Oil Prices and BRIC Countries

8/6/2019 Oil Prices and BRIC Countries

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Oil Prices and Equity Returns in the BRIC Countries

Biljana Nikolova

ANZ Banking Group Ltd

Institutional Banking

Level 1, 20 Martin Place

Sydney NSW 2000, AUSTRALIA

Email: [email protected]

Ramaprasad BharSchool of Banking and Finance

The University of New South Wales

Sydney 2052, AUSTRALIA

Email: [email protected]

Abstract:

This paper measures the level by which global oil prices influence the stock price

creation process and volatility in the BRIC equity markets, and observes the time

varying conditional correlation between BRIC equity returns and oil prices. The study

concludes that the level of impact of oil prices 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. It also deducts that despite the aggressive economic growth of the

BRIC countries in the past 25 years, the volatility of stock prices in these economies

does not have significant impact on the volatility of global oil prices.

Keywords: Volatility spillover, dynamic correlation, BRIC, oil prices.JEL classification number: E37, G15


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Oil Prices and Equity Returns in the BRIC Countries

1. Introduction

Oil has played a significant role in the economic and political development of the

industrialised countries in the world. Volatility of oil prices is one of the crucial

factors determining the future economic stability and economic growth of the

developing countries of today. The severity of the impact of oil crises on the

macroeconomic variables of oil importing countries has been vastly researched and

documented to date. Hamilton (1983) concludes that increases in oil prices are

responsible for declines in real GNP. He also demonstrates that oil price increases

were partly responsible for every post second world war recession in the US, except

for the one in 1960. Mork (1989), Mork et al. (1994), Lee et al. (1995) and Ferderer

(1996) find that oil price shocks have asymmetric effects on the economy.

The correlation between oil prices and GDP growth in industrialised countries

has weakened since the 1970s, mainly due to technological innovation, development

of cost-effective alternative sources of energy and sectoral change (Schneider, 2004).

The adverse economic impact of higher oil prices on oil importing developing

countries is generally more severe than that for industrialised countries. This is mainly

because these economies are more energy intensive, as they experience a rapid

economic growth, and generally, energy is used less efficiently. According to the

International Energy Report (2004)

1

, on average, oil importing developing countriesuse more than twice as much oil to produce a unit of economic output as do OECD

countries.

In recent times, there has been an increasing number of published research,

which studies the relationship between oil prices and stock prices. Huang et al. (1996)

use daily data for the period 1979-1990 and 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. Jones and Kaul (1996) use a

1Analysis of the Impact of High Oil Prices on the Global Economy, May 2004


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standard cashflow dividend valuation model and quarterly data for the period 1947-

1991 to test wether the reaction of international stock markets to oil shocks can be

justified by current and future changes in real cashflows and/or changes in expected

returns. They suggest that the reaction of Canadian and US stock prices to oil price

shocks can be completely accounted for by the impact of these shocks on real

cashflows, while the results for Japan and UK are not as strong. Sadorsky (1999) uses

vector autoregression (VAR) methodology and monthly data for the period 1947-1996

to investigate the interaction between 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 an evidence of increasing impact

since 1986. There is also evidence that oil price volatility has asymmetric effects on

the economy. Faff and Brailsford (1999) use monthly data for the period 1983-1996 to

test the relationship between Australian industry equity returns and oil prices. They

conclude that there is a positive and significant impact of oil prices on the Oil and Gas

and Diversified Resources industries and a negative impact of oil prices on the Paper

and Packaging, and Transportation industries. 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.

Liberalization and integration of international markets, characterised with

increased level of capital flows and international investments in emerging economies,

have made global investors more vulnerable to oil price impact on emerging stock

markets. Therefore, understanding the level of susceptibility of stock prices in

emerging economies to movement in global oil prices is very important.

Following the Asian and Russian financial markets 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. In financial terms, the BRIC

countries dominate the emerging market economies of today (Jensen and Larsen,

2004).

China and India have historically been net oil importing countries, meaning

that the level of oil production in the country does not satisfy the level of oil

consumption; hence, these economies have to resort to other sources of oil to meet the

national oil demand. China has been a net oil importer since 1993. Chinese oil


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production accounted for 5.18% of the worlds annual oil production, and 8.63% of

the worlds annual oil consumption in calendar year 2006 (International Energy

Agency IEA/International Petroleum Monthly, 2005-2006). According to the

Energy Information Administration EIA, China was the second largest consumer of

oil after the United States in 2006, the fifth largest producer of oil in the same year,

and the third largest importer of oil after United States and Japan. China alone

accounted for 38% of the increase in demand for oil in 2006.

