Oil Price Changes and Stock Markets in Energy Exporting and Importing Countries

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Master’s Thesis International Business and Management

& International Financial Management

Oil Price Changes and Stock Markets in

Energy Exporting and Importing Countries

Michael de Kroon

(s1335286)

Msc International Business and Management & International Financial Management

Groningen, Netherlands June, 2009

Email: michaeldekroon@gmail.com

Supervisor: dr. ing. N. Brunia

Faculty of Economics and Business University of Groningen

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Abstract

The dependence of the world on oil is widely known. The oil price is a macroeconomic factor which influences production and consumption. This is expected to be reflected in stock markets. Whereas the influence of the oil price on stock markets has only recently been addressed by a handful of studies, this study makes a distinction between energy importing countries and energy exporting countries. The key argument lies in the fact that a higher oil price transfers income from energy importing countries to energy exporting countries. Vector auto regressions are performed to determine the response of stock markets to a change in the oil price in energy importing and exporting countries. Granger tests are performed to examine the predictive power of the oil price on total stock return. Results show that stock markets in energy exporting countries do not respond to oil price changes in the long-run, whereas significant short-run responses are lacking. A negative response of energy importing countries in the long- and in the short-run can be concluded when insignificant responses are taken into account. Furthermore, the oil price does not predict total stock return in energy exporting countries.

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1. Introduction

The price of oil is an important factor in the global economy these days. Before 1973 there was little volatility in the price of oil. Since the control of the oil price has been in hands of mainly the Organization of Petrol Exporting Countries (OPEC), the price of oil has been fluctuating a lot. Only 10 years ago (1998), the price for a barrel of crude oil was $15, whereas the price of a barrel reached an all time high of $140 in June 2008. The fact that the price dropped to $40 within 6 months (January 2009) is enough evidence to state that the price of oil is very volatile. This volatility directly affects the global economy. The International Monetary Fund (IMF, 2000) estimates that a $5 price increase per barrel of crude oil reduces the global economic growth by 0.3% during the following year.

The media pays much attention to daily changes in the oil price and its effect on stock markets. However, only recently, an increasing amount of literature is devoted to this relationship. This could be explained by the increasing concern of the heavy oil dependence of the world nowadays and the current high volatility of the oil price.

Graph 1 World oil consumption in barrels (x mln) per day Graph 2 Oil price in US$ per barrel of Brent Crude

Source: E.I.A. (2008) _{Source: E.I.A. (2008)}

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whether a country is an energy exporter or importer. Moreover, the results of Ratti & Park (2008) and Jones & Kaul (1996) contradict each other. In fact, Jones & Kaul (1996) find a negative oil price exposure for an energy exporting country whereas Ratti & Park (2008) find a positive oil price exposure for an energy exporting country. The oil price can be generalized to the price of energy because other types of energy can be regarded as substitutes (Regnier, 2007). Therefore, the distinction between energy importers and exporters can be made, instead of only oil importers and exporters.

It can be argued that stock markets in energy exporting countries benefit from higher oil prices due to higher revenues as opposed to the energy importing countries which have to cope with higher energy costs. This suggests that a rise in the oil price transfers income from energy importing to energy exporting countries (Sachs, 1981). Costs in energy importing countries rise, while revenues in energy exporting countries rise. This implies, ceteris paribus, an opposite reaction of the stock market return to oil price fluctuations in energy exporting and energy importing countries.

The objective of this research is to analyze the impact of oil price changes on the stock markets of energy exporting and importing countries. A distinction is made between short-run and long-run reactions. If a stock market shows a certain response, it should not be taken for granted that the same effect is likely to persist over the long-run. Therefore it is investigated whether there is a difference between short-run and long-run effects and whether these are different for energy importing and energy exporting countries. Results of this study will give investors on the stock markets empirical evidence on whether stock markets in energy exporting countries react differently to changes in the oil price compared to energy importing countries. The empirical evidence may be of significant value when it comes to deciding whether to invest in an energy importing or exporting country. If an unexpected oil price change occurs, the investor can anticipate to this. The academic relevance of this research can be found in the fact that no research has made the distinction between energy importing and exporting countries when studying the impact of oil prices on stock markets. This study argues that this distinction needs to be made due to opposite reactions to oil price changes. Therefore, the following research question has been formulated:

Research Question:

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To investigate the relations between oil price changes and the stock markets of energy importers and exporters in the short- and in the long-run , the following hypotheses have been formulated:

H1a: Stock market return in energy exporting countries is positively related to unexpected oil price changes in the short-run.

H1b: Stock market return in energy exporting countries is positively related to unexpected oil price changes in the long-run.

H2a: Stock market return in energy importing countries is negatively related to unexpected oil price changes in the short-run.

H2b: Stock market return in energy importing countries is negatively related to unexpected oil price changes in the long-run.

This study covers 42 countries, of which 13 are energy exporting countries, from January 1987 until October 2008. The relations between oil price, stock market return, and a set of control variables are studied for all separate countries by applying a Vector Autoregressive model (VAR). Most researches that study the influence of the oil price on stock markets apply a VAR (Park & Ratti, 2008; Ciner, 2001; Cong et al., 2008; Henriques & Sadorsky, 2007; Sadorsky, 1999; Huang et al., 1996; Papapetrou, 2001). In the VAR model all variables are endogenous and it allows the data to speak freely in estimating economic relationships (Sims, 1980). Accumulated generalized impulse response functions are performed in the different VARs to determine the responses of the stock markets to oil price changes in the short- and in the long-run. Variance decompositions indicate the extent to which oil price changes contribute to the variance in stock market returns. Granger tests are conducted to test whether the oil price can predict total stock returns and whether differences between energy exporters and importers can be observed.

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2. Literature

2.1 Stock markets and the Oil Price

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because other types of energy can be regarded as substitutes (Regnier, 2007). Pindyck (1999) found that prices and price volatility for non-petroleum energy products correlate with the price of oil.

2.2 Energy exporting versus energy importing countries

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expenditures often remain inside the country, they are redirected back into the economy. It remains to be seen whether the higher energy costs in energy exporting countries are offset by the higher revenues due to the higher price of energy.

