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Transcript of Oil Price Volatility
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9th Global Conference on Business & Economics ISBN : 978-0-9742114-2-7
Oil Price Volatility and the Global Financial Crisis
By
Olowe, Rufus Ayodeji
Department of Finance,
University of Lagos, Akoka, Lagos, Nigeria.
E-mail: [email protected]
Tel : +234-8022293985
ABSTRACT
This paper investigated weekly oil price volatility of all countries average spot price, Non-
OPEC countries average spot price, Nigeria Bonny Light spot price, Nigeria Forcados spot
price, OPEC countries average spot price and United States spot price using EGARCH (1,1)
model in the light of the Asian and global financial crises. Using data over the period, January
3, 1997 and March 6, 2009, volatility persistence, asymmetric and clustering properties are
investigated for the oil market. It is found that the oil price returns series show high persistence
in the volatility and clustering and asymmetric properties. The asymmetric and leverage effects
are rejected for all the selected crudes. The result shows that the Asian and global financial
crisis have an impact on oil price return. The Asian and global financial crises are not found
to have accounted for the sudden change in variance. The results, on average, are the same for
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different oil markets All Countries average spot price, OPEC average spot price, Non-OPEC
average spot price, Nigeria Bonny Light, Nigeria Forcados and United States.
Field of Research: Oil price, Asian Financial crisis, Global Financial crisis, Volatility
persistence, EGARCH
1. INTRODUCTION
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The volatility of the oil prices has been of concern to exporters, importers, investors, analysts,
brokers, dealers and government. Oil price volatility which represents the variability of oil
price changes could be perceived as a measure of risk and determinant of derivatives. Mabro
(2000) points out that "trading requires volatility. Without it there would be no need to hedge
and where there are no hedgers, there are no speculators" (see also UNCTAD, 2005). However,
volatility does not only serve trading interests. Volatile oil prices can also increase uncertainty
and discourage much-needed investment in the oil sector. High oil prices and tight market
conditions have also raised fears about oil scarcity and concerns about energy security in many
oil-importing countries. Mabro notes that volatility disturbs governments of exporting
countries as they rely heavily on oil revenues. Low prices lead to severe curtailment of
expenditures, but such are the constraints of domestic politics that the axe does not always fall
on the less worthy projects. High prices lead to demands for expenditure increases that are not
sustainable in the long run. Price instability generates instability on a wide front: investments,
human capital, corporate performance and the economic development of oil exporting
countries.(UNCTAD, 2005). The drivers of current oil price volatility has been adduced, by
some observers, to strong demand (mainly from outside OECD), the erosion of spare capacity
in the entire oil supply chain, distributional bottlenecks, crude oil inventories, OPEC supply
response, weather shocks the emergence of new large consumers (mainly China, and India to a
lesser extent), the new geopolitical uncertainties in the Middle East following the US invasion
of Iraq, the re-emergence of oil nationalism in many oil-producing countries and the increasing
role of speculators and traders in price formation (Fattouh, 2007). The oil price behaviour has
also been interpreted in terms of cyclicality of commodity prices (Fattouh, 2007). The increase
in price of oil price will lead to increase in oil production which eventually will reduce the
demand for oil. The reduction in demand for oil will cause oil prices to go down which in turn
would increase demand and increase the oil price (Stevens, 2005).
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The volatility of assets has been of growing area of research (see Longmore and
Robinson (2004) among others). The variance or standard deviation of are two of the common
means of measuring volatility of an asset (see Bailey et al.(1986, 1987),Chowdhury (1993), and
Arize etal. (2000)). The use of variance or standard deviation as a measure of volatility is
unconditional and does not recognize that there are interesting patterns in asset volatility; e.g.,
time-varying and clustering properties. Researchers have introduced various models to explain
and predict these patterns in volatility. Engle (1982) introduced the autoregressive conditional
heteroskedasticity (ARCH) to model volatility. Engle (1982) modeled the heteroskedasticity by
relating the conditional variance of the disturbance term to the linear combination of the squared
disturbances in the recent past. Bollerslev (1986) generalized the ARCH model by modeling the
conditional variance to depend on its lagged values as well as squared lagged values of
disturbance, which is called generalized autoregressive conditional heteroskedasticity
(GARCH) . Since the work of Engle (1982) and Bollerslev (1986), various variants of GARCH
model have been developed to model volatility. Some of the models include IGARCH
originally proposed by Engle and Bollerslev (1986), GARCH-in-Mean (GARCH-M) model
introduced by Engle, Lilien and Robins (1987),the standard deviation GARCH model
introduced by Taylor (1986) and Schwert (1989), the EGARCH or Exponential GARCH model
proposed by Nelson (1991), TARCH or Threshold ARCH and Threshold GARCH were
introduced independently by Zakoan (1994) and Glosten, Jaganathan, and Runkle (1993), the
Power ARCH model generalised by Ding, Zhuanxin, C. W. J. Granger, and R. F. Engle (1993)
among others.
