3957 0.9069 0.5421 0.0538*** 0.4499 0.1422 0.8144 0.6600 0.9039 0.6118 0.3804 0.5466 0.7442 0.5182 0.8857

Notes: k is based on SBC information criteria test results. r is the number of cointegrating vectors. CV is critical values that are taken from Osterwald-Lenum (1992). *,** and *** indicate the existing one cointegration relationship between variables at 1%, %5 and %10 significance levels, respectively.

Residual terms, ε1t, ε2t and ε3t, independently and normally distributed with zero mean and constant variance. The optimal lag length, k, based on Schwarz Bayesian Criterion (SBC), is found one. The VEC modeling approach allows us to distinguish between “short-run” and “long-run” Granger causality. The Wald-tests of the “differenced” explanatory variables give us an indication of the “short-term” causal effects, whereas the “long-run” causal relationship is implied through the significance or other wise of the t test(s) of the lagged error–correction term that contains the longterm information since it is derived from the long-run cointegrating relationship. Nonsignificance or elimination of any of the “lagged error–correction terms” affects the implied long-run relationship and may be a violation of theory. The nonsignificance of any of the “differenced” variables that reflects only short-run relationship, however, does not involve such violations because; theory typically has little to say about short-term relationships (see Masih and Masih, 1996).

Using Eq. (5), Granger causal relationships can be examined in two ways (see Table 2): i) Masih and Masih (1996) and AsafuAdjaye (2000) interpret the weak Granger causality as ‘short run’ Granger causality in the sense that the dependent variable responds only to short-term shocks to the stochastic environment. Short-run or weak Granger causalities are detected through the F-statistics or Wald test for the significance of the relevant π coefficients on the first differenced series: For short-run dynamic effect, we check H0 : π12, 1 = 0 and H0 : π13, 1 = 0for Δsp, H0 : π21, 1 = 0 and H0 : π23, 1 = 0for Δip, and H0 : π31, 1 = 0 and H0 : π32, 1 = 0for Δnp. ii) Masih and Masih (1996) point out that another possible source of causation is the ECT in equations. The coefficients of the ECT's represent how fast deviations from the long run equilibrium are eliminated following changes in each variable. The long-run Granger causalities are examined through the t-test or Wald test for the significance of the relevant ψ coefficients on the lagged error–correction term. For longrun effect, we check H0 : ψ1 = 0 for Δsp, H0 : ψ2 = 0 for Δip, and H0 : ψ3 = 0 for Δnp. For example ψ1 is zero, sp does not respond to the deviations from the long-run equilibrium in the previous period. ψi = 0, i = 1, 2, 3 for all i is equivalent to both Granger non-causality in the long-run and the weak exogeneity (Hatanaka, 1996). 3. Empirical results Results of the weighted symmetric ADF test (ADF-WS) are presented in Table 3. The null hypothesis is unit root and the alternative hypothesis is level stationary. The Dickey–Fuller regressions include an intercept and a linear trend in the levels, and include an intercept in the first differences. The numbers of optimal lags are based on Schwarz Bayesian Criterion (SBC). 95% critical values for several observations computed by stochastic simulations. Findings indicate that all variables have unit root or non-stationary in levels but they are stationary in first differences. Thus, we can confidently apply the JJ cointegration methodology to determine the long run relationships between the variables and derive the error correction terms from the cointegrated variables. The numbers of optimal lags for the JJ cointegration tests are obtained by unrestricted VAR model using SBC. Critical values are taken from Osterwald-Lenum (1992). The results of JJ cointegration rank tests in Table 4 indicate that there is a unique long-term or equilibrium relationship between natural gas prices, industrial production and stock prices in Austria, Denmark, Finland, Germany and Luxembourg. However, no relationship is found in the other ten EU countries. The long-run relationship between natural gas prices, industrial production and stock prices (Eq. (1) for Austria, Denmark, Finland, Germany, and Luxembourg are estimated by using multipleequation ML method. Long-run analysis results for EU-15 countries demonstrate that there are positive long-run elasticity estimates of stock prices with respect to industrial production (θ2〉0) in Denmark, Finland and Germany (see Table 5). But there are negative long-run elasticity estimates of stock prices with respect to industrial production (θ2〈0) in Austria and Luxembourg. We detect positive long-run