According to EIA estimates, India was the sixth largest consumer of oil in the

world during 2006. Indian oil production accounted for 1.12% of the worlds annual

oil production, and 2.96% of the worlds annual oil consumption in calendar year

2006 (IEA/International Petroleum Monthly, 2005-2006). The combination of rising

oil consumption and fairly stable production levels leaves India increasingly

dependent on imports to meet consumption needs.

Russia on the contrary, has historically been a net oil exporter of crude oil.

Russian oil production accounted for 13.22% of the worlds annual oil production,

and 3.67% of the worlds annual oil consumption in calendar year 2006

(IEA/International Petroleum Monthly, 2005-2006). The country is a major oil

producer, ranked number two in the world after Saudi Arabia in 2006. Russias

economy will continue to be heavily dependent on oil and natural gas exports, making

it vulnerable to fluctuations in world oil prices.

According to the IEA, in calendar year 2006 Brazil accounted for

approximately 2.58% of the worlds annual oil consumption and 2.95% of the worlds

annual oil production. Brazil was a net importer of crude oil until April 2006, when it

celebrated the achievement of self-sufficiency. It is expected that self sufficiency will

help protect Brazil from future international energy crises and contribute to managing

excessive volatility in the world commodity market. However, although it will

produce the same volume of oil as it consumes, Brazil will still depend on light oil

imports because the countrys refining profile is unable to process all of the

domestically produced heavy oil (IEA, World Energy Outlook, 2006).

This paper measures the level by which global oil prices influence the stock

price creation process in the BRIC equity markets, and the impact of oil price

volatility on the volatility of stock prices in the same countries. The approach taken to

measure this bilateral relationship is the dynamic bivariate EGARCH model, as


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developed by Nelson (1991), which effectively measures the level of return and

volatility spillovers from the oil market to the BRIC equity markets.

An important characteristic of an efficient stock market is timely transmission

of information. Evidence of correlation between the stock market and the oil market

volatilities will imply dependence in the information process and therefore will

indicate a certain level of dependence of equity prices on international oil prices. In

this particular case, an increase in oil prices will cause expected earnings to decline,

and this will bring about an immediate decrease in stock prices if the stock market

efficiently capitalizes the cashflow implications of the oil price increase. If the stock

market is not efficient, there may be lags in adjustment to oil price changes. In this

paper we observe the level of dependence of equity markets in the BRIC countries on

international oil prices by using time varying correlation mechanisms, which is

allowed to depend on the lagged standardized innovations in the BRIC equity markets

and the oil price.

Barsky and Kilian (2004), provide evidence that exogenous events, such as

events in the Middle East are one of several factors which influence the level of oil

prices. They find that seemingly similar political events may differ greatly with

variations in demand conditions in the oil market and global macroeconomic

conditions. 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, we test for presence of volatility spillovers from the BRIC equity

markets to global oil prices.

The contribution that this paper makes is significant in several respects. Firstly,

majority of the research work to date has concentrated on the relationship between

stock returns and oil prices. In addition to that, this paper analyses the oil price

volatility as a determinant of volatility in the BRIC equity markets by using the return

and volatility spillover approach. Secondly, the paper measures the dependence of

equity markets in the BRICs on global oil price dynamics by using the time varying

correlation mechanism. Thirdly, we test for presence of volatility spillovers from the

BRIC equity markets to the volatility of oil prices. This will test for presence of equity

market volatility impact on volatility of oil prices in addition to the impact of

exogenous geopolitical events and other global macroeconomic factors.

The remainder of the paper is organised as follows: Section 2 discusses the

time series properties of the data; Section 3 presents the model used for the purpose of


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this study; Section 4 covers the discussion of the results; and Section 5 provides the

conclusions of the study.

2. Data and Preliminary Statistics

Data used in this paper are weekly closing equity market price indices for four

emerging markets: Brazil (Bovespa), Russia (AKMI Composite), India (Sensex) and

China (Shanghai Composite), and weekly West Texas Intermediate (WTI)2 crude oil

prices. The data are sampled weekly (Wednesdays) over the period January 1995 to

February 2007. 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, measured in terms of US dollars. 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 0.2058, 0.7849, 0.1274 and 0.1694 respectively, and the

standard deviations are 6.0003, 6.4953, 4.2118 and 3.8871. The skeweness and excess

kurtosis indicate that negative shocks are more common than positive for Brazil,

Russia and India, and positive are more common for China. Shapiro-Wilk and

Skewness and Kurtosis normality tests were conducted, and the results for both

confirm that all return series are not normally distributed.

