oil prices and bric countries
TRANSCRIPT
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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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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
Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation
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,2 0.0691 (20.99) oil,2 -0.0160 (-0.72)Variance equation
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)
Correlation function
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
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 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
Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation
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,2 0.0081 (0.50) oil,2 -0.0485 (-1.61)Variance equation
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)
Correlation function
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
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 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
p-values for Engle and Ng (1993) diagnostic tests
Sign bias test 0.745 0.555Negative size bias test 0.433 0.564Positive size bias test 0.922 0.288Joint test 0.563 0.870
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 9
Parameter Estimates for the Bivariate EGARCH Model with Dynamic Correlation
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,2 0.0082 (0.41) oil,2 -0.0491 (-1.60)Variance equation
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)
Correlation function
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
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 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
p-values for Engle and Ng (1993) diagnostic tests
Sign bias test 0.775 0.311Negative size bias test 0.504 0.624Positive size bias test 0.316 0.118Joint test 0.496 0.680
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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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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