Oil Prices and Equity Returns in the BRIC Countries - SSRN

The World Economy (2009) doi: 10.1111/j.1467-9701.2009.01194.x

Oil Prices and Equity Returns in the BRIC Countries

Oxford, World TWEC © 1467-9701 0378-5920 Original XXX OIL RAMAPRASAD 2009 PRICES Economy The UK Article Author AND BHAR EQUITY Journal compilation RETURNS BILJANA NIKOLOVA © INBlackwell BRIC COUNTRIES Publishers Ltd. 2009 Blackwell Publishing LtdAND

Ramaprasad Bhar and Biljana Nikolova University of New South Wales importers net exporters of oil.by It also concludes despite theinfluence aggressive growth the BRIC the past 25 years, the volatility stock returns in these economies not have significant impact volatility of The global oil price returns. This paperormeasures ABSTRACT the level which global oilthat price returns theeconomic stock returns andofvolatility in countries the BRIC in equity markets and observes theoftime-varying conditional correlationdoes between BRICa equity returns andon oilthe price returns. study concludes that the level of impact of oil price returns on equity returns and volatility in the BRIC countries depends on the extent to which these countries arenet

1. INTRODUCTION

O

IL has played a significant role in the economic and political development of the industrialised countries in the world. Oil price shocks are an important determinant of the future economic growth and stability of the developing countries of today. The economic impact of higher oil prices on developing countries is generally more severe than that for industrialised countries. This is mainly because of the energy intensiveness of these economies as they experience a rapid economic growth and, generally, energy is used less efficiently. According to the International Energy Agency report (2004), on average, developing countries use more than twice as much oil to produce a unit of economic output as do OECD countries. Economic liberalisation and integration of international markets is characterised by an increased level of capital flow and international investment in emerging markets. Given the oil intensity of the emerging economies of today, it is important for global portfolio investors to understand the level of susceptibility of stock prices in these markets to movements in global oil prices. Following the Asian and Russian financial market crisis in the late 1990s, Brazil, Russia, India and China (‘BRIC’) have emerged among the largest countries in the world in both demographic and economic terms. These countries have seen their share in global GDP rise from 15 per cent in 1995 to 22 per cent in 2007.1 In financial terms, the BRIC countries dominate the emerging market economies of today (Jensen and Larsen, 2004). According to the Energy Information Administration (EIA) Report (2007), 18 per cent of the total annual oil demand in 2006 (84.6 million barrels per day) came from the BRIC countries. Approximately 23 per cent of the total annual oil demand in 2030 (anticipated to increase to 117.6 million barrels per day) is expected to come from these four countries, with 13.4 per cent coming from China alone.

1

GDP measured at purchasing power parity. Source: IMF, Euromonitor International.

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© 2009 The Authors Journal compilation © 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1037 In view of the increasing significance of the BRICs as an integral part of the global economy and the emergence of these markets as major oil consumers going forward, this paper concentrates on studying the impact of oil price dynamics on the first- and second-order moments of equity returns in the BRIC countries and vice versa. More specifically, the paper conducts a return and volatility spillover analysis of the price discovery and volatility transmission between the oil market and BRIC equity markets. In addition, it focuses on documenting and explaining the time-varying conditional correlation between equity market returns in the BRIC countries and international oil price returns. The approach taken to measure this bilateral relationship is the dynamic bivariate EGARCH model, as developed by Nelson (1991). To the best of our knowledge, price and volatility spillover effects between oil prices and BRIC equity returns have not been reported in the literature to date. Such study is considered very beneficial as it provides insights regarding information transmission and volatility estimation of oil price returns and BRIC equity returns and pricing of BRIC equities. While there are other factors that could potentially affect the price discovery process and volatility of BRIC equity returns, this paper specifically concentrates on the relationship between oil price and BRIC equity returns. The remainder of the paper is organised as follows: Section 2 covers the literature review; Section 3 provides information on the economics of oil prices and oil consumption in each of the BRIC countries; Section 4 discusses the time series properties of the data; Section 5 presents the model used for the purpose of this study; Section 6 covers the discussion of the results; and Section 7 concludes.

2. LITERATURE REVIEW

During the last decade and a half, there has been an increase in published research on the relationship between oil and stock prices. Huang et al. (1996) use a vector autoregression (VAR) methodology to assess the relationship between oil future returns and US stock returns, and find no evidence of correlation between them. Sadorsky (1999) uses vector autoregression (VAR) to investigate the interaction between spot oil prices, stock returns and economic activity. The results of this study suggest that oil price and oil price volatility both play important roles in affecting real stock returns, with evidence of increasing impact since 1986. There is also evidence that oil price volatility has asymmetric effects on the economy. Basher and Sadorsky (2006) use an international multi-factor model, which allows for unconditional and conditional factors, to study the impact of oil price changes on a large set of emerging stock market returns. They find strong evidence that oil price risk impacts stock price returns in emerging markets. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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RAMAPRASAD BHAR AND BILJANA NIKOLOVA