Few studies have done using family of GARCH models have been applied in the
modeling of the volatility of oil prices. Day and Lewis (1993) used both the GARCH(1,1) and
EGARCH(1,1) to model crude oil volatility based on daily data from November 1986 to March
1991. They find that both implied volatility; and GARCH and EGARCH conditional volatilities
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contribute incremental volatility information. Kuper (2008) used the GARCH model to model
the volatility of the price of a barrel Brent crude, over the period 5 January, 1982 to 23 April,
2002. He found GARCH (1, 3) as the preferable model while rejecting asymmetric leverage
effects. Some other studies on the volatility of oil prices using GARCH framework include
Fattouh (2007), Bacon and Kojima (2008) among others. Most of the studies focused discussion
on a single crude market especially UK Brent. No study has been done on oil price volatility
using various crudes. This paper attempt to fill that gap.
The oil price volatility has implications for many countries. For oil exporting countries,
it hampers their ability to meet expenditure plans, causing countries to take decisions that
shield their economies from low prices, including curtailing public services, reducing the
government payroll, abandoning vital projects that contribute development (e.g. electrification
projects, schools, hospitals), reducing imports to offset oil revenue losses and finding ways in
servicing external debt that more often than not has been based on a minimum expected
revenue of oil exports. For all countries, adverse oil prices lead to high transportation cost due
to rising fuel cost, high procurement cost for refineries, high food prices, threat to continuous
provision of electricity supply especially for countries that generate electricity by thermal
methods using crude oil, and cut back on investment by energy-intensive industries because of
the uncertainty surrounding expected revenues. Oil price volatility often leads to grave
macroeconomic consequences for both oil importers and exporters. The volatility of oil prices
could significantly impact on inflation, economic growth, exchange rate appreciation, balance
of payments and benchmark interest rates (UNCTAD, 2005).
Since the latter part of the 1980s, a market-related oil pricing system has been
developed that links oil prices to the market price of certain reference crude, namely Brent,
Dubai or West Texas Intermediate. Oil producing countries used these as marker crudes to price
their products at a discount or premium, depending on the quality. Thus, there is a variation in
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prices between various crudes among oil producing countries. Even among the OPEC countries,
there are variation prices. The volatility of oil prices could be different among various crudes.
The Asian Financial crisis of 1997 and the Global Financial crisis of 2008 could have
affected oil price volatility. The Asian Financial Crisis which began in 1997 was a period of
financial crisis that affected much ofAsia raising fears of a worldwide economic meltdown due
to financial contagion. The crisis started in Thailand on July 2, 1997 with the devaluation of
Thai baht caused by the decision of the Thai government to float the baht, cutting its peg to the
United States dollar, after being unsuccessful in an attempt to support it in the face of a severe
financial overextension that was in part real estate driven. Prior to the crisis, Thailand economy
was in the glimpse of collapse as it had acquired a burden of foreign debt. The crisis spread to
other Southeast Asia countries (Philippine, Malaysian, Indonesian, Singapore, South Korea,
Hong Kong and Taiwan) and Japan with their currencies slumping, stock markets collapsing
and otherasset prices declining, and a precipitous rise in private debt. The Asian crisis made
international investors reluctant to lend to developing countries, leading to economic
slowdowns in developing countries in many parts of the world. The economic slowdowns
affected the demand for oil reducing the price ofoil, to as low as $8per barrel towards the end
of 1998, causing a financial pinch in OPEC nations and other oil exporters. This reduction in
oil revenue led to the 1998 Russian financial crisis, which in turn caused Long-Term Capital
Management in the United States to collapse after losing $4.6 billion in 4 months (Wikipedia,
2009).