With regard to the energy importing countries, the consequences of a rise in the oil price are more unfavorable. A negative effect that energy importers (as well as exporters) experience is that the profitability of the firms generally decreases, due to higher energy costs. As the share price is the present value of the expected future cash flows (to equity), the values on the stock markets will decrease. Because firms incorporate higher energy prices in the prices of their products, the consumer will also pay part of the bill. As a result, the demand will drop. This will also have a negative effect on the companies’ profits and therefore on the value of their shares. Moreover, the question remains to what extent the oil price can predict the possible effect that an oil price change has on the stock market.

2.3 Indirect effects of oil price changes on stock markets

The oil price can also have indirect effects on stock markets. The word indirect refers to the fact that oil price has an influence on a certain factor which in turn has an influence on stock returns. One of these factors is inflation. When oil prices rise, consumers will save less, borrow more, or reduce consumption. This is caused by the fact that the cost of living increases (inflation) due to higher costs. The cost of living increases because firms let their consumers pay part of the bill of the increased costs of energy. Empirical research confirms that the oil price is an indicator for inflation (Sadorsky, 1999; Cunado & Perez, 2005). The inflation that is caused by the rise in the oil price causes a decrease of the value of the stock market. In which way and to what extent the stock market value decreases, depends on whether or not wages are adjusted to cover for increased consumer prices. If wages are not adjusted, the consumer demand will decrease due to the higher cost of living of the consumers. This has a negative effect on the sales, profits, and thus the stock prices of the companies. If wages are adjusted by the companies, the consumer demand will not drop while the costs of the firms for the wages increase. This has its effect on the profits of the companies and will result in lower share prices. The negative effect of inflation on stock markets is confirmed by many empirical studies (e.g. Schwert, 1981; Udegbunam & Eriki, 2001; Fama, 1981).

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pressure on the interest rate. The interest rate, in turn, has its influence on the stock market. The relationship between the interest rate and the stock market is confirmed by the study conducted by Park & Ratti (2008) which studies the impact of oil price shocks on real stock returns. Studies by Sweeney & Warga (1986), O’Neal (1998), and Bernanke & Blinder (1992) confirm the sensitivity of stock markets to changes in the interest rate. A higher interest rate negatively influences the stock markets due to the fact that the value of firms is related to the interest rate. This is the case as the interest rate determines the cost of debt and equity. A change in the interest rate changes the cost of debt and consequently has an impact on the profitability of the firm and consequently its dividend payments. As the present value of the future expected cash flows is the definition of the share price, a change in the interest rate will change the price of a share. Furthermore, a change in the interest rate affects the opportunity cost of investments. Companies that have plans to invest, come to the conclusion that the net present value of the investment has decreased when interest rates rise. This decreases the probability of firms expanding their operations and their willingness to invest in new capital. Such a situation might even lead to a decrease in current operations. This effect will also be reflected in the share price of a company and thus on the stock market.

A third way in which the oil price indirectly influences the stock market is through industrial production. The level of industrial production can account for the growth of an economy, which has previously been mentioned in this chapter. The consequences of a higher oil price for industrial production can be different for energy exporters as opposed to importers. As discussed before, higher oil prices lead to higher (energy) costs and eventually lower (consumer) demand. This has a direct negative effect on the industrial production in energy importing countries. Lower industrial production means lower earnings of the companies due to decreased revenues. This is then reflected in the value of the shares on the stock market. On the other hand, the effect of a higher oil price can be different in energy exporting countries, where industrial production is stimulated by the higher oil earnings. This relates to the fact that the increase of the oil price transfers income from energy importers to exporters, as stated earlier. Industrial production is then stimulated by the higher oil earnings that are indirectly distributed to the private sector by government investments and expenditures which favor the economy and the country as a whole.

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price (e.g. Ewing & Thompson, 2007). This relationship, however, is more applicable to industrial production on a global level as the oil price is determined on a global level.

As stated before, negative effects of the oil price also strike energy exporting countries. However, energy exporters have the benefit of compensating the negative effects due to higher earnings that derive from energy export. This, in the first place, boosts profits in the energy sector. The extent to which this shows up on the stock market depends on whether and how many energy companies are listed on the domestic stock market. This effect is expected to occur immediately. The second benefit that has been discussed refers to the additional governmental income which is redirected into the economy and stimulates economic activity. Investments increase, which give the country long-run profits. The extent to which energy exporting countries benefit from higher oil prices depends on whether the disadvantages of a higher oil price can be offset by the advantages.

2.4 Empirical work

As stated earlier, few studies have examined the influence of the oil price on stock markets. Table 1 shows an overview of studies that have been conducted on this topic and their key findings. As most researchers have the object to capture all dynamic interrelations between the variables, a VAR is applied in most studies. Because this method has proven to be an effective way to determine the influence of oil price changes on stock markets among other macroeconomic variables, this study follows this practice. The variables that have been applied in previous studies are taken into account when determining which macroeconomic variables need to be included in this study.

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Table 1 Previous empirical work studying the effect of oil price changes on stock markets*

* All studies apply a VAR model and use impulse responses and variance decompositions in the analysis

Author Period Title Countries Variables Key findings

Cong et al.

(2008) 1996-2007 monthly

Relationships between oil price shocks and stock market: An empirical analysis from China

China Real oil price, real stock returns, short-term interest rate, industrial production, consumer price index

- Oil price changes do not show significant impact on stock market returns. Manufacturing index and oil companies are affected.

- No asymmetric effect of oil price changes on stock returns of oil

- Increase in volatility does not affect most stock market returns, but increases speculation on stock market.

Papapetrou (2001) 1989-19996 monthly Oil price shocks, stock market, economic activity and employment in Greece

Greece Real oil price, real stock index return, 12-month T-bill rate, industrial production, industrial employment

- Oil price changes have impact on stock market returns.

- Oil price changes affect real economic activity and employment rate. - Stock market returns do not lead to changes in real activity and employment. - Stock market returns respond negatively to interest rate shocks.

Park & Ratti (2008)

1986-2005 monthly

Oil price shocks and stock markets in the US and 13 European countries USA, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, UK

Real oil price, real stock returns, short-term interest rate, industrial production

- Oil price changes have impact on stock market returns contemporaneously and/or within the following month.

- Norway, as an oil exporter, shows a significant positive response of stock market returns to an oil price increase.

- An increase in real oil price leads to an increase in short-term interest rate in 9 out of 14 countries within one or two months.