A well-documented empirical finding in the finance literature is the asymmetric impact of news on volatility transmission (see Bae and Karolyi, 1994; Koutmos and Booth, 1995; Booth et al., 1997). Several economic theories have been proposed to explain the mechanism generating this asymmetry including leverage effects (Black, 1976), stochastic bubbles models (Chen et al., 2001) and investor heterogeneity theory (Hong and Stein, 2003). In the past decade, numerous authors have used the EGARCH approach to capture the high degree persistence in the conditional mean and variances of asset prices at high frequency levels and to capture the asymmetric volatility of equity returns (Abhyankar et al., 1995; Koutmos and Booth, 1995; Kanas, 1998; Kassimatis, 2002; Kim et al., 2005, among others). To date, the bivariate EGARCH methodology has been used to test stock return volatility spillovers between or within equity markets (see Kanas, 1998; Reyes, 2001; Miyakoshi, 2003; Kim et al., 2005), volatility spillovers between equity markets and futures index markets (see Tse and Booth, 1996; Tse, 1999; Bhar, 2001) and volatility spillovers between stock returns and exchange rate changes (Kanas, 2000). This study uses the bivariate EGARCH model to extend the return and volatility spillover analysis to the price discovery and volatility transmission between the oil market and BRIC equity markets.

3. ECONOMICS OF OIL PRICES AND OIL CONSUMPTION IN THE BRICs

Oil prices have a different impact on oil-importing countries from oil-exporting countries. The level of vulnerability of oil-importing countries to higher oil prices depends on the degree to which the country is a net importer and the oil intensity of the economy. Oil prices affect the oil-importing countries’ economy through the supply side, demand side and the terms of trade. In case of oil price increase, supply suffers as production costs for non-oil-producing companies rise, and in the absence of fully passing these costs on to consumers, this will result in reducing profits and dividends, which are key drivers of stock prices. On the demand side, oil price increases drive up the general level of prices, which translates into lower real disposable income, and consequently reduces demand. Besides the direct impact on general price levels, oil prices also have secondary effects on wage levels, which in combination with high general prices result in increased inflation. Inflationary pressures are usually controlled by central banks through increase in interest rates. Given the higher interest rates, bond investments will become more attractive than stock investments, which will result in lower stock prices. Finally, increasing import prices trigger a deterioration of the terms of trade and therefore impose welfare losses. Oil-exporting countries, on the other hand, benefit from higher export revenues, which could be diminished by a decline in a global oil demand. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1039 a. Brazil Brazil was a net importer of crude oil until April 2006, when it celebrated the achievement of self-sufficiency. According to the International Energy Agency (‘IEA’), in calendar year 2006 Brazil accounted for approximately 2.6 per cent of the world’s annual oil consumption and 2.9 per cent of the world’s annual oil production. Brazil achieved its oil sufficiency through a combination of increased investment in oil exploration and the introduction of ethanol as an alternative source of energy (Reel, 2006). It is expected that self-sufficiency and the increased focus on ethanol production will help protect Brazil from future international energy crises and contribute to managing excessive volatility in the world commodity market. However, although the economy will produce the same volume of oil as it consumes, Brazil will still depend on light oil imports because the country’s refining profile is unable to process all of the domestically produced heavy oil (The Office of Global Energy Dialogue, 2006). b. Russia Russia has historically been a net exporter of crude oil, ranked number two in the world after Saudi Arabia in 2006. Following the collapse of the Soviet Union in 1991, Russia’s oil production fell sharply from a high of 11.4 billion barrels per day in 1987, to a low of 6.4 million barrels per day in 1994. Oil production remained at this level during the financial crisis in 1998, and saw a recovery only in late 1999 and early 2000. The country produced 9.8 million barrels per day in 2006. Russia’s economy continues to be heavily dependent on oil and natural gas exports, making it vulnerable to fluctuations in world oil prices. c. India India has historically been a net oil importer. According to EIA estimates, India was the sixth largest consumer of oil during 2006. Indian oil production accounted for 1.1 per cent of the world’s annual oil production and 2.9 per cent of the world’s annual oil consumption in calendar year 2006 (EIA, 2006; IEA, 2006). The combination of rising oil consumption and fairly stable production levels makes India increasingly dependent on imports to meet consumption needs. EIA estimates that India registered oil demand growth of 100,000 barrels per day during 2006. To help meet growing oil demand, India has promoted various exploration and production projects over the last several years in an effort to boost domestic oil production. In recent years, Indian national oil companies have acquired equity stakes in overseas exploration and production companies in Africa, Asia, Latin America and the Middle East. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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d. China China has been a net oil importer since 1993. Chinese oil production accounted for 5.2 per cent of the world’s annual oil production and 8.6 per cent of the world’s annual oil consumption in calendar year 2006 (EIA, 2006; IEA, 2006). According to the EIA, China was the second largest consumer of oil after the United States in 2006 and the third largest importer of oil after the United States and Japan. China alone accounted for 38 per cent of the increase in demand for oil in 2006. With China’s expectation of growing future dependence on oil imports, and in an attempt to secure a level of certainty and reliability of future oil supply, the country has been acquiring interests in exploration and production abroad. To limit the problem of extreme dependence on the Middle East, while at the same time guaranteeing supplies from that region, China has used trade agreements and the acquisition of oil interests to establish closer ties with several producer countries and regions such as Russia, Central Asia, Sudan, Iran, Venezuela and Myanmar, among others.