The first order autocorrelation for the BRIC equity index returns and the WTI

returns ranges from -0.0816 to 0.0127 and for the squared returns it ranges from 0.025

to 0.4047, which indicates presence of non-linear dependence in the returns in the first

period. The Portmanteau tests for serial correlation for the returns and the squared

value of the returns confirm that there is persistence of non-linear dependence, that is,

there is a presence of conditional heteroscedasticity in the returns of all variables in

the sample.

2

WTI is a light, sweet crude oil. WTI is the underlying commodity of the New York MerchantileExchanges oil future contracts. WTI is considered a sweet crude because is contains 0.24% sulfur, a

higher concentration than the North Sea Brent crude.


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Table 2 contains the results of the unconditional correlation between the WTI

returns and equity markets returns of the BRICs. It is evident that there is a positive

correlation between the equity returns form these countries and oil returns, or more

precisely, oil returns share 0.04%, 0.13%, 0.01% and 0.02% of its variability with

Brazil, Russia, India and China respectively.

The augmented Dicky Fuller and Phillip Perron unit root tests were conducted

for the BRIC equity market index returns and the WTI crude returns, and all of them

rejected the null for presence of unit root.

3. Model

A well documented empirical finding in the finance literature is the asymmetric

impact of news on the volatility transmission (see Bae and Karolyi, 1994, Koutmos

and Booth, 1995 and Booth et al, 1997). The asymmetric phenomenon in combination

with the observed volatility clustering in equity market returns and oil price returns

validate the use of a bivariate EGARCH framework. The bivariate EGARCH model,

as developed by Nelson (1991), captures the potential asymmetric behaviour of equity

market and oil price returns and avoids imposing non-negativity constraints in

GARCH modelling - by specifying the logarithm of the variance ln( t2 ), it is no

longer necessary to restrict parameters in order to avoid negative variances.

The purpose of this paper is to determine the impact of oil price returns in the

equity price creation process in the BRICs, and also to analyse the impact of oil price

volatility on volatility of equity returns in the BRIC countries. The volatility spillover

mechanism in the BRIC equity markets is modeled by assuming oil price shocks,

represented by WTI innovations (standardized error component).

It should be noted that the bivariate EGARCH model used for the purpose of

this study has a restriction in the mean equation for oil prices. The model 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 eg. Iraq invasion of Kuwait in

1990, September 11 events the American war with Iraq in 2003, and other (Hamilton,

1985). Restrictions in the volatility equation for oil prices are not imposed, as the

BRIC countries represent significant part of the global oil consumption, hence the


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model is constructed to identify and measure the presence of volatility spillovers from

the BRIC equity markets to global oil prices.

3.1 Model Specification

A brief description is provided of the bivariate EGARCH 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 tjr, where the

subscriptj represents one of the BRIC index returns, and by toilr , the oil price change.

The mean spillover effect is captured by the following relationship:

+

+

=

toil

tj

toil

tj

oil

jj

oil

j

toil

tj

r

r

r

r

,

,

1,

1,

2,

2,1,

0,

0,

,

,

0

(1)

where,

),0(~,

,

tt

toil

tj

(2)

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).t

indicates all

relevant information known at time t, andt

is the time varying covariance matrix

defined below. The diagonal elements of the ( )22 covariance matrix are given by:

),ln()()()ln( 2 1,1,22,1,11,0,2

, +++= tjjtoiljtjjjtj zfzf and (3)

)ln()()(1)ln( 2 1,1,22,1,1,0,2

, +++= toilotoiloiltjoiloiltoil zfzf (4)

In equations (3) and (4),1

f and2

f are functions of standardized innovations. These

innovations are defined astjtjtjz ,,, /= and toiltoiltoilz ,,, /= . The functions 1f and

2f capture the effect of sign and the size of the lagged innovations as:


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1,1,1,1,1 )()( += tjjtjtjtj zzEzzf (5)

1,1,1,1,2 )()( += toiloiltoiltoiltoil zzEzzf (6)

The first two terms in equations (5) and (6) capture the size effect and the third term

measures the sign effect. If is negative, a negative realisation ofzt-1 will increase the

volatility by more than a positive realisation of equal magnitude. Similarly, if the past

absolute value ofzt-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.