Sadorsky (1999)

1947-1996 monthly

Oil price shocks and stock market activity

US real oil price, real stock return, industrial production, interest rates

- After 1986, oil price changes explain a larger fraction of the variance in stock market returns than interest rates.

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3. Data

As discussed before, the factors that will be considered in this research are: oil price, stock market returns, inflation, interest, and industrial production. In contrast with previous researches, oil price, stock market return and interest are not corrected for inflation. Nominal data is used. This is due to the fact that the influence of inflation is more important in the long-run than it is in the short-run1. Because the effect of the oil price on stock market return is found to normally die out within a year (Park & Ratti, 2008), the consideration of real values becomes of less importance. By including inflation as a separate variable in the model, its interactions with other variables can be studied. Industrial production will not be deflated. As this variable is measured in output volume, these time series represent real values. Because the focus of this study is on the interaction between oil price and stock market return, the industrial production is kept at its original real level.

Data is collected on a monthly basis, starting in January 1987 and ending in December 2008. All data are expressed in the same currency (US$). This makes all data consistent and comparable. The choice of using monthly data is based on two arguments. Firstly, data on inflation and industrial production are only available on a monthly basis. Secondly, it is in line with previous studies that study the relationship between the oil price and stock market movements (Cong et al., 2008; Driesprong, Jacobsen & Maat, 2005; Papapetrou, 2001; Park & Ratti, 2008; Sadorsky, 1999). Time series that represent industrial production and inflation are all adjusted using the Census X12 method to correct for seasonal trends. This is the common practice in previous studies that include these variables in similar studies (Cong et al., 2008; Papapetrou, 2001).

3.1 Countries

The Energy Information Administration (2009) provides a database of country level energy data. Based on information on total energy production and total energy consumption, it can be determined whether a country is a net energy exporter or a net importer. An overview of the total energy production and energy consumption is shown in table 2 of appendix A. As discussed before, all types of energy are considered (crude oil, coal, petroleum products, gas, nuclear, hydro, geothermal, solar, renewable energy and waste, electricity, and heat). The

1

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database of country level energy data lists OECD and OPEC members and ranges from 1980 until 2006. Based on yearly figures on total energy production and consumption it can be calculated whether a country is an energy exporter (energy production > energy consumption) or importer (energy production < energy consumption). Countries where energy production exceeds energy consumption (net exporter) from 1987 until 2006 are included in the sample for energy exporters and countries where energy consumption exceeds energy production (net importer) in each year are included in the sample of energy importers. Countries that are energy exporters in one year and energy importers in other years, or vice versa, are not included in this research. Furthermore, countries that do not have a stock exchange are not included in the sample. Many listed countries are lacking data on at least one of the variables. Only countries that lead to a set of data of at least 90 observations in which all five variables are represented are included in the sample. A set of data that consists of less than 90 observations is not favourable when applying a VAR. This leads to a total of 29 importers and 13 exporters. The sample that represents the energy exporters is smaller for two reasons. First, larger problems are encountered when it comes to data availability as it often concerns less developed countries (e.g. Middle East, Africa) as opposed to many more developed energy importers. Second, the amount of exporters in the world is substantially lower than the amount of importers. As a result, the sample of energy importers is larger. The countries that are included in the research and the amount of observations are listed below.

Table 2 Countries in the samples

Importers period obs period obs Exporters period obs

Austria 07/91-09/08 207 Jordan 01/99-09/08 117 Argentina 04/91-08/08 209

Belgium 11/89-08/08 226 Netherlands 06/87-09/08 256 Canada 06/87-06/08 253

Brazil 01/96-10/08 154 Pakistan 01/93-06/08 186 Colombia 01/93-08/08 188

Chile 12/99-08/08 105 Peru 10/95-09/08 156 Indonesia 02/93-09/08 188

Czech Rep. 01/95-08/08 164 Philippines 01/88-09/08 249 Malaysia 01/90-09/08 225

Finland 01/88-10/08 250 Poland 01/93-10/08 190 Mexico 01/88-09/08 249

France 11/87-09/08 251 Portugal 02/99-10/08 117 Nigeria 01/00-03/08 99

Germany 06/87-09/08 256 Spain 01/90-09/08 225 Norway 06/87-09/08 256

Greece 04/94-09/08 174 Sri Lanka 01/00-04/08 100 Oman 01/00-09/08 105

Hungary 10/95-09/08 156 Sweden 06/87-09/08 256 Russia 01/95-09/08 165

India 04/94-08/08 173 Switzerland 01/96-06/08 150 Saudi Arabia 01/01-06/08 90

Ireland 01/88-09/08 249 Thailand 01/90-10/08 226 South Africa 01/93-09/08 189

Israel 01/93-08/08 188 Turkey 01/90-09/08 225 Venezuela 02/97-04/07 123

Italy 01/90-09/08 225 USA 06/87-10/08 257

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3.2 Stock market return (sr)

The nominal return on stock markets is defined as the continuously compounded return on the stock market index (Park & Ratti, 2008; Sadorsky, 1999; Jones & Kaul, 1996). The nominal monthly returns of the different stock markets are retrieved from the Morgan Stanley Capital International (MSCI) Barra (2009) database. The index that will be used to measure the nominal monthly return on the stock markets is the standard MSCI gross index (total return) in US dollars for each country. If a country’s index is not provided by the MSCI Barra database, the Standard & Poor’s database will provide data. The S&P/IFC index in US dollars will then be the representative variable. To test whether the different indices are comparable, correlation is checked between the indices for countries where both series are available. The correlations confirm a strong positive correlation between the series. The average correlation between the series is 0.993 (table 5, appendix B). Table 1 in appendix B gives an overview of the series that are used for each country.

3.3 Oil Price (op)

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Graph 3 Spot prices ($/barrel) of crude oil 1997 – 2009

Source: www.eia.doe.gov

(World, Non-OPEC, and OPEC prices are based on weekly spot prices weighted by estimated export volume. Brent is based on weekly spot prices)

Statistical results for correlation confirm a strong positive correlation between Brent crude oil spot prices and the other three price indicators. The correlation between the Brent crude oil spot price and the world crude oil price, the non-OPEC crude oil price, and the OPEC crude oil price is 0.999 (appendix B, table 6).