4. DATA AND PRELIMINARY STATISTICS

Data used in this paper are weekly closing equity market price indices expressed in local currencies for four emerging markets: Brazil (Bovespa), Russia (AK&M Composite), India (Sensex) and China (Shanghai Composite), and weekly West Texas Intermediate (WTI)2 crude oil prices expressed in US dollars. The data are sampled weekly (Wednesdays) over the period January 1995 to February 2007. The selected period is effectively a collective post-liberalisation period for all of the BRIC countries (Bekaert et al., 2002), characterised by increased levels of industrialisation and economic development. Weekly (Wednesday) price series data have been used to avoid non-synchronous trading and day-of-the week effects, as discussed in Ramchand and Susmel (1998), Aggarwal et al. (1999) and Ng (2000). The data were sourced from Bloomberg. Weekly equity index returns and oil price returns were calculated as a log difference between current price and previous period price for the indices and the oil price. Summary statistics for the weekly index and oil price returns are presented in Table 1. The average weekly returns for Brazil, Russia, India and China are 0.2454, 0.7849, 0.1504 and 0.2543 respectively, and the standard deviations are 5.9149, 6.4034, 4.1837 and 3.9895. The skewness and excess kurtosis indicate that negative shocks are more common than positive for Brazil, Russia and India, and positive shocks are more common for China. Shapiro–Wilk and skewness 2 WTI is a light, sweet crude oil. It is the underlying commodity of the New York Mercantile Exchange’s oil futures contracts. WTI is considered a ‘sweet’ crude because it contains 0.24 per cent sulphur, a higher concentration than the North Sea Brent crude.

© 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1041 TABLE 1 Summary Statistics for Weekly Equity Index Returns and Oil Price Returns

Brazil Russia India China Oil

Mean

Std. Dev.

ρ1

Q1(20)

ρ2

Q2(20)

Skewness

Kurtosis

0.2454 0.7849 0.1504 0.2543 0.1934

5.9149 6.4034 4.1837 3.9895 4.8983

−0.0816 0.0127 0.0057 −0.0446 −0.0539

37.2385 34.1152 37.8401 25.3295 46.1953

0.2245 0.4047 0.0250 0.1189 0.0523

167.5125 342.5806 108.9891 61.9097 30.1709

−0.8325 −0.4608 −0.3732 0.2135 −0.4097

7.4683 13.1352 11.4337 8.0343 4.0835

Notes: Data used are weekly equity index returns and oil price returns for the period January 1995 to February 2007. Q1(20) refers to the Portmanteau statistic with the null hypothesis of no data series serial correlations measured with a lag of 20. Similarly, Q2(20) Sq refers to the same test with squared data series. Large p-value entries would indicate that there are no serial correlations in the data series.

and kurtosis normality tests were conducted and the results of both confirm that all return series are not normally distributed. The first-order autocorrelation test results for the WTI and BRIC equity index returns and the Portmanteau tests for serial correlation for the returns and the squared value of returns confirm that there is persistence of non-linear dependence and there is a presence of conditional heteroscedasticity in the returns of all variables in the sample, which justifies the use of a model from the ARCH family.

5. MODEL

The purpose of this paper is to determine the impact of oil price returns in the equity price creation process in the BRICs and to analyse the impact of oil price volatility on volatility of equity returns in the BRIC countries and vice versa. Financial markets as well as oil prices are in a constant motion and co-movements between these financial parameters fluctuate on a daily basis. The parsimonious bivariate AR(1)–EGARCH(1,1) model used in this study allows for asymmetric response characteristics in stock and oil price returns and has been specified to incorporate time-varying correlations. These features are well suited to capture the dynamic relationship between oil price and stock returns. It should be noted that the model has a restriction in the mean equation for oil price returns. It assumes that BRIC equity prices do not affect oil prices, as oil shocks are exogenous events and causes can usually be attributed to historical events (Hamilton, 1985). Some relevant exogenous events from more recent times are: the Iraq invasion of Kuwait in 1990, the terrorist attacks of 11 September 2001, the American war with Iraq in 2003, and other events. Restrictions in the volatility equation for oil price returns are not imposed, as the BRIC countries represent a significant part of the global oil consumption. Hence the model is constructed to identify and measure the presence of volatility spillovers from the BRIC equity markets to global oil prices. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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a. Model Specification A brief description is provided of the bivariate AR(1)–EGARCH(1,1) model with time-varying correlations relating the equity returns from the BRIC countries and the oil price changes. We denote the return from one of the BRIC countries by rj,t, where the subscript j represents one of the BRIC index returns, and by roil,t the oil price change. The mean spillover effect is captured by the following relationship: ⎡ rj , t ⎤ ⎡ β j ,0 ⎤ ⎡β j ,1 β j , 2 ⎤ ⎡ rj , t−1 ⎤ ⎡ ε j , t ⎤ ⎢r ⎥ = ⎢ ⎥⎢ ⎥+⎢ ⎥, ⎥+⎢ ⎣ oil, t ⎦ ⎣βoil,0 ⎦ ⎣ 0 βoil, 2 ⎦ ⎣roil, t−1 ⎦ ⎣ε oil, t ⎦