The asymmetric effect of standardised innovations on volatility may be

measured as derivatives from equations (5) and (6):

+

+=

i

i

ititizzf

1

1/)(

0

0

i

i

z

z(7)

Relative asymmetry is defined as )1/(|1| ii ++ . 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:

||ln

)5.0ln(

i

HL

= (8)

The off diagonal elements of the covariance matrixt

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 the equations

(3) and (4) generate the required conditional covariance. The conditional correlation is

allowed to depend on the lagged standardized innovations and transformed using a

suitable function so that it lies between ( )1,1 . This is given by the following equation:


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toiltjtoiljtoilj ,,,,,, = ,

1

)exp(1

12,,

+

=t

toilj

,

121,1,10 ++= ttoiltjt czzcc (9)

Although the function t may be unbounded, the sin function transformation will

restrict it to the desired range for correlation.

For a given pair of return series the 17 parameters to be estimated is

conveniently labelled as:

),,,,,,,,,,,,,,,,,( 2102,1,0,2,1,0,2,0,2,1,0, cccoiloiloiloiloiljjjjjoiloiljjj

(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 is:

[ ]

=

= =

toil

tj

t

T

t

T

t

toiltjtTL,

,1

1 1

,,5.0ln5.0)5.0ln()(

(11)

3.2 Diagnostics tests

The diagnostics statistics for the BRIC equity market indices and WTI are detailed in

Tables 4, 6, 8 and 10. The test statistics include the 20 th 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 non-

linear dependence in the standardised innovations for all equity markets and WTI, 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 90% 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


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asymmetry effect. The Engle and Ng test also indicates a good fit of the bivariate

EGARCH model to the available data set.

4. Empirical Results

The bivariate EGARCH model applied in this analysis allows for both price and

volatility spillovers as well as for time varying correlation structure. 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 GAUSSTM

) without any parameter restrictions imposed.

4.1 Mean and volatility spillover effects

Based on the results for each of the BRIC countries, as presented in Tables 3, 5, 7 and

9, and as indicated by the j,1 and j,2 coefficients, the returns in the Brazilian, Indian

and Chinese equity markets are not affected by the countries previous equity returns,

nor by price spillovers from the oil market. According to Bhar and Nikolova (2007),

equity prices in these countries are mainly determined by spillover effects from equity

prices from the regions to which these countries belong, and to a lesser extent, world

equity prices spillovers. Unlike the rest of the countries which form the BRIC

grouping, returns in the Russian equity market are largely determined by its own past

returns and by oil price spillovers to a lesser degree. Unlike the other BRIC countries,

Russia has historically been a net exporter of oil and income from oil production

represents a significant part of Russias national income. According to the IMF and

World Bank, the oil and gas sector in Russia represents 20% of the national income,

hence it is only expected that the economy and its financial market returns would be

largely related to global oil price fluctuations. The positive sign of the j,2 coefficient

indicates a positive relationship between equity prices in the Russian market and WTI

oil prices.

Parameters j,1 and oil,2 capture the impact of the markets own lagged

standardised innovations on the conditional volatility for each of the BRIC and the

WTI markets respectively. The behaviour of the market returns is summarised by the

quantity of relative asymmetry detailed in the respective markets tables.

The j,1 and oil,2parameters are statistically significant for all BRIC countries

and the WTI, which indicate that the volatility in these markets depends on their


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respective lagged standardised innovations. The BRIC countries past innovation

coefficient are somewhat higher than the WTI past innovation coefficients, which

might be explained by the fact that oil prices are also quite largely affected by

international geopolitical events and global macroeconomic forces, besides their own

past innovations.

There is a support for the presence of asymmetric volatility in equity markets

for Brazil, India and Russia. The relative asymmetry is greater than one for these

markets, which indicates that negative innovation in the previous period will result in

a higher conditional volatility in the current period for all markets.

Unlike the other BRIC equity markets, the Chinese equity markets relative

asymmetry is less than one, which indicates that negative innovation in the previous

period will result in a lower conditional volatility in the current period for the market,

and vice versa. Similar to China, the results for the WTI price volatility asymmetry

indicate that positive past events will trigger higher volatility than negative past

events.