Besides the fact that Brent crude oil spot prices show a strong correlation with other oil prices, it is also commonly used as a representative oil price in previous studies (Abeysinghe, 2001; Cong et al., 2008; Jacobsen & Maat, 2005). Monthly spot prices from January 1987 until December 2008 are retrieved from the Energy Information Administration (2008) and not corrected for inflation. The results of a unit-root test (table 4) show that the series is integrated of the first order I (1).

Table 4 Results unit-root test of oil price series*

log-level 1st diff. log

p – value 0.9914 0.0000

* ADF-test was performed at a significance level of 5% (H0: Series has a unit-root)

3.4 Inflation (i), Short-term interest (r), and Industrial production (ip)

To take the effect of inflation into account consumer price indexes (CPIs) of each country are included in this study. The (nominal) short-term interest rates are in the majority of the cases represented by the monthly 3-month interbank rates or 3-month treasury bills. This is in line with previous work that studies the relationship between oil prices and stock markets (Cong et al., 2008; Papapetrou, 2001; Park & Ratti, 2008; Sadorsky, 1999). The choice of which

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alternative is used is made based on data availability. Data on industrial production for each country is expressed in monthly industrial production indices (IPIs). The data for the different variables are acquired through different databases. The different sources and raw series that are used are displayed in appendix B (tables 2, 3, and 4).

Table 5 displays a summary of the descriptive statistics. The values in the table are averages of the series that are used in this study. All series are in first differences of the logarithms. The oil price refers to the same series for energy importers and exporters. However, a difference in the average oil price is found in the table, because the datasets of the different countries do not all represent the same period of time. The table also shows that the averages of the series differ most for the three control variables. Appendix A gives a complete overview of the descriptive statistics.

Table 5. Summary of descriptive statistics*

ENERGY EXPORTERS

ENERGY IMPORTERS

Oil Price 0.0107 0.0102

Total Stock Return 0.0126 0.0121

Inflation 0.0079 0.0046

Short-term Interest -0.0051 -0.0030

Industrial Production 0.0017 0.0031

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4. Methodology

4.1 The Vector Auto Regression (VAR)

The VAR model is a commonly used model to measure the dynamic interaction between macroeconomic variables (Sadorsky, 1999). As mentioned before, most researches that have studied the influence of the oil price on stock markets apply a VAR (Park & Ratti, 2008; Cong, Wei & Fan, 2008; Sadorsky, 1999; Papapetrou, 2001). A VAR is a system of equations in which each variable is a function of its own lag and the lag of other variables in the system (see equations below).

Each country within the sample (Table 1) is separately analyzed using the VAR model. In the VAR that is performed, all series are included in first differences of the logarithms. The use of first differences of the logarithms is in line with previous studies that apply a VAR in studying the relationship between the oil price and stock markets (Park & Ratti, 2008; Cong, Wei & Fan, 2008; Sadorsky, 1999; Papapetrou, 2001). This common practice assumes the series to be integrated of the first order I(1). However, this does not necessarily have to be the case for all series. The results of unit-root tests in table 1 in appendix C show that not all series are integrated of the first order I(1). One could argue that a series should be included in the VAR according to its appropriate order of integration. This is taken into account in this study by performing a robustness check which will be discussed paragraph 4.5.

The model of the VAR system per country is shown below.

The vector auto regression:

Y

t

=

A

1

Y

t-1

+ A

2

Y

t-2

+ ... + A

p

Y

t-p

+

ε

t

Where: Yt = k-vector of the variables

Ai = matrices of the coefficients to be estimated

ε

_t = k-vector of unexpected change

p = lags determined by AIC

As mentioned earlier, a separate dataset is created for each country. This way every country covers a different period and a different amount of observations. There will be no missing values within these different datasets.

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The HQ criterion is found to outperform other criteria. However, in models with smaller samples (n<120) AIC is a better criterion (Liew, 2004).

4.2 Impulse Response Functions

As the standard estimated coefficients from structural VARs often appear to be lacking statistical significance (Sims, 1986), impulse response functions are often a better test of the specification of the model. The impulse response functions show the response of one variable to an unexpected change of another variable. The focus in this study will be on the response of the stock market return in the short-run and in the long-run to an unexpected change in the oil price. The interpretation of these results will show whether the hypotheses are accepted or rejected.

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With regard to the long-run responses, generalized accumulated impulse response functions of a VAR have a limitation. The VAR allows the variables to converge to their co integrating relationships in the long-run. A vector error correction (VEC) corrects for the long-run equilibrium by adding an error correction feature to the VAR. The error correction term captures the information on the long-run relation between variables of interest that is otherwise lost. To test whether adding an error correction term to the model leads to different results, the log likelihoods of the VARs are compared to the log likelihoods of the VECs. It appears that only in the case of Oman, the error correction leads to better results (appendix C, table 2). However, this result for Oman could be related to the fact that this is a country with a relatively low number of observations. From the test results, it can be concluded that adding the error correction feature to the model does not lead to better results. Therefore, the generalized accumulated impulse response functions are used to interpret long-run responses. Based on the results of each country, differences between energy importers and exporters can be identified. The interpretation of the results will give an answer to the research question and test the following hypotheses:

H1a: Stock market return in energy exporting countries is positively related to unexpected oil price changes in the short-run.

H1b: Stock market return in energy exporting countries is positively related to unexpected oil price changes in the long-run.

H2a: Stock market return in energy importing countries is negatively related to unexpected oil price changes in the short-run.

H2a: Stock market return in energy importing countries is negatively related to unexpected oil price changes in the long-run.

4.3 Variance decompositions

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fact that variance decompositions quantify the extent to which an oil price change contributes to total stock return. If the response of the stock market to an unexpected change in the oil price is very strong, for example, the variance that is contributable to the oil price is expected to be high. The decompositions may also be an indicator of energy dependence of a country. To investigate whether this can be confirmed, actual dependencies on net energy export and net energy import are calculated. The dependencies are determined by the relative volume of energy exports or imports to gross domestic product (GDP). It remains to be seen whether the actual dependence can be found back in the empirical results of the variance decompositions and whether there are differences between energy exporters and importers.