(1)

⎡ ε j,t ⎤ ⎢ε ⎥ Ω t ~ N 0, ∑t . ⎣ oil, t ⎦

(2)

where

(

)

As mentioned above, we assume that stock returns do not affect oil price changes, but oil price changes do affect stock returns as expressed in equation (1). Ωt indicates all relevant information known at time t, and ∑t is the time-varying covariance matrix defined below. The diagonal elements of the (2 × 2) covariance matrix are given by: ln(σ 2j , t ) = α j , 0 + α j ,1 f1(z j , t−1) + α j , 2 f2 (zoil, t−1) + γ j ln(σ 2j , t−1)

(3)

2 2 ln(σ oil , t ) = α oil , 0 + α oil ,1 f1( z j , t −1) + α oil , 2 f2 ( zoil , t −1) + γ oil ln(σ oil , t −1).

(4)

and

In equations (3) and (4), f1 and f2 are functions of standardised innovations. These innovations are defined as zj,t = εj,t /σj,t and zoil,t = εoil,t /σoil,t. The functions f1 and f2 capture the effect of sign and the size of the lagged innovations as: f1(zj,t−1) = | zj, t−1 | − E(| zj, t−1 |) + δj zj, t−1,

(5)

f2(zoil, t−1) = | zoil, t−1 | − E(| zoil,t−1 |) + δoilzoil,t−1.

(6)

The first two terms in equations (5) and (6) capture the size effect and the third term measures the sign effect. If δ is negative, a negative realisation of zt−1 will increase the volatility by more than a positive realisation of equal magnitude. Similarly, if the past absolute value of zt−1 is greater than its expected value, the current volatility will rise. This is called the leverage effect and is documented by Black (1976) and Nelson (1991) among others. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1043 The asymmetric effect of standardised innovations on volatility may be measured as derivatives from equations (5) and (6): ⎛ 1 + δi ∂ fi (zit )/ ∂ zit = ⎜ ⎝ −1 + δ i

zi > 0⎞ ⎟. zi < 0⎠

(7)

Relative asymmetry is defined as | −1 + δi |/(1 + δi). This quantity is greater than, equal to, or less than 1 for negative asymmetry, symmetry and positive asymmetry, respectively. The persistence of volatility may also be quantified by an examination of the half life (HL), which indicates the time period required for the shocks to reduce to one-half of their original size: HL =

ln(0.5) . ln | γ i |

(8)

The off-diagonal elements of the covariance matrix Σt are defined in a manner similar to that in Darbar and Deb (2002). The key is to define a time-varying conditional correlation which, when combined with the conditional variances given in equations (3) and (4), generate the required conditional covariance. The conditional correlation is allowed to depend on the lagged standardised innovations and is transformed using a suitable function so that it lies between (−1, 1). This is given by the following equation:

σ j ,oil,t = ρ j ,oil,tσ j ,tσ oil,t , ⎞ ⎛ 1 ρ j ,oil,t = 2⎜ ⎟ − 1, ⎝ 1 + exp(−ξt ) ⎠

ξt = c0 + c1z j ,t−1zoil,t−1 + c2ξ t−1.

(9)

Although the function ξt may be unbounded, the sine function transformation will restrict it to the desired range for correlation. For a given pair of return series the 18 parameters to be estimated is conveniently labelled as: Θ ≡ (βj,0, βj,1, βj,2, βoil,0, βoil,2, αj,0, αj,1, αj,2, γj, δj, αoil,0, αoil,1, αoil,2,

γoil, δoil, c0, c1, c2).