The persistence in volatility is measured by the parameters j and oil. The

values of 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

parameters suggests the tendency for the volatility shocks to persist. Using the HL

parameter, the volatility persistence can be expressed in day terms. 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 days) and the Indian market takes the least time

(1.8 days), which suggests that India has the lowest level of volatility persistence out

of all BRIC countries.

Parameters j,2 and oil,1 capture the impact of cross-market standardised

innovations for the BRIC equity markets and WTI oil price. Based on the results, the

conditional volatility of the Brazilian equity markets is not affected by past

innovations in WTI oil prices, that is, there is no evidence of volatility spillover

effects from the WTI market to the Brazilian equity markets. Brazil has historically

been a net importerof crude oil, however the oil consumption for the larger part has

been met by local oil production, which has made the country less dependent on

external oil supply and less susceptible to changes in international oil prices. Brazil

achieved oil sufficiency in 2006 and has aspirations towards becoming a net oil

exporter. The positioning of Brazil as a net oil exporter in the global oil supply scene


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will result in greater dependence of Brazils equity prices on global oil price dynamics,

as larger proportion of the countrys national income will become represented by

revenues from oil exports. 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 prices. The relationship

between oil price past innovations and volatility of Russian equity prices is positive,

while the relationship between past oil price innovations and volatility of Chinese and

Indian equity prices is negative. This can be explained by the historical net oil

exporterposition of Russia, and net oil importer position of China and India. This

means that increase in global oil prices results in increased Russian oil export

revenues, which will then translates into increased share prices. On the other side,

increase in oil prices for China and India means higher oil import prices. This has a

negative impact on cash flows of businesses and ability to pay dividends to

shareholders, and effectively translates into lower stock prices.

The results for the oil price variance equation, allowing for volatility spillovers

from the BRIC equity markets to the oil price, show that while there is a statistically

significant evidence of dependence of oil price volatility on past innovations in oil

prices, there is no evidence of volatility spillover effects from any of the BRIC equity

markets to the WTI oil prices. This indicates that despite the BRICs aggressive

economic growth in the past 25 years, average annual growth rate since 1980 of 9.8%,

5.9% and 2.5% for China, India and Brazil, and 5.9% growth for Russia since 1998,

the volatility of stock prices in these countries does not have significant impact on the

volatility of global oil prices.

4.2 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.

In line with the correlation results for the Brazilian equity markets with the

WTI price in Table 3, it is evident that there is a relatively low level of dependence of

equity returns in Brazil on international/global oil prices. This is mainly due to the

ability of Brazil to meet majority of its domestic oil demand through local production,

to the extent that it has reached oil sufficiency in 2006.


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Brazil went through a progressive period after the market liberalization in

1991 (Bekaert, et al, 2003), which was followed by the introduction of the Real Plan

stabilization program in June 19943 (Dornbusch and Cline, 1997), opening up to

private capital in 1995 and the formation of the Auto Pact4

in 1996. These events

mainly shaped the development and the dynamics of the equity market in Brazil.

Brazil was affected by the Asian economic crisis almost immediately, with

sharp increase in volatility in equity prices in the first half of July 1997. It is evident

that the reduced demand for crude oil from the economies affected by the Asian crisis

had a negative impact over global oil prices during this time, however based on the

results as discussed above, it can be concluded that the reduction in oil demand from

Brazil did not have a significant impact over global oil prices. This could be explained

with the ability of Brazil to meet the majority of its oil consumption through domestic

production of oil.

Unlike Brazil, the conditional correlation between Russian equity market

returns and global oil prices is statistically significant and at times negative. Russia

has historically been a net oil exporter the country was ranked number two oil

producer after Saudi Arabia in 2006 and 20% of its national income is represented by

oil exports revenue, which explains the relatively strong relationship between Russian

equity prices and global oil prices. With heightened concerns about security of

supplies from the Middle East, Russia has become, and will most likely remain,

central to the international geopolitical stage. The positioning of Russia as a

comparably reliable supplier of oil, especially in times of turmoil in the Middle East,

is the main explanation of the evidence of periodical negative relationship between

global oil prices and Russian equity prices.