4.4 Granger tests

As a VAR focuses on the correlations between the variables, the Granger test identifies the predictability of variables. Results of these tests show whether the lagged values of one variable help to predict another variable at a 95% confidence level. Granger tests are often applied to test for causality. However, the fact whether the identified relationships can be said to be causal is arguable. The Granger test can test whether one variable can help to predict another, but this says nothing about the nature of the relationship. Causality refers to the nature of the relationship. Therefore, Granger tests are performed to test for predictability, rather than causality. Furthermore, predictability can help investors to make better decisions. If the oil price can predict stock market return, investors can anticipate to this. Predictability is also tested vice versa. The reason for testing for predictability of total stock return on the oil price is that stock prices reflect expected future earnings. If higher stock prices imply higher future profits, then future demand is expected to be higher. This means that more will be produced and that the demand for energy will increase. At a global level this would imply higher oil (energy) prices. However, it remains to be seen whether higher domestic stock market returns can predict the oil price which is determined on the world market.

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The results of the impulse response functions directly contribute to an answer on the research question by testing the four hypotheses. The variance decompositions and the Granger tests do not directly contribute to an answer on the research question. However, they are relevant in this study. Variance decompositions quantify the extent to which an oil price change contributes to the change in total stock return. Results of the Granger tests contribute to better decision making by providing information on the predictability of total stock return by the oil price.

4.5 Alternative VAR specifications

As discussed before, first differences of the logarithms are used in the VAR model. As this is the common practice in literature (Park & Ratti, 2008; Cong, Wei & Fan, 2008; Sadorsky, 1999; Papapetrou, 2001), this study follows this method. Using first differences of the logarithms, however, assumes that the series are integrated of the first order I(1). This asks for a check whether this is the case for each of the series. Therefore, each time series is subjected to a unit root test in order to examine the properties of the series before including them in the VAR model. The null hypotheses that the series have a unit root are tested at a 95% confidence level using the Augmented Dickey-Fuller test (ADF). The results are used to conclude whether series are I(0), I(1) or I(2). The results of the unit root tests are displayed in table 1 in appendix C. From the table it can be concluded that not all series are integrated of the first order I(1). This observation asks for an alternative model. Therefore, a robustness check is performed to explore whether different results are found when the series are included according to their appropriate order of integration (I(0), I(1) or I(2)). The robustness check will focus on comparing the generalized accumulated impulse response functions of the two models as these are of the most value in answering the research question.

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5. Empirical Results

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Table 6.1 & 6.2. Results for energy exporting and importing countries

Note: Significant results are indicated by a capital ‘P’ (positive response) and ‘N’(negative response) *Net energy imports and exports are measured in volume (BTUs)

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Tabel 7. Hypotheses tested by impulse responses Hypothesis Responses needed to accept Hypothesis* Significant responses Hypothesis accepted? Insignificant responses H1a: Stock market return in energy exporting

countries is positively related to unexpected oil price changes in the short-run.

10 or more out of 13 positive 4 positive NO 8 positive H1b: Stock market return in energy exporting

countries is positively related to unexpected oil price changes in the long-run.

10 or more out of 13 positive 0 positive NO 9 positive H2a: Stock market return in energy importing

countries is negatively related to unexpected oil price changes in the short-run.

19 or more out of 29 negative 11 negative NO 24 negative H2b:Stock market return in energy importing

countries is negatively related to unexpected oil price changes in the long-run.

19 or more out of 29 negative 9 negative NO 22 negative * Based on 95% confidence that the results are not a result of coincidence (binomial distribution)

5.1 Energy exporting countries

The results (table 6) for the energy exporters show 4 significant short-run responses out of the 13 countries that have been included in the sample of energy exporters. The significant results that are found accept the hypothesis that total stock return in energy exporting countries is positively related to the oil price in the short-run. As the sample of exporters only consists of 13 countries, the binomial distribution of the experiment is explored to investigate whether the findings are not a result of coincidence. As can be seen in table 7, ten or more energy exporters should show a significant positive response to an oil price increase in order to accept hypothesis H1a. As this is not the case, the hypothesis that energy exporters respond positively to a higher oil price in the short-run (H1a) is rejected.

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this is nine times the case in the long-run. This amount of positive responses is not enough to draw conclusions, as at least 10 results are needed to draw conclusions from this sample. This is regardless of the fact insignificant responses are also considered. Hence, insignificant results do not give the positive significant results additional support to draw the conclusion that stock markets in energy exporting countries respond positively to an oil price increase. A surprising result is that 2 out of the 4 significant positive results are the two countries that are found to be the least energy dependent among the energy exporters. However, the contribution of the oil price to the variance in total stock return is high in the case of Argentina. This can explain the positive response in the case of Argentina. Furthermore, the two countries that depend most on their energy exports (Oman and Saudi Arabia) do not show significant results. This could be due to the limited amount of data that was available when these countries were investigated. As the period that was concerned for Oman and Saudi Arabia only ranges from the year 2000 and 2001 onwards respectively, the results may partially be biased by the extraordinary oil price behavior in the years 2007 and 2008 combined with the start of the financial crisis in 2008. To test whether this is the reason for such poor results on such oil dependent countries, these countries should be subjected to another VAR analysis, where the year 2008 is omitted. Omitting observations is not favorable as the two countries do not have many observations. However, as the oil price behavior of 2008 could be the cause of insignificant responses, a check is performed. The results of the accumulated generalized impulse responses show a negative short-run effect for Oman, while the response for Saudi Arabia remains insignificant (appendix E). No long-run effects can be identified for the two countries. However, no further conclusions are drawn on the question whether the extraordinary year of 2008 is the cause of insignificant results, given the constraint of insufficient observations.

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The results for the energy exporting countries cannot be compared to existing literature, as only results for Norway (Park & Ratti, 2008) and Canada (Jones & Kaul, 1996) are known to exist in literature. The positive response of the Norwegian stock market in the short-run is confirmed in this study, whereas short-term results for Canada prove to be insignificant.

5.2 Energy importing countries

The results of the energy importing countries show 12 significant responses out of 29 countries in the short-run. Only one country shows a positive response. In order to conclude a statistical significant negative short-run response, 19 negative short-run responses are needed (table 7). Hence, the hypothesis that an oil price increase leads to a negative response of total stock return in energy importing countries in the short-run (H2a) is rejected. Peru is the only energy importer that shows a significant positive response to an unexpected oil price increase in the short-run. An explanation for this contradiction may be found in the fact that Peru appears to be the least energy-dependent energy importing country within the sample. On the other hand, the most energy dependent energy importing countries do not show many significant (negative) responses. In fact, out of the ten most energy dependent energy importers only one country shows a significant negative response.