(10)

The estimation of these parameters is achieved by numerical maximisation of the joint likelihood function under the distributional assumption of this model. If the sample size is T then the log-likelihood function to be maximised with respect to the parameter set Θ is: © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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RAMAPRASAD BHAR AND BILJANA NIKOLOVA T

T

t =1

t =1

L(Θ) = −T ln(0.5π ) − 0.5∑ ln ∑t − 0.5∑[ε j ,t

⎡ε ⎤ ε oil,t ] ∑ −t 1 ⎢ j ,t ⎥. ⎣ε oil,t ⎦

(11)

6. EMPIRICAL RESULTS

The parameters of the model are estimated by the numerical maximisation of the above discussed joint likelihood function with the algorithm developed by Berndt, Hall, Hall and Hausman (1974; BHHH in GAUSS™) without any parameter restrictions imposed. Table 2 reports several diagnostic checks, including the 20th-order serial correlation in the level and squared standardised innovations as well as the asymmetry test statistics following Engle and Ng (1993). The Ljung–Box statistics indicate the absence of linear dependence in the standardised innovations for all equity markets, and indicates a potential for linear dependence of the WTI residuals. However, we are still comfortable with the outcomes of the test, as the non-randomness can still be rejected at the 90 per cent confidence level. The validity of the Ljung–Box test is confirmed by the Engle and Ng test, which confirms that there are no sign biases, that is, there is no asymmetry effect. The above tests indicate a good fit of the bivariate AR(1)– EGARCH(1,1) model to the available dataset. a. Mean and Volatility Spillover Effects Based on the results for each of the BRIC countries presented in Tables 3–6, and as indicated by the βj,1 and βj,2 coefficients, the returns in the Brazilian, TABLE 2 Diagnostic Tests (BRIC Equity Market Returns and WTI Price Returns) Brazil

Russia

India

China

WTI

0.954 0.253 0.081

0.028 0.530 0.186

0.253 0.979 0.415

0.010 0.371 0.227

p-Values for Engle and Ng (1993) diagnostic tests Sign bias test 0.940 0.683 Negative size bias test 0.751 0.836 Positive size bias test 0.974 0.090 Joint test 0.224 0.324

0.745 0.433 0.922 0.563

0.775 0.504 0.316 0.496

0.484 0.621 0.353 0.876

p-Values for Ljung–Box Q(20) statistics z 0.416 z2 0.997 z1.z2 0.224

Notes: z represents the standardised residual for the corresponding equation, i.e. either country index return or world index return. z1.z2 indicates product of the two standardised residuals. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1045 TABLE 3 Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation: Brazilian Equity Market Returns and WTI Price Returns Brazil Mean equation βj,0 βj,1 βj,2 Variance equation αj,0 αj,1 αj,2 γj δj Correlation function c0 c1 c2 Half life Relative asymmetry

WTI

0.0031 (1.75) 0.0346 (0.79) 0.0042 (0.13)

βoil,0

0.0020 (1.03)

βoil,2

−0.0387 (−0.96)

−0.2913 (−3.59) 0.2447 (6.70) 0.0332 (0.98) 0.9500 (70.58) −0.3894 (−2.76)

αoil,0 αoil,1 αoil,2 γoil δoil

−7.3686 (−2.49) −0.0264 (−0.31) 0.2229 (2.37) −0.2189 (−0.45) 0.3624 (1.39)

0.0782 (0.70) 0.0379 (0.57) −0.5481 (−14.60) 13.5134 1.2755

0.4563 1.0442

Notes: ln(σ 2j , t ) = α j ,0 + α j ,1 f1(z j , t−1) + α j ,2 f2 (zoil, t−1) + γ j ln(σ 2j , t−1). 2 2 ln(σ oil , t ) = α oil,0 + α oil,1 f1(zoil, t−1) + α oil,2 f2 (zoil, t−1) + γ j ln(σ oil, t−1).

The numbers in parentheses indicate t-statistics. Half life represents the time it takes for the shocks to reduce its impact by one-half. Relative asymmetry may be greater than, equal to or less than one indicating negative asymmetry, symmetry and positive asymmetry, respectively.

TABLE 4 Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation: Russian Equity Market Returns and WTI Price Returns Russia Mean equation βj,0 βj,1 βj,2 Variance equation αj,0 αj,1 αj,2 γj δj Correlation function c0 c1 c2 Half life Relative asymmetry

0.0058 (20.46) 0.1338 (27.00) 0.0691 (20.99) −0.3331 (−34.08) 0.2691 (8.34) 0.1208 (5.77) 0.9399 (10,975.21) −0.0711 (−1.21)

WTI

βoil,0

0.0023 (1.26)

βoil,2

−0.0160 (−0.72)


−4.7923 (−217.33) 0.1061 (1.37) 0.1956 (7.63) 0.2062 (24.82) 0.5062 (91.58)

0.2258 (14.49) −0.0773 (−3.92) −0.8450 (−1,544.63) 11.1831 1.1531

Notes: See Table 3. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

0.4390 0.5316

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RAMAPRASAD BHAR AND BILJANA NIKOLOVA TABLE 5 Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation: Indian Equity Market Returns and WTI Price Returns India