The level of conditional correlation becomes more significant in the second

half of 1998, when 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

3The real plan stabilization program of June 1994 linked the nominal value of Brazils currency to the

dollar, restored price stability moving the economy rapidly from triple to single digit inflation, andexpedited the process of trade liberalization.4 The Auto Pact between Brazil and Argentina, effective in January 1996, established conditions thatwould essentially compel foreign manufacturers to locate production in the two countries if theywished to maintain or increase their share of the Mercosur. The Mercosur is common market among

Argentina, Brazil, Paraguay and Uruguay, known as the "Common Market of the South" ("MercadoComun del Sur"). It was created by the Treaty of Ascuncin on March 26, 1991, and added Chile and

Bolivia as associate members in 1996 and 1997.


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precipitously. The volatility of conditional correlation was further motivated by the

float of the ruble in early September 1998 (Baig and Goldfajn, 2000).

It is evident from Figure 2 that there is a negative conditional correlation

between Russian equity returns and oil prices after September 11, 2001 and the

commencement of the Unites States war with Iraq. Oil prices declined sharply

following the September 11, 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 March 19, 2003 resulted in oil prices

fall, 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 of oil supplies from Russia. This is viewed as the major driving force behind

the speedy recovery of the Russian equity market returns following the above listed

events. BP reported increase in oil production for Russia in 2001/2002 of 9.1%, while

the oil production in the Middle East decreased by 6.1% in the same period. Also,

while oil production in Russia increased further 8.7% in calendar year 2003 compared

to 2002, the level of oil production in Iraq declined by 34.2% during the same period.

The time varying conditional correlation of the Indian equity market index

returns with the oil price, while not as strong as for the Russian equity market, is

statistically significant, and as evident in Figure 3, it is mainly negative. The negative

spikes are mainly evident in 1998, 2000, 2001 and 2003. The negative conditional

correlation between India and the oil price in 1998 can most likely be related to the

fact that the economy of India was relatively unaffected by the South-Asian crisis. In

addition, India was in a quite unique position during this time as the Group of Seven

(G7) imposed sanctions on the country following their nuclear testings conducted in

1998, and the subsequent downgrade of Indias sovereign rating from investment

grade to speculative. Year 2000 is characterised with sharp increase in oil prices due

to increased world demand of oil and OPEC production cuts, oil prices plummeted

following the September 11 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 prices was quite severely affected by these events, the impact was not translated


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onto Indian equity returns to the same extent, hence the negative correlation between

the two.

The results for the Chinese equity market index returns show a very nominal,

almost insignificant conditional correlation with the oil price. This can be explained

by the relatively closed nature of the Chinese financial system. Bekaert and Harvey

(2003) recognise July 1993 as the financial liberalization date for China, however

unlike the other emerging market economies regionally and globally, the financial

liberalization 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 1997-1998 did not exert a negative effect on

China. China has in fact absorbed a considerable amount of foreign direct investment

that could have otherwise channelled to neighbouring Asian economies. Overall, the

level of conditional correlation between Chinese equity returns and the oil price has

remained nominal, which indicates low level of dependence of equity returns in China

on international oil prices.

5. Conclusion

The level of impact of oil prices 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 importing country until 2006. The ability of Brazil to meet

the majority of its oil demand through local production has made the country less

vulnerable to global oil price movements relative to other net oil importing countries.

Brazil has achieved oil 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 for the country from 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 (China

became a net importer of oil in 1993). While dynamics in the oil prices do not impact

the price creation process of equities in these markets, there is evidence which shows

that past innovations in oil prices do affect the conditional volatility of equity returns

in both the Indian and Chinese equity markets, and this relationship is negative. This

can be explained by the historical net oil importer role of these countries. As net oil


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17

importers, India and China have to pay higher oil import prices when global oil prices

increase. The higher import prices have 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 prices and equity returns in India, and very

nominal and positive time varying conditional correlation between oil prices 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 great impact over equity returns and volatility in

these equity markets. India was subject to a G7 embargo in 1998, went through a

political turmoil in 2002 and saw significant regulatory changes in 2004, just to name

a few. At the same time, China is characterised by a relatively closed and comparably

highly regulated economy, where the government has an active role in the creation

and regulation of asset prices.

Unlike the other BRIC countries, Russia has historically been a net exporter of

oil. Oil production represents 20% of Russias national income, and Russia was the

second largest oil producing country after Saudi Arabia in 2006. Given that oil

production and export represent such a significant part of the Russian economy, it

comes at no surprise to find out that there is a very significant relationship between

Russian equity returns and global oil prices. Both, equity prices and the conditional

volatility of Russian equity prices are largely determined by oil price spillovers. What

comes as a surprise is the relatively frequent negative time varying correlation

between Russian equity prices and global oil prices. It was interesting to note that the

level of oil production in Russia increased by 9.1% following September 11 and a

further 8.7% after the commencement of the US war with Iraq, while the rest of the

major oil producing population experienced significant cuts in their production quotas.