Significant long-run results do not differ much from the significant short-run results. Nine out of 29 countries show a significant negative response while no significant positive responses occur. In line with the significant short-run responses, this does not give enough evidence to accept the hypothesis that stock markets in energy importing countries respond negatively to an oil price increase in the long-run (H2b).

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found in cases where the contribution of the oil price is high. Sri Lanka is an exception. This country shows a 26.97% contribution of the oil price to a change in total stock return while no significant responses are found. An explanation for this could be that the dataset for Sri Lanka only consists of 99 observations.

Given these findings, it can be stated state the results for energy importing countries are generally in line with the results that have been found in previous studies that included importing countries in the sense that results suggest a negative relationship between the oil price and stock returns. However, a study by Park & Ratti (2008) provides stronger empirical evidence for the fourteen countries that are investigated in their study. Results in this study are lacking significance.

5.3 Robustness Check

As discussed previously, two alternative models are applied in this study. The objective of this is to assess whether the alternative models lead to significantly different results. The results of the generalized accumulated impulse response functions are summarized in appendix E (table 1). The first alternative model includes the series in the VAR model according to their appropriate order of integration. The second alternative model ignores the control variable, which results in a two-variable VAR that only includes the oil price and total stock return. Z-tests for two proportions (appendix D, tabes 5.1-5.4) do not find any significant differences between the results of the two alternative models and the original model that is applied in this study. This, in the first place, means that the use of first differences of the logarithms instead of using the series according to their appropriate order of integration does not lead to different results. The use of first differences of the logarithms is therefore justified. Secondly, control variables do not significantly influence the response of total stock market return to a change in the oil price.

5.4 Predictability

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Table 8 & 9. Predictability of the variables on the other variables

ENERGY EXPORTERS (13) ENERGY IMPORTERS (29)

ON Oil Price Inflation Short- term Interest Industr. Prod. Total Stock Return ON OF OF Oil Price Inflation Short- term Interest Industr. Prod. Total Stock Return

Oil price 0 0 3 0 Oil price 3 5 4 12

Inflation 0 3 3 4 Inflation 9 4 3 6 ST-Interest 0 4 3 4 ST-Interest 2 6 5 5 Industrial Production 2 2 0 3 Industrial Production 2 1 4 0 Total stock return 1 5 1 2 Total stock return 2 6 2 4

Not one of the energy exporting countries shows predictability of the oil price on total stock return whereas inflation, short-term interest, and industrial production predict total stock return in some energy exporting countries. Although four significant responses of total stock return to a change in the oil price have been found for the short-run, the oil price appears to be a very weak predictor of stock market return in energy exporting countries. In energy importing countries, the predictability of the oil price on total stock return is stronger (12 out of 29 countries), but results are lacking statistical significance.

The predictability of total stock return on the oil price is found to be low for both; energy exporters and importers. As expected, domestic stock markets do not have the power to predict the oil price which is determined on a global level.

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6. Limitations

Like any other study, this research has its limitations. The first limitation of this study can be found in the fact that the sample of energy exporters only consists of 13 countries, while energy importers are represented by 29 countries. The fact that the amount of energy importers is larger, is in the first place due to the fact that there are more energy importers in the world than there are exporters. Another reason for the small amount of energy exporters is the fact that many exporters (especially oil exporters) are less developed countries such as Oman and Nigeria. This has consequences for the data availability. Some of these countries do not even have a domestic stock exchange such as Bahrein. Therefore, not all energy exporting countries in the world can be considered.

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7. Conclusion

The theoretical foundation for stating that energy exporting countries benefit from higher oil prices, while energy importing countries suffer, is existent. The key argument is that a higher oil price transfers money from energy importing countries to energy exporting countries. Nevertheless, academic literature has paid little attention to this phenomenon in relation to the stock markets in these countries. This research has tried to empirically investigate the reactions of stock markets in energy importing and exporting countries to changes in the oil price. By examining 29 energy importers and 13 energy exporters the attempt is made to determine the effect of the oil price on the stock markets in energy importing and exporting countries while controlling for interest, inflation, and industrial production.

As stated in the introduction, the research question that this study attempts to answer is: ‘Do stock markets in energy exporting and importing countries react differently to unexpected changes in oil prices?’ The findings of this study contribute to an answer. Although the responses of the stock markets to a change in the oil price are lacking statistical significance, certain observations can be made.

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that dependence on oil price changes can in the long-run be avoided by investing in energy exporting countries.

Energy importing countries also show insufficient significant results to be able to draw conclusions. When insignificant responses are considered, however, the results suggest a negative relation between the stock markets in energy exporting countries and the oil price in the long- and in the short-run. Although statistical significance is lacking, the response of stock markets in energy importing countries appears to be stronger than in energy exporting countries, especially in the long-run. This is surprising as the energy dependence of the energy importers is significantly lower than the energy dependence of the energy exporting countries. Whereas predictability of the oil price on stock market return was not found for any of the energy exporting countries, the oil price is found to have some predictive power in energy importing countries. From an investor’s perspective, the results of energy importing countries suggest that stock markets are mostly negatively affected by a higher oil price although statistical significance is lacking in this study. Previous studies have confirmed this negative relationship. The ability of an investor to anticipate to this negative relationship is more existent in energy importing countries than in energy exporting countries as predictability of the oil price on stock market return exists to some extent. However, statistical significance to generalize this to all energy importing countries is lacking.

In sum, the formulated hypotheses are rejected. The proposed relationships are not statistically confirmed in this study. However, there is an indication that stock markets in energy exporting and importing countries respond differently to oil price changes. Moreover, the distinction between short-run and long-run effects has proven to be correct as differences are concluded for energy exporting countries.