Mean equation βj,0 βj,1 βj,2 Variance equation αj,0 αj,1 αj,2 γj δj Correlation function c0 c1 c2 Half life Relative asymmetry

WTI

0.0045 (3.74) 0.0079 (0.18) 0.0081 (0.50)

βoil,0

0.0028 (1.58)

βoil,2

−0.0485 (−1.61)

−2.0176 (−2.43) 0.3345 (3.76) −0.2464 (−1.57) 0.6859 (5.14) −0.1789 (−1.22)


−0.4136 (−1.59) 0.0865 (1.35) 0.1169 (2.06) 0.9306 (21.45) 0.1900 (0.68)

0.0169 (0.39) −0.1311 (−2.30) 0.4780 (2.30) 1.8385 1.4358

9.6369 0.9865

Notes: See Table 3.

TABLE 6 Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation: Chinese Equity Market Returns and WTI Price Returns China Mean equation βj,0 βj,1 βj,2 Variance equation αj,0 αj,1 αj,2 γj δj Correlation function c0 c1 c2 Half life Relative asymmetry

WTI

0.0018 (1.72) −0.0337 (−1.61) 0.0082 (0.41)

βoil,0

0.0022 (1.31)

βoil,2

−0.0491 (−1.60)

−0.2675 (−2.66) 0.2151 (2.94) 0.0068 (0.05) 0.9589 (68.00) 0.2110 (1.73)


−0.4420 (−2.32) −0.0860 (−1.42) 0.0991 (1.73) 0.9270 (30.62) 0.2390 (0.76)

0.0648 (0.93) 0.0226 (0.31) 0.2789 (3.49) 16.5159 0.6515

9.1442 0.6284

Notes: See Table 3. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1047 Indian and Chinese equity markets are not affected by the countries’ previous equity returns, nor by return spillovers from the oil market. According to Bhar and Nikolova (2007), equity returns in the BRIC countries are mainly determined by their respective regional return trends and to a lesser degree by world equity prices. Returns in the Russian equity market, on the other hand, are largely determined by its own past returns and to a lesser degree by oil price return spillovers. Unlike the other BRIC countries, Russia has historically been a net exporter of oil and income from oil production represents a significant part of Russia’s national income. According to the IMF and World Bank, the oil and gas sector in Russia represents 20 per cent of the national income, hence the relationship between global oil price fluctuations and Russian financial market returns. The positive sign of the βj,2 coefficient indicates a positive relationship between equity prices in the Russian market and WTI oil prices. Parameters αj,1 and αoil,2 capture the impact of the market’s own lagged standardised innovations on the conditional volatility for each of the BRIC and WTI markets. Both parameters are statistically significant for all BRIC countries and the WTI, which indicates that the level of volatility in these markets depends on their respective lagged standardised innovations in the previous period. In other words, the results imply that volatility levels from the previous period are good indicators and determinants of current volatility levels. It should be noted that the BRIC countries’ past innovation coefficients are somewhat higher than the WTI coefficients. This might be related to the fact that, besides their own past innovations, oil price returns are quite largely affected by international geopolitical events and global macroeconomic factors. The asymmetric effect from past unexpected shocks on volatility of returns is represented by the δ coefficient. The results of our study, as reported in the country and WTI tables, indicate that this parameter is statistically significant only for Brazil and China and implies asymmetry and symmetry of volatility of equity returns for the two countries respectively. While the asymmetric volatility of returns is a well-researched and relatively common characteristic of return time series, symmetric volatility is somewhat more unique. The symmetric volatility of returns in China could potentially be explained and related to the relatively high investor homogeneity, low level of risk averseness and investor confidence (see Hong and Stein, 2003). Despite the fact that the Chinese economy went through the process of financial markets liberalisation in 1993 (Bekaert et al., 2003), it is still considered heavily regulated. Government regulations restrict Chinese investors to domestic investments only and impose limited access to the Chinese equity market for foreign investors. Chinese investors are proven to be highly speculative and risk loving. Gao (2002) shows that the annual average turnover ratio, a commonly used procedure to measure the degree of speculation, was more than 500 per cent between 1994 and 2001. This should be compared to figures in the range of 30–70 per cent for more developed countries. The © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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speculative nature of Chinese investors together with relatively high domestic investor confidence, based mainly on the belief of government intervention in support of Chinese public companies, could arguably be a possible explanation of the symmetric volatility of returns in the Chinese stock exchange. Investors might view past negative unexpected shocks as only temporary movements, expected to be corrected either via market forces or government intervention, or might be seen as speculative and opportunistic events and might not necessarily trigger increased volatility in market returns. The persistence in volatility is measured by the parameters γj and γoil. The values of γ are less than one for all BRIC equity markets and the WTI, which is a necessary condition for the volatility process to be stable. The magnitude of the γ parameters suggests the tendency for the volatility shocks to persist. Using the HL parameter, the volatility persistence can be expressed in terms of weeks. Based on the HL results for the BRICs, the Chinese equity market takes the longest to reduce the impact from its shocks by half (16.5 weeks) and the Indian market takes the least time (1.8 weeks), which suggests that India has the lowest level of volatility persistence out of all BRIC countries. Parameters αj,2 and αoil,1 capture the impact of cross-market standardised innovations for the BRIC equity markets and WTI. Based on the results, the conditional volatility of the Brazilian equity markets is not affected by past innovations in WTI oil prices. Brazil has historically been a net importer of crude oil. However, oil consumption for the larger part has been met by local oil and ethanol production, which has made the country less dependent on external oil supply and less susceptible to changes in international oil prices. Brazil’s oil sufficiency in 2006 has contributed further to its relative immunity to changes in international oil prices. Brazil has aspirations towards becoming a net oil exporter. However, this may result in greater dependence of Brazil’s equity prices on global oil price dynamics, as a larger proportion of the country’s national income would become represented by revenues from oil exports. An increase in global oil prices will result in increased Brazilian oil export revenues, which will translate into increased share prices. The conditional volatility of the Russian, Indian and Chinese equity markets, on the other hand, are affected by past innovations in WTI price returns. The relationship between past oil price return innovations and volatility of Russian equity price returns is positive, while the relationship between past oil price return innovations and volatility of Chinese and Indian equity price returns is negative. This can be explained by the historical net oil exporter position of Russia, and net oil importer position of China and India. This means that an increase in global oil prices results in increased Russian oil export revenues, which then translates into increased share prices, hence increased share price returns. On the other side, an increase in oil prices for China and India means higher oil import prices. This has a negative impact on cash flows of businesses © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1049 and their ability to pay dividends to shareholders, and effectively translates into lower stock prices and consequently lower stock price returns. The results for the oil price return variance equation, which allows for volatility spillovers from the BRIC equity markets to the oil price returns, show that while the oil price return volatility depends on past innovations in oil price returns, there is no evidence of volatility spillover effects from any of the BRIC equity markets to the WTI oil price returns. This indicates that despite the BRIC’s aggressive economic growth in the past 25 years, the volatility of stock price returns in these countries does not have a significant impact on the volatility of global oil price returns. b. Time-varying Conditional Correlation The estimated dynamic conditional correlation between the BRIC countries’ equity returns and the WTI oil price returns are displayed in Figures 1– 4. The correlation between BRIC equity and oil price returns is measured by the c1 FIGURE 1 Time-varying Conditional Correlation between Brazilian Equity Returns and Oil Price Returns (Jan. 1985–Feb. 2007)