The global financial crisis of 2008, an ongoing majorfinancial crisis was caused by the
subprime mortgage crisis in the United States became prominently visible in September 2008
with the failure, merger, or conservatorship of several large United States-based financial firms
exposed to packaged subprime loans and credit default swaps issued to insure these loans and
their issuers (Wikipedia, 2009). The crisis rapidly evolved into a global credit crisis, deflation
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http://en.wikipedia.org/wiki/Financial_crisishttp://en.wikipedia.org/wiki/Asiahttp://en.wikipedia.org/wiki/Financial_contagionhttp://en.wikipedia.org/wiki/Thailandhttp://en.wikipedia.org/wiki/Thai_bahthttp://en.wikipedia.org/wiki/Floating_currencyhttp://en.wikipedia.org/wiki/USDhttp://en.wikipedia.org/wiki/Real_estatehttp://en.wikipedia.org/wiki/Foreign_debthttp://en.wikipedia.org/wiki/Southeast_Asiahttp://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Private_debthttp://en.wikipedia.org/wiki/Developing_countrieshttp://en.wikipedia.org/wiki/Oilhttp://en.wikipedia.org/wiki/Per_barrelhttp://en.wikipedia.org/wiki/OPEChttp://en.wikipedia.org/wiki/1998_Russian_financial_crisishttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Financial%20crisishttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Subprime%20lendinghttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20default%20swaphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20crunchhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Deflationhttp://en.wikipedia.org/wiki/Financial_crisishttp://en.wikipedia.org/wiki/Asiahttp://en.wikipedia.org/wiki/Financial_contagionhttp://en.wikipedia.org/wiki/Thailandhttp://en.wikipedia.org/wiki/Thai_bahthttp://en.wikipedia.org/wiki/Floating_currencyhttp://en.wikipedia.org/wiki/USDhttp://en.wikipedia.org/wiki/Real_estatehttp://en.wikipedia.org/wiki/Foreign_debthttp://en.wikipedia.org/wiki/Southeast_Asiahttp://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Private_debthttp://en.wikipedia.org/wiki/Developing_countrieshttp://en.wikipedia.org/wiki/Oilhttp://en.wikipedia.org/wiki/Per_barrelhttp://en.wikipedia.org/wiki/OPEChttp://en.wikipedia.org/wiki/1998_Russian_financial_crisishttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Financial%20crisishttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Subprime%20lendinghttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20default%20swaphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20crunchhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Deflation -
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and sharp reductions in shipping and commerce, resulting in a number of bank failures in
Europe and sharp reductions in the value of equities (stock) and commodities
worldwide(Wikipedia, 2009). In the United States, 15 banks failed in 2008, while several
others were rescued through government intervention or acquisitions by other banks
(Wikipedia, 2009). The financial crisis created risks to the broader economy which made
central banks around the world to cut interest rates and various governments implement
economic stimulus packages to stimulate economic growth and inspire confidence in the
financial markets. The financial crisis could have affected the uncertainty in the demand for oil,
thus, causing uncertainty in the price of oil.
The purpose of this paper is to model weekly oil price volatility of selected crudes using
all countries average spot price, Non-OPEC countries average spot price, Nigeria Bonny Light
spot price, Nigeria Forcados spot price, OPEC countries average spot price and United States
spot price using EGARCH model in the light of the Asian and global financial crises. The paper
will investigate the volatility persistence in the oil market using weekly oil prices. The rest of
this paper is organised as follows: Section two discusses overview of global oil market. Section
three discusses Theoretical background and literature review while Section four discusses
methodology. The results are presented in Section five while concluding remarks are presented
in Section six.
2. OVERVIEW OF THE GLOBAL OIL MARKET
The world oil market consists of the United States, Organization of Petroleum Exporting
Countries (OPEC) and non- OPEC countries. Prior to the establishment of OPEC, the United
States and British oil companies provided the world with increasing quantities of cheap oil. The
world price was about $1 per barrel, and during this time the United States was largely self-
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sufficient, with its imports limited by a quota. In 1960, as a way of curtailing unilateral cuts in
oil prices by the big oil companies in the U.S and Britain, the governments of the major oil-
exporting countries formed the Organization of Petroleum Exporting Countries, or OPEC.
OPECs goal was to try to was to establish stability in the petroleum market by preventing
further cuts in the price that the member countries - Iran, Iraq, Kuwait, Saudi Arabia, and
Venezuela - received for oil. The OPEC countries succeeded in stabilizing the oil prices
between $2.50 and $3 per barrel up till the early 70s. Apart from the four founding members of
OPEC, other countries later joined OPEC. The membership of OPEC has fluctuated overtime.