Russia showed political and economic resilience during times of heightened concerns

about security of supplies from the Middle East and was perceived as a reliable

supplier of oil for developed and developing world economies. This has pushed the

country 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.


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The impact of high oil consumption from developing economies on global oil

prices, especially the BRICs, seems to be an area of concern for a number of finance

professionals. Based on the findings of this study, it can be concluded that despite the

BRICs aggressive economic growth in the past 25 years, the volatility of stock prices

in these countries does not have significant impact on the volatility of global oil prices.


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Table 1

Summary statistics for weekly equity index returns and oil price returns

Mean Std. Dev. 1 Q1(24) 2 Q2(24) Skewness Kurtosis

Brazil 0.2454 5.9149 -0.0816 37.0249 0.2245 167.5125 -0.8325 7.4683Russia 0.7849 6.4034 0.0127 34.4052 0.4047 342.5806 -0.4608 13.1352India 0.1504 4.1837 0.0057 37.8504 0.0250 108.9891 -0.3732 11.4337China 0.2543 3.9895 -0.0446 26.6025 0.1189 61.9097 0.2135 8.0343

Oil 0.1934 4.8983 -0.0539 46.1953 0.0523 30.1709 -0.4097 4.0835

Data used are weekly equity index returns and oil price returns for the period January 1995 to February 2007.Q1(24) refers to the Portmanteau statistic with the null hypothesis of no data series serial correlations measured

with a lag of 24. Similarly, Q2(24) Sq refers to the same test with squared data series. Large p-value entrieswould indicate that there are no serial correlations in the data series.

Table 2

First order unconditional correlations of the BRIC and WTI returnsBrazil Russia India China

Oil 0.0212 0.0359 0.0092 0.0137

Data used are weekly equity index returns for the period January 1995 to October 2006.


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Table 3

Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation

Brazil Equity Markets and WTI Prices

Brazil WTI

Mean equation

j,0 0.0031 (1.75) oil,0 0.0020 (1.03)j,1 0.0346 (0.79)

j,2 0.0042 (0.13) oil,2 -0.0387 (-0.96)Variance equation

j,0 -0.2913 (-3.59) oil,0 -7.3686 (-2.49)

j,1 0.2447 (6.70) oil,1 -0.0264 (-0.31)

j,2 0.0332 (0.98) oil,2 0.2229 (2.37)

j 0.9500 (70.58) oil -0.2189 (-0.45)j

-0.3894 (-2.76) oil

0.3624 (1.39)

Correlation function

0c 0.0782 (0.70)

1c 0.0379 (0.57)

2c -0.5481 (-14.60)Half Life 13.5134 0.4563

RelativeAsymmetry

1.2755 1.0442

The numbers in parentheses indicate t-statistics. Half life represents the time it takes for the shocks toreduce its impact by one-half. Relative asymmetry may be greater than, equal to or less than 1

indicating negative asymmetry, symmetry and positive asymmetry respectively.

Table 4

Diagnostics Tests (Brazil Equity Market Returns and WTI Prices

Brazil WTI

p-values for Ljung-Box Q(20) statisticsz 0.416 0.010z

2 0.997 0.327

z1.z2 0.224

p-values for Engle and Ng (1993) diagnostic testsSign bias test 0.940 0.570Negative size bias test 0.751 0.632Positive size bias test 0.974 0.526Joint test 0.224 0.989

z represents the standardised residual for the corresponding equation i.e. either country index return or regionalor world index return. z1.z2 indicate product of the two standardised residuals.