Table 5 Cointegrating relationship estimation (spt = θ1 + θ2ipt + θ3ngt + εt). Variables

Austria

Denmark

Finland

Germany

Luxembourg

θ1 θ2 θ3 ECT R-squared LM HET

5.0161 − 2.2828 [− 3.2729] 2.1596 [5.8887] − 0.1108 [− 2.4054] 0.0667 8.6386 (0.4713) 81.4318 (0.5591)

− 8.4365 2.1653 [5.0516] 0.6636 [4.9746] − 0.1481 [− 2.8291] 0.1793 15.1783 (0.0862) 92.8512 (0.2384)

− 13.4764 5.8782 [8.6025] − 2.0084 − [4.7454] − 0.0966 [− 2.6156] 0.1726 11.9565 (0.2458) 98.9877 (0.1262)

− 134.0928 39.9772 [4.1450] − 10.1935 [− 4.8541] − 0.0681 [− 2.9064] 0.0623 13.0419 (0.1607) 95.6805 (0.1805)

27.3685 − 7.0033 [− 4.4177] 2.0576 [6.3455] − 0.1056 [− 0.2164] 0.1730 10.0473 (0.3467) 46.2399 (0.5452)

Notes: The long-run relationships between variables and deriving of error correction terms from the cointegrated variables obtained by using multiple-equation ML method. t-statistics for coefficients are in []. The null hypothesis of the LM test is that there is no serial correlation up to lag order 4. HET is White's (1980) test that is a test of the null hypothesis of no heteroskedasticity against heteroskedasticity of unknown, general form. Probability values for the LM and HET tests are in ().

A. Acaravci et al. / Economic Modelling 29 (2012) 1646–1654

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Table 6 Granger causality test results. Austria

Short-run causality

Variables

Δsp

Δip

Δgp

ψi, i = 1, 2, 3

Long-run causality

Δsp Δip Δgp

– 5.1980 (0.0226)** 0.5849 (0.4444)

0.0222 (0.8815) – 0.0836 (0.7725)

0.0975 (0.7549) 0.0736 (0.7861) –

0.1458 (0.9858) 1.6401 (0.2003) 17.3893 (0.0000)*

Denmark

Short-run causality

Variables

Δsp

Δip

Δgp

ψi

Long-run causality

Δsp Δip Δgp

– 14.7201 (0.0001)* 0.3885 (0.5331)

0.0432 (0.8354) – 0.0293 (0.8641)

3.1010 (0.0782)*** 1.9659 (0.1609) –

0.4868 (0.4854) 2.8211 (0.0930)*** 6.3631 (0.0117)**

Finland

Short-run causality

Variables

Δsp

Δip

Δgp

ψi

Long-run causality

Δsp Δip Δgp

– 17.6635 (0.0000)* 2.0764 (0.1496)

1.4363 (0.2307) – 4.8053 (0.0284)**

0.0853 (0.7703) 2.5982 (0.1070) –

9.7014 (0.0018)** 4.7020 (0.0301) 11.1554 (0.0008)*

Germany

Short-run causality

Variables

Δsp

Δip

Δgp

ψi

Δsp Δip Δgp

– 4.1966 (0.0405)** 2.0990 (0.1474)

0.0557 (0.8134) – 5.1611 (0.0231)**

0.0864 (0.7688) 0.0008 (0.9778) –

0.1415 (0.9053) 1.9678 (0.1607) 14.2024 (0.0002)*

Luxembourg

Short-run causality

Variables

Δsp

Δip

Δgp

ψi

Δsp Δip Δgp

– 1.6343 (0.4417) 5.0343 (0.0807)***

0.5966 (0.3564) – 10.0820 (0.0065)*

3.4910 (0.1746) 4.5300 (0.1038) –

1.6980 (0.4278) 4.5968 (0.1004) 28.9696 (0.0000)*

Long-run causality

Long-run causality

Notes: The null hypothesis is that there is no causal relationship between variables. Values in parentheses are p-values for Wald tests with a χ2 distribution. Δ is the first difference operator. *,** and *** are 1%, %5 and %10 significance levels, respectively. An appropriate lag length, based on Schwarz Bayesian Criterion (SBC), is one.