Indonesia withdrew from OPEC in January 2009, Angola joined OPEC in January 2007,
Ecuador withdrew from OPEC in January 1993 and rejoined in November 2007, and Gabon
withdrew from OPEC in July 1996. The current membership of OPEC include Algeria,
Ecuador, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates, and
Venezuela. OPEC member countries agreed on a quota system to help coordinate its production
policies, but attempts to stabilize prices within a price band relied on producers having to
constrain supply to create a tight market, thus generating an economic disincentive to build
stocks (UNCTAD, 2005). OPEC members benefit from higher short-term prices, however, a
tight market generates volatility and reduces the markets ability to respond to contingencies
(UNCTAD, 2005). Furthermore, disagreements on production quotas and members' mistrust
have added to uncertainty and fuelled volatility.
The displacement of coal as a primary source of energy and development of internal-
combustion engine and the automobile led to increasing oil consumption throughout the world,
especially in Europe and Japan, thus, causing an enormous expansion in the demand for oil
products.
The era of cheap oil came to an end in 1973 when, as a result of the Arab-Israeli War,
the Arab oil-producing countries cut back oil production and embargoed oil shipments to the
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United States and the Netherlands. This raised prices fourfold to $12 per barrel. The Arab
nations' cut in production, totaling 5 million barrels, could not be matched by an increase in
production from by countries (UNCTAD, 2005; Yergin, Stobauch and Weeks , 2009). This
shortfall in production, which represented 7 per cent of world production outside the USSR and
China, caused shock waves in the market especially to oil companies, consumers, oil traders,
and some governments(UNCTAD, 2005; Yergin, Stobauch and Weeks , 2009). Furthermore,
the Iranian revolution in 1979 which led to a reduction in Iran's output by 2.5 million barrels of
oil per day forced up oil prices in 1979. The outbreak of war between Iran and Iraq in 1980
aggravated the situation in the world oil market. The war led to a loss in oil production of 2.7
million barrels per day on the Iraqi side and 600,000 barrels per day on the Iranian side. This
force oil prices to increase to $35 per barrel (UNCTAD, 2005). The high oil prices contributed
to a worldwide recession which gave energy conservation a push reducing oil demand and
increasing supplies. There were significant increases in oil supplies from non-OPEC countries,
such as those in the North Sea, Mexico, Brazil, Egypt, China, and India. This forced down the
oil prices. Attempts by OPEC to stabilize prices during this period (after the Iran-Iraq war)
were unsuccessful. The failure of OPEC to stabilize prices during this period has been
attributed to members of OPEC producing beyond allotted quotas (UNCTAD, 2005). By 1986,
Saudi Arabia had increased production from 2 million barrels per day to 5 million barrels per
day. This made oil prices to crash below $10 per barrel in real terms (UNCTAD, 2005). Oil
prices remain volatile despite various efforts by OPEC to stabilize prices. As at 1989, the
Soviet Union increased its production to 11.42 million barrels per day, accounting for 19.2
percent of world production in that year. This led to further reduction in oil prices.
The invasion of Kuwait by Iraq leading to the Gulf War in 1990 caused prices to rise,
but with the increasing world oil supply, oil prices fell again, maintaining a steady decline until
1994. The lower oil prices brightened the economies of United States and Asia, thus, boosting
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oil demand and prices rise again. The financial crisis in Asia in 1997 caused economies in the
region to grind to a halt. Oil demand fell and the surplus oil production pushed down oil prices.
Oil prices decreased to around $10 per barrel in late 1998. In 1999, there was a sudden increase
in demand which along with production cutbacks by OPEC raises oil prices to about $ 30 per
barrel in 2000 but they fell once again in 2001. However, since March 2002, oil prices have
been on an upward trend climbing to record level reflecting especially the developments related
with the war in Iraq and increasing speculative trading in oil futures on Futures exchanges. As
at July 4, 2008, the crude oil prices per barrel of all countries average (ALL), Non-OPEC
countries average (NOPEC), Nigeria Bonny Light (BL), Nigeria Forcados (FD), OPEC
countries average spot price average (OPEC) and United States (US) were $137.11, $133.6,
$137.03, $146.15, $146.12 and $137.18 respectively. Figure 1 shows the trend in oil prices
since 1997. From July 25, 2008, oil prices have been gradually falling possibly reflecting world
economic recession. As at January 2, 2009, the crude oil prices per barrel of all countries
average (ALL), Non-OPEC countries average (NOPEC), Nigeria Bonny Light (BL), Nigeria
Forcados (FD), OPEC countries average spot price average (OPEC) and United States (US)
were $34.57, $31.76, $33.48, $39.85, $40.65 and $35.48 respectively. However since January