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Table 5


Russia Equity Market Returns and WTI Prices

Russia WTI

Mean equation

j,0 0.0058 (20.46) oil,0 0.0023 (1.26)j,1 0.1338 (27.00)


j,0 -0.3331 (-34.08) oil,0 -4.7923 (-217.33)

j,1 0.2691 (8.34) oil,1 0.1061 (1.37)

j,2 0.1208 (5.77) oil,2 0.1956 (7.63)

j 0.9399 (10975.21) oil 0.2062 (24.82)j

-0.0711 (-1.21) oil

0.5062 (91.58)


0c 0.2258 (14.49)

1c -0.0773 (-3.92)

2c -0.8450 (-1544.63)Half Life 11.1831 0.4390

RelativeAsymmetry

1.1531 0.5316



Table 6

Diagnostics Tests (Russia Equity Market Returns and WTI Prices)

Russia WTIp-values for Ljung-Box Q(20) statistics

z 0.954 0.005

z2

0.253 0.298z1.z2 0.081

p-values for Engle and Ng (1993) diagnostic tests

Sign bias test 0.683 0.501Negative size bias test 0.836 0.663Positive size bias test 0.090 0.481Joint test 0.324 0.965

z represents the standardised residual for the corresponding equation i.e. either country index return or regional

or world index return. z1.z2 indicate product of the two standardised residuals.


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Table 7


India Equity Market Returns and WTI Prices

India WTI

Mean equation

j,0 0.0045 (3.74) oil,0 0.0028 (1.58)j,1 0.0079 (0.18)


j,0 -2.0176 (-2.43) oil,0 -0.4136 (-1.59)

j,1 0.3345 (3.76) oil,1 0.0865 (1.35)

j,2 -0.2464 (-1.57) oil,2 0.1169 (2.06)

j 0.6859 (5.14) oil 0.9306 (21.45)j

-0.1789 (-1.22) oil

0.1900 (0.68)


0c 0.0169 (0.39)

1c -0.1311 (-2.30)

2c 0.4780 (2.30)Half Life 1.8385 9.6369

RelativeAsymmetry

1.4358 0.9865



Table 8

Diagnostics Tests (India Equity Market Returns and WTI Prices)

India WTIp-values for Ljung-Box Q(20) statistics

z 0.028 0.018

z2

0.530 0.489z1.z2 0.186






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Table 9


China Equity Market Returns and WTI Prices

China WTI

Mean equation

j,0 0.0018 (1.72) oil,0 0.0022 (1.31)j,1 -0.0337 (-1.61)


j,0 -0.2675 (-2.66) oil,0 -0.4420 (-2.32)

j,1 0.2151 (2.94) oil,1 -0.0860 (-1.42)

j,2 0.0068 (0.05) oil,2 0.0991 (1.73)

j 0.9589 (68.00) oil 0.9270 (30.62)j

0.2110 (1.73) oil

0.2390 (0.76)


0c 0.0648 (0.93)

1c 0.0226 (0.31)

2c 0.2789 (3.49)Half Life 16.5159 9.1442

RelativeAsymmetry

0.6515 0.6284



Table 10

Diagnostics Tests (China Equity Market Returns and WTI Prices)

China WTIp-values for Ljung-Box Q(20) statistics

z 0.253 0.008

z2

0.979 0.368z1.z2 0.415






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24

Figure 1: Time Varying Conditional Correlation between Brazil Equity

Returns and Oil Price Returns

Jan 1995 Feb 2007

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

25/01/1995

25/01/1996

25/01/1997

25/01/1998

25/01/1999

25/01/2000

25/01/2001

25/01/2002

25/01/2003

25/01/2004

25/01/2005

25/01/2006

25/01/2007

Brazil_Oil Correlation

Figure 2: Time Varying Conditional Correlation between Russian Equity

Returns and Oil Price Returns

Jan 1995 Feb 2007

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.30.4

0.5

25/01/1995

25/01/1996

25/01/1997

25/01/1998

25/01/1999

25/01/2000

25/01/2001

25/01/2002

25/01/2003

25/01/2004

25/01/2005

25/01/2006

25/01/2007

Russia_Oil Correlation


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Figure 3: Time Varying Conditional Correlation between Indian Equity

Returns and WTI Oil Price Returns

Jan 1995 Feb 2007

India

-0.5

-0.3

-0.1

0.1

0.3

0.5

25/01/1995

25/01/1996

25/01/1997

25/01/1998

25/01/1999

25/01/2000

25/01/2001

25/01/2002

25/01/2003

25/01/2004

25/01/2005

25/01/2006

25/01/2007

India_Oil Correlation

Figure 4: Time Varying Conditional Correlation between Chinese Equity

Returns and WTI Oil Price Returns

Jan 1995 Feb 2007

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.30.4

0.5

25/01/1995

25/01/1996

25/01/1997

25/01/1998

25/01/1999

25/01/2000

25/01/2001

25/01/2002

25/01/2003

25/01/2004

25/01/2005

25/01/2006

25/01/2007

China_Oil Correlation


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