elasticity estimates of stock prices with respect to natural gas prices (θ3〉0) in Austria, Denmark and Luxembourg; whereas there appear negative long-run elasticity estimates of stock prices with respect to natural gas prices (θ3〈0) in Finland and Germany. The reason for long-run positive relationship between variables in Austria, Denmark and Luxembourg may be due the demand factor in these countries, which is also mentioned in the study of Kilian (2009). These findings imply that the long-run impacts of natural gas prices and industrial production on stock prices are rather complicated. Table 5 also presents the coefficient of error correction terms that show how quickly variables converge to equilibrium and it should have a statistically significant coefficient with a negative sign. The estimated coefficient of ECTs are negative and their values are about 10–15%. They indicate that any deviation from the long-run equilibrium between variables is corrected about 10–15% for each period and takes about 7 periods to return the long-run equilibrium level. The findings about the relationship between stock prices and economic activity are in line with the literature. Nasseh and Strauss (2000) report positive linkages among stock prices and economic activity in six European countries. Moreover, they find that economic activity forecasts considerable portion of the variation in stock prices. Likewise, Domian and Louton (1997) provide similar evidence from the US. They assert that changes in industrial production are followed by changes in stock prices in the same direction. However, they fail to find an opposite linkage. On the other hand, we cannot observe similarity between our findings and the literature of energy prices and stock price relationships. Gjerde and Saettem (1999) and Basher and Sadorsky (2006) present evidence of a direct positive impact of energy prices on stock prices in Norway and 21 emerging markets, respectively. Nandha and Faff (2008) and O'Neill et al. (2008) indicate that oil prices negatively impact stock prices. Furthermore, Kandir (2008)

and Cong et al. (2008) fail to detect any relationship between energy prices and stock prices. Nevertheless, there is no consensus about the direction of the relationship between stock prices and energy prices. This study also explores Granger causal relationship between the variables by using error–correction based Granger causality models which are weak (short-run) Granger causality and long-run Granger causality. Although all variables are in natural logarithm, their first differences imply the growth rate in these variables that are stock

sp

sp sp ip

ip

sp ip

sp gp ip

gp

Denmark

Austria

sp

ip gp

gp

sp sp ip

ip

gp

ip

Finland

gp

Germany

sp ip gp

gp

ip

sp ip

Long-run Granger causality running from the independent variables to dependent variable, One-way short-run Granger causality.

Luxembourg

Fig. 2. Granger causality relationship flows.

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returns (Δsp), industrial production growth (Δip), and natural gas inflation (Δgp) and for variables sp, ip and gp, respectively. The results of both Granger causality models (see Table 6 and Fig. 2) can be summarized as follows: i) There is a long-run Granger causal relationship from industrial production growth and natural gas inflation to stock returns in Finland. ii) There is a long-run Granger causal relationship from stock returns and natural gas inflation to industrial production growth in Denmark. iii) There is a long-run Granger causal relationship from stock returns and industrial production growth to natural gas inflation in Austria, Denmark, Finland, Germany and Luxembourg. .008

Response of sp

iv) There is a short-run unidirectional Granger causal relationship from stock returns to industrial production growth in Austria, Denmark, Finland and Germany. v) There is a short-run unidirectional Granger causal relationship from industrial production growth to natural gas inflation in Finland, Germany and Luxembourg. vi) There is a short-run unidirectional Granger causal relationship from natural gas inflation to stock returns in Denmark. vii) Finally there is a short-run unidirectional Granger causal relationship from stock returns to natural gas inflation in Luxembourg. Granger causality test results imply the existence of an indirect relationship between stock returns and natural gas inflation. We .000