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Appendix A

Tables 1.1 - 1.13. Descriptive statistics – Energy exporting countries

(Values are in first differences of the logarithms) Argentina

Oil price Inflation St-interest Industr. prod Stock return

Mean 0.009221 0.005683 -0.002652 0.002824 0.010313 Median 0.022704 0.004290 0.000000 0.001294 0.016618 Maximum 0.276863 0.090793 1.781249 0.135709 0.670454 Minimum -0.369537 -0.010231 -1.030751 -0.160922 -0.376233 Std. Dev. 0.092750 0.009629 0.343724 0.038813 0.121937 Skewness -0.281693 4.091504 1.017766 -0.599777 0.452860 Kurtosis 4.066507 32.19801 8.852125 6.116646 7.346445 Jarque-Bera 12.60862 7968.871 332.7199 96.65424 170.8366 Probability 0.001828 0.000000 0.000000 0.000000 0.000000 Sum 1.917936 1.182101 -0.551547 0.587438 2.145049 Sum Sq. Dev. 1.780723 0.019192 24.45628 0.311830 3.077806 Observations 208 208 208 208 208 Canada

Mean 0.007678 0.002061 -0.004404 -1.54E-05 0.009259 Median 0.018128 0.001994 -0.000595 -0.001552 0.015714 Maximum 0.388811 0.022361 0.447110 0.116719 0.135638 Minimum -0.369537 -0.008630 -0.633249 -0.119448 -0.248779 Std. Dev. 0.101212 0.002934 0.088291 0.032518 0.053022 Skewness 0.043326 0.960835 -0.229671 0.085806 -1.183691 Kurtosis 4.746775 12.47790 18.47471 5.008215 6.662407 Jarque-Bera 32.11669 981.9956 2516.615 42.65495 199.6861 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 1.934789 0.519332 -1.109744 -0.003878 2.333155 Sum Sq. Dev. 2.571192 0.002161 1.956600 0.265409 0.705642 Observations 252 252 252 252 252 Colombia

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Indonesia

Mean 0.009795 0.009529 -0.000848 0.002040 0.004919 Median 0.023098 0.006680 -0.004589 0.002577 0.012050 Maximum 0.276863 0.112535 1.713658 0.259382 0.441989 Minimum -0.369537 -0.012874 -1.279240 -0.269329 -0.519902 Std. Dev. 0.096174 0.014591 0.321891 0.073888 0.135189 Skewness -0.287851 3.965877 0.880980 -0.202143 -0.482335 Kurtosis 3.897767 22.29564 9.918457 6.924297 5.448614 Jarque-Bera 8.862383 3391.201 397.1378 121.2661 53.96741 Probability 0.011900 0.000000 0.000000 0.000000 0.000000 Sum 1.831692 1.781845 -0.158489 0.381549 0.919945 Sum Sq. Dev. 1.720398 0.039600 19.27222 1.015463 3.399353 Observations 187 187 187 187 187 Malaysia

Mean 0.007504 0.002617 -0.001316 0.005683 0.003831 Median 0.018128 0.001969 0.000000 0.006527 0.006913 Maximum 0.388811 0.038151 0.465739 0.094022 0.405762 Minimum -0.369537 -0.004946 -0.603398 -0.111996 -0.359501 Std. Dev. 0.103300 0.003616 0.090421 0.027911 0.088765 Skewness 0.051481 4.706252 -2.235843 0.030074 -0.167308 Kurtosis 4.777581 44.02668 22.74242 5.393273 6.899617 Jarque-Bera 29.59035 16536.65 3824.418 53.49282 142.9772 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 1.680880 0.586213 -0.294714 1.272985 0.858249 Sum Sq. Dev. 2.379593 0.002915 1.823240 0.173719 1.757076 Observations 224 224 224 224 224 Mexico

Mean 0.007340 0.010677 -0.013833 0.002308 0.017383 Median 0.019047 0.007326 -0.006982 0.001855 0.025001 Maximum 0.388811 0.076454 0.692892 0.043885 0.254071 Minimum -0.369537 -0.005153 -0.682677 -0.043783 -0.419327 Std. Dev. 0.102379 0.011142 0.124591 0.012104 0.092874 Skewness 0.049475 2.687950 0.631844 0.222919 -0.933385 Kurtosis 4.646931 13.41914 13.03973 4.862924 6.194097 Jarque-Bera 27.90227 1408.952 1049.530 37.60988 140.2926 Probability 0.000001 0.000000 0.000000 0.000000 0.000000 Sum 1.805700 2.626564 -3.402887 0.567890 4.276232 Sum Sq. Dev. 2.567945 0.030416 3.803126 0.035897 2.113254 Observations 246 246 246 246 246 Nigeria

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Norway

Mean 0.007055 0.008706 -0.003125 0.001698 0.009360 Median 0.017489 0.005319 0.001693 0.003643 0.014595 Maximum 0.388811 0.118952 0.729734 0.119279 0.155837 Minimum -0.369537 -0.010363 -0.569671 -0.102458 -0.326419 Std. Dev. 0.101050 0.013750 0.087782 0.030956 0.072847 Skewness 0.053780 4.213208 1.018156 0.174236 -0.974102 Kurtosis 4.730008 26.73443 29.31456 5.424688 5.725087 Jarque-Bera 31.92280 6739.733 7401.403 63.75579 119.2295 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 1.798902 2.220050 -0.796750 0.432987 2.386737 Sum Sq. Dev. 2.593643 0.048025 1.957245 0.243398 1.347891 Observations 255 255 255 255 255 Oman

Mean 0.014347 0.002314 -0.004294 -0.002504 0.016916 Median 0.034189 0.001997 -0.002961 -0.002654 0.017100 Maximum 0.276863 0.017572 0.017732 0.066384 0.172898 Minimum -0.369537 -0.011034 -0.034262 -0.058915 -0.118546 Std. Dev. 0.102718 0.006555 0.009486 0.024519 0.052929 Skewness -0.596985 0.194096 -0.161059 0.264675 0.348649 Kurtosis 4.222602 2.433257 3.387038 3.214230 3.000835 Jarque-Bera 12.65472 2.044858 1.098757 1.413126 2.106978 Probability 0.001787 0.359720 0.577309 0.493337 0.348719 Sum 1.492050 0.240627 -0.446618 -0.260454 1.759312 Sum Sq. Dev. 1.086756 0.004426 0.009269 0.061924 0.288554 Observations 104 104 104 104 104 Russia