FIGURE 2 Time-varying Conditional Correlation between Russian Equity Returns and Oil Price Returns (Jan. 1985–Feb. 2007)


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FIGURE 3 Time-varying Conditional Correlation between Indian Equity Returns and Oil Price Returns (Jan. 1985–Feb. 2007)

FIGURE 4 Time-varying Conditional Correlation between Chinese Equity Returns and Oil Price Returns (Jan. 1985–Feb. 2007)

coefficient. The results indicate that there is no statistically significant evidence that information arrival in the Brazilian equity market or oil market is subsequently transmitted to the conditional correlation between the two (see Figure 1). This is most likely due to the ability of Brazil to meet the majority of its domestic oil demand through local oil and ethanol production and the significance of other internal political and regulatory events which have largely shaped the development of the equity market in Brazil. Unlike Brazil, the information shocks significantly impact the conditional correlation between Russian equity market returns and global oil price returns. The relationship is statistically significant and at times negative, as evident in Figure 2. Russia has historically been a net oil exporter. The heightened concerns about security of supplies from the Middle East seem to have contributed towards Russia’s more dominant role in the international geopolitical scene. The positioning of Russia as a comparably reliable supplier of oil, especially in times of turmoil in the Middle East, could be a viable explanation of the evidence of a periodical negative relationship between global oil prices and Russian equity prices. © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1051 The level of conditional correlation becomes more significant in the second half of 1998, which coincides with the period Russia started to suffer from the effects of the Asian financial markets crisis. Russia faced severe cash flow problems as investors withdrew their funds from the government debt market and as international reserves dropped precipitously. In addition, the rouble was floated in early September 1998 (Goldfajn and Baig, 2000). The negative conditional correlation between Russian equity returns and oil returns coincides with the period after 11 September 2001, and after the commencement of the US war with Iraq in 2003. Oil prices declined sharply following the 11 September 2001 terrorist attacks on the United States, largely on increased fears of a sharper worldwide economic downturn and significantly lower oil demand. Also, the military actions in Iraq on 19 March 2003 resulted in a fall in oil prices, as uncertainty around global economic conditions increased. While these two events had a temporary negative impact on Russian equity prices, the effect was much more short-lived than the effect on global oil prices. While Russia depends highly on the level of global oil prices, the perceived stability of supply of oil produced in Russia compared to the instability in the Middle Eastern region resulted in increased demand for Russian oil. British Petroleum (BP) reported an increase in oil production in Russia in 2001/02 of 9.1 per cent, while oil production in the Middle East decreased by 6.1 per cent in the same period. Also, while oil production in Russia increased by a further 8.7 per cent in calendar year 2003 compared to 2002, the level of oil production in Iraq declined by 34.2 per cent during the same period. The time-varying conditional correlation of Indian equity market index returns with oil price returns, while not as strong as evidenced in the Russian equity market, is statistically significant, and as evident in Figure 3, it is often negative. The negative spikes are mainly evident in 1998, 2000, 2001 and 2003. The negative conditional correlation between India and the oil price returns in 1998 can most likely be related to the fact that the Indian economy was relatively unaffected by the South Asian crisis. In addition, India was in quite a unique position during this time as the Group of Seven (G7) imposed sanctions on the country following their nuclear tests conducted in 1998, and the subsequent downgrade of India’s sovereign rating from investment grade to speculative. The year 2000 is characterised by a sharp increase in oil prices due to increased world demand for oil and OPEC production cuts, oil prices plummeted following the 11 September terrorist attacks on the United States and another decrease in oil price following the commencement of the war with Iraq in 2003. While the level of oil price returns was quite severely affected by these events, the impact did not seem to be translated onto Indian equity returns to the same extent. The results for the Chinese equity market index returns, and as evident in Figure 4, show a very nominal, statistically insignificant conditional correlation with the oil price returns. This can be explained by the relatively closed nature © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