.004

-.002

.000

-.004

-.004

-.006

-.008 2

4

6

8

10 12 14 16 18 20

.05

-.008 Austria

Response of ip

2

4

6

8

10 12 14 16 18 20

2

4

6

8

10 12 14 16 18 20

.000 Response of sp

-.001

.04

-.002

.03

-.003 .02

-.004

.01

-.005

.00

-.006 2

4

6

8

10 12 14 16 18 20 Denmark

.00

.000 Response of sp

Response of ip

-.001

-.04

-.002

-.08

-.003 -.12

-.004

-.16

-.005

-.20

-.006 2

4

6

8

2

10 12 14 16 18 20

4

6

8

10 12 14 16 18 20

Germany .003

.01

Response of ip

Response of sp

.00

.002

-.01

.001

-.02

.000

-.03

-.001 -.002

-.04 2

4

6

8

10 12 14 16 18 20

2

4

6

8

10 12 14 16 18 20

Finland .00 Response of sp

-.02

Response of ip

.0125 .0100

-.04

.0075

-.06

.0050

-.08

.0025

-.10

.0000 2

4

6

8

10 12 14 16 18 20

2

4

6

8

10 12 14 16 18 20

Luxembourg Fig. 3. Responses of stock price (sp) and industrial production (ip) to Cholesky one S.D. innovations on natural gas prices (gp).

A. Acaravci et al. / Economic Modelling 29 (2012) 1646–1654

observe a Granger causal relationship that streamlines from natural gas price increase to industrial production growth in Finland and Germany. Likewise, Granger causality test results reveal a pervasive causality from industrial production growth to stock returns. The only exception is Luxembourg. However, we cannot observe any Granger causal relationship between stock returns and natural gas price increase in these countries except Denmark. Oberndorfer (2009) obtains a similar result by asserting that natural gas price increase do not seem to impact stock returns. However, Oberndorfer (2009) does not examine the indirect channel between stock returns and natural gas price increase, rather he focuses on direct one. Similarly, Boyer and Filion (2007) ignore the indirect channel. Although they report a significant natural gas and oil price increase effect on stock returns, they suffer from not including a proxy of economic activity in their regression model. In the light of these findings, we may conclude that there is an indirect Granger causal relationship between stock returns and natural gas price increase. Increases in natural gas prices impact industrial production growth and industrial production growth effect stock returns. We also explore the response of industrial production growth and stock returns to Cholesky one standard deviation natural gas price increase innovations using impulse response functions. These are computed here as dynamic multipliers and shown in Fig. 3 for Austria, Denmark, Finland, Germany and Luxembourg. These figures indicate that result of one standard deviation shock in natural gas inflation and stock returns in Finland, Germany and Luxembourg are negatively affected, however, it is positively affected in Denmark and fluctuated in Austria. In addition, result of one standard deviation shock in natural gas inflation and industrial production growth in Austria, Denmark, Finland are negatively affected, while industrial production growth in Germany and Luxembourg are positively affected. 4. Conclusion and policy implications Aim of this study is to investigate the long-run relationship between natural gas prices and stock prices for EU-15 countries. Sample period spans from the first quarter of 1990 to the first quarter of 2008. Empirical findings suggest that there is a unique long-term relationship among natural gas prices, industrial production and stock prices in Austria, Denmark, Finland, Germany and Luxembourg. On the contrary, no relationship is found between these variables for Belgium, France, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweeden and the UK. Although we detect a significant long-run relationship between stock prices and natural gas prices, Granger causality test results imply an indirect Granger causal relationship between these two variables. In addition, we investigate the Granger causal relationship between stock returns, industrial production growth and natural gas inflation for Austria, Denmark, Finland, Germany and Luxembourg. Natural gas prices seem to impact economic activity at the first place. In turn, economic activity appears to affect stock returns. This study makes original contributions to finance and energy literature. This study is one of the first studies that examine the relationship between natural gas prices and stock prices. Although the relationship between oil prices and financial indicators is well documented, limited number of studies examine the relationship between natural gas prices and stock prices. Moreover, this limited literature exclusively focus on the effect of natural gas prices on energy companies. In addition, although, the scarce literature that examine the linkage between stock prices and natural gas prices concentrate on the direct channel, we focus on the indirect channel through which natural gas prices seem to impact stock prices. Thus, financial managers will have to take into account the macroeconomic consequences of shifts in natural gas prices beside focusing on varying cost and profit levels due to changes in natural gas prices. Finally, conventional asset