Mean 0.011815 0.018571 -0.018658 0.003194 0.015593 Median 0.024202 0.011125 -0.012342 0.002981 0.029073 Maximum 0.276863 0.320070 1.393175 0.070388 0.477071 Minimum -0.369537 -0.007095 -0.641446 -0.101677 -0.930583 Std. Dev. 0.099334 0.029875 0.203926 0.019765 0.172419 Skewness -0.341588 6.892295 1.747029 -0.796286 -1.087530 Kurtosis 3.831119 65.51074 16.65347 8.917566 8.394585 Jarque-Bera 7.909504 28000.32 1357.275 256.6181 231.1883 Probability 0.019163 0.000000 0.000000 0.000000 0.000000 Sum 1.937670 3.045644 -3.059924 0.523751 2.557181 Sum Sq. Dev. 1.608351 0.145477 6.778483 0.063675 4.845735 Observations 164 164 164 164 164 Saudi Arabia

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South Africa

Mean 0.009782 0.005544 -0.000426 0.002165 0.010279 Median 0.022704 0.005266 -0.000984 0.001427 0.015373 Maximum 0.276863 0.024639 0.189242 0.106198 0.193694 Minimum -0.369537 -0.004635 -0.122690 -0.062361 -0.363989 Std. Dev. 0.095917 0.004346 0.043877 0.026981 0.078887 Skewness -0.288193 0.644131 0.201300 0.336728 -0.905605 Kurtosis 3.918418 4.871365 5.279753 3.971969 5.518283 Jarque-Bera 9.209750 40.43273 41.98164 10.95309 75.37416 Probability 0.010003 0.000000 0.000000 0.004184 0.000000 Sum 1.838941 1.042182 -0.080043 0.407059 1.932376 Sum Sq. Dev. 1.720404 0.003532 0.360007 0.136127 1.163726 Observations 188 188 188 188 188 Venezuela

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Table 1.14 – 1.42. Descriptive statistics – Energy importing countries (Values are in first differences of the logarithms) Austria

Mean 0.008838 0.001788 -0.003024 0.003108 0.006560 Median 0.022704 0.001662 0.000000 0.001945 0.009766 Maximum 0.276863 0.008704 0.146873 0.064871 0.124575 Minimum -0.369537 -0.003862 -0.151490 -0.065765 -0.193930 Std. Dev. 0.093305 0.001951 0.040531 0.021339 0.053814 Skewness -0.275448 0.352125 -0.223685 0.062460 -0.531403 Kurtosis 4.012973 4.295943 5.077636 3.366743 3.488384 Jarque-Bera 11.41241 18.67250 38.76844 1.288407 11.74266 Probability 0.003325 0.000088 0.000000 0.525080 0.002819 Sum 1.820642 0.368350 -0.623016 0.640194 1.351419 Sum Sq. Dev. 1.784713 0.000780 0.336774 0.093348 0.593668 Observations 206 206 206 206 206 Belgium

Mean 0.008411 0.001865 -0.002935 0.001154 0.007394 Median 0.018767 0.001783 0.000000 0.002575 0.013116 Maximum 0.388811 0.011669 0.355877 0.134883 0.167128 Minimum -0.369537 -0.004764 -0.181895 -0.120872 -0.210728 Std. Dev. 0.103137 0.002331 0.052456 0.041437 0.051508 Skewness 0.037465 0.504686 1.108320 0.196760 -1.009908 Kurtosis 4.787267 4.705765 13.21935 3.219627 5.917639 Jarque-Bera 29.99942 36.82938 1025.144 1.904010 118.0526 Probability 0.000000 0.000000 0.000000 0.385966 0.000000 Sum 1.892550 0.419529 -0.660457 0.259598 1.663572 Sum Sq. Dev. 2.382742 0.001217 0.616371 0.384622 0.594292 Observations 225 225 225 225 225 Brazil

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Chile

Mean 0.015121 0.003132 -0.000581 0.002544 0.011844 Median 0.034189 0.002624 0.005465 0.002332 0.018238 Maximum 0.276863 0.011583 1.105270 0.041271 0.183412 Minimum -0.369537 -0.003980 -1.121824 -0.061685 -0.184675 Std. Dev. 0.102137 0.003200 0.193672 0.016336 0.057555 Skewness -0.616202 0.298924 -0.298772 -0.482062 -0.437378 Kurtosis 4.321805 2.726726 22.56095 4.652854 3.973800 Jarque-Bera 14.15261 1.872442 1659.614 15.86633 7.425100 Probability 0.000845 0.392107 0.000000 0.000359 0.024415 Sum 1.572594 0.325763 -0.060381 0.264599 1.231748 Sum Sq. Dev. 1.074490 0.001055 3.863401 0.027488 0.341197 Observations 104 104 104 104 104 Czech Republic

Mean 0.012511 0.003659 -0.007025 0.003499 0.015607 Median 0.024495 0.003102 -0.004320 0.003809 0.020826 Maximum 0.276863 0.028990 0.716399 0.129074 0.263019 Minimum -0.369537 -0.006822 -0.331926 -0.126869 -0.322547 Std. Dev. 0.099238 0.004633 0.083262 0.044442 0.079687 Skewness -0.356555 2.097394 3.608832 -0.224925 -0.582515 Kurtosis 3.868989 12.02896 37.86669 4.026334 4.942843 Jarque-Bera 8.582407 673.1783 8610.345 8.528469 34.85437 Probability 0.013688 0.000000 0.000000 0.014063 0.000000 Sum 2.039350 0.596486 -1.145074 0.570397 2.544001 Sum Sq. Dev. 1.595391 0.003477 1.123086 0.319961 1.028703 Observations 163 163 163 163 163 Finland

Mean 0.007045 0.001941 -0.002187 0.003141 0.008654 Median 0.018767 0.001162 0.000000 0.003591 0.008874 Maximum 0.388811 0.015303 0.187334 0.086196 0.287158 Minimum -0.369537 -0.007782 -0.289190 -0.095581 -0.382113 Std. Dev. 0.102149 0.003523 0.057169 0.019664 0.092138 Skewness 0.053503 0.586612 -0.627728 -0.549527 -0.391353 Kurtosis 4.640257 3.955633 5.995382 9.601646 4.713760 Jarque-Bera 28.03213 23.75553 109.4405 464.6927 36.82711 Probability 0.000001 0.000007 0.000000 0.000000 0.000000 Sum 1.754316 0.483357 -0.544578 0.782113 2.154839 Sum Sq. Dev. 2.587721 0.003078 0.810523 0.095896 2.105363 Observations 249 249 249 249 249 France

Oil Price Changes and Stock Markets in Energy Exporting and Importing Countries

Master’s Thesis International Business and Management

& International Financial Management