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of the Chinese financial system. Unlike the other emerging market economies, regionally and globally, the financial liberalisation in China is characterised by a gradual decline in the state sector and a steady growth of importance of collective, individual and foreign enterprises. In addition, the Asian financial crisis of 1997–98 did not exert a negative effect on China. China has, in fact, absorbed a considerable amount of foreign direct investment that could otherwise have been channelled to neighbouring Asian economies. Overall, the results indicate a low level of dependence between equity returns in China and international oil prices.

7. CONCLUSION

The level of impact of oil price returns on equity returns and volatility in the BRIC countries depends on the extent to which these countries are net importers or net exporters of oil. Brazil was a net oil importer until 2006. The ability of Brazil to meet the majority of its oil demand through local oil and ethanol production has made the country less vulnerable to global oil price movements relative to other net oil-importing countries. Brazil achieved oil self-sufficiency in 2006 and has aspirations to become a net oil exporter in the near future. As much as the increase in oil exports will have a positive effect through higher export revenues, it will make equity prices and returns in Brazil more susceptible to changes in global oil prices. Both India and China have historically been net importers of oil. While dynamics in the oil price returns do not impact the price creation process of equities in these markets, there is evidence that past innovations in oil price returns do affect the conditional volatility of equity returns in both the Indian and Chinese equity markets, and this relationship is negative. As net oil importers, India and China have to pay higher oil import prices when global oil prices increase. The higher import prices have a negative impact on cash flows of businesses and their ability to pay dividends to shareholders, which effectively translates into lower stock prices. There is also evidence of nominal, and at times negative, time-varying conditional correlation between oil price returns and equity returns in India, and statistically insignificant time-varying conditional correlation between oil price returns and equity returns in China. Both India and China are quite unique in a sense that they were largely unaffected by the Asian financial markets crisis. Also, there are macroeconomic factors that have had a strong impact over equity returns and volatility in these equity markets. These factors appear to have had a much greater role in shaping the equity price dynamics in these countries than global oil price movements. Unlike the other BRIC countries, Russia has historically been a net exporter of oil. There is a strong relationship between Russian equity and global oil price © 2009 The Authors Journal compilation © Blackwell Publishing Ltd. 2009

OIL PRICES AND EQUITY RETURNS IN BRIC COUNTRIES 1053 returns. Both, equity returns and the conditional volatility of returns are largely determined by oil price return spillovers. What comes as a surprise is the relatively frequent negative time-varying conditional correlation between Russian equity returns and global oil price returns. The level of oil production in Russia increased after 11 September 2001 and the commencement of the US war with Iraq, while the rest of the major oil-producing countries experienced significant cuts in their production quotas. Russia showed political and economic resilience during times of heightened concerns about continued availability of supplies from the Middle East and was perceived as a reliable supplier of oil for the developed and developing world economies. This has pushed the country further to the forefront of the international geopolitical scene, and this position is expected to strengthen even further as the country continues to invest in oil production projects. The impact of high oil consumption from developing economies on global oil prices, especially the BRICs, seems to be an area of both interest and concern for a number of finance professionals. The findings of this study conclude that despite the BRIC’s aggressive economic growth in the past 25 years, the volatility of stock returns in these countries does not have a significant impact on the volatility of global oil prices.

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