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pricing models that employ merely oil prices to proxy energy factor would also consider the impacts of natural gas prices. New studies would employ a wide variety of energy sources in modifying asset pricing models. The study also has significant policy implications: Particularly for Austria, Denmark, Finland, Germany and Luxembourg. Since capital markets of these countries demonstrate sensitivity to innovations in natural gas prices, rendering gas supply security gains a crucial importance. Moreover, concerns related with gas supply security may justify costs of the projects to diminish reliance on Russian gas. Future of expensive natural gas supply projects such as Nabucco would benefit from this justification. Although we make several contributions to the literature, there remains a gap related with the unexplained source of different exposures of EU-15 countries to natural gas prices. Previous studies reveal discrepancies among exposures of developed and emerging countries to varying energy prices (Maghyereh, 2004). However, although we reveal differences among the exposures of EU-15 countries to natural gas prices, it is not expected to observe intra-country differences in an economically and financially united region, such as EU. Moreover, we cannot observe any significant differences in natural gas dependency levels of EU-15 countries. Thus, it seems difficult to extract significant results about the source of discrepancies in exposures to natural gas prices. Further research efforts would shed light on this issue. References AEGPL, L. The LPG industry roadmapAvailable at: http://www.aegpl.eu/Objects/1/ Files/AEGPL%20Roadmap%20June2007.pdf. Awokuse, T.O., 2003. Is the export-led growth hypothesis valid for Canada? Canadian Journal of Economics 36, 126–136. Apergis, N., Miller, S.M., 2009. Do structural oil-market shocks affect stock prices? Energy Economics 31, 569–575. Arouri, M.E.H., 2011. Does crude oil move stock markets in Europe? A sector investigation. Economic Modelling 28 (4), 1716–1725. Asafu-Adjaye, J., 2000. The Relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Economics 22, 615–625. Barro, R.J., 1990. The stock market and investment. Review of Financial Studies 3 (1), 115–131. Basher, S.A., Sadorsky, P., 2006. Oil price risk and emerging stock markets. Global Finance Journal 17, 224–251. Boyer, M.M., Filion, D., 2007. Common and fundamental factors in stock returns of Canadian oil and gas companies. Energy Economics 29, 428–453. BP, 2009. Statistical review of world energyJune 2009. Available at: bp.com/ statisticalreview2009. Chen, N., Roll, R., Ross, S.A., 1986. Economic forces and the stock market. Journal of Business 59 (3), 383–403. Chen, Shiu-S, 2010. Do higher oil prices push the stock market into bear territory? Energy Economics 32 (2), 490–495. Choi, J.J., Hauser, S., Kopecky, J., 1999. Does the stock market predict real activity? Time series evidence from the G-7 countries. Journal of Banking & Finance 23, 1771–1792. Cong, R.-G., Wei, Y.-M., Jiao, J.-L., Fan, Y., 2008. Relationships between oil price shocks and stock market: an empirical analysis from China. Energy Policy 36 (9), 3544–3553. Domian, D.L., Louton, D.A., 1997. A threshold autoregressive analysis of stock returns and real economic activity. International Review of Economics and Finance 6 (2), 167–179. EIA, 2009. Independent statistics and analysisSeptember 2009. Available at: http:// www.eia.doe.gov/emeu/mer/pdf/pages/sec1_7.pdf2009. EIA, 1998. Natural gas issues and trends. Available at: http://www.eia.doe.gov/oil_gas/ natural_gas/analysis_publications/natural_gas_1998_issues_and_trends/it98. html1998. European Commision Statistical Pocketbook, 2010. EU Energy and Transport in Figures. Faff, R.W., Brailsford, T.J., 1999. Oil price risk and the Australian stock market. Journal of Energy Finance and Development 4, 69–87. Fama, E.F., 1990. Stock returns, expected returns, and real activity. Journal of Finance 45 (4), 1089–1108. Gjerde, O., Saettem, F., 1999. Causal relations among stock returns and macroeconomic variables in a small open economy. Journal of International Financial Markets, Institutions & Money 9, 61–74. Giuli, M., 2008. Nabucco pipeline and the Turkmenistan conundrum. Caucasian Review of International Affairs 2 (3), 124–132. Granger, C.W.J., 1988. Some recent developments in a concept of causality. Journal of Econometrics 39, 199–211. Gordon, M.J., 1962. The savings investment and valuation of a corporation. Review of Economics and Statistics 44 (1), 37–51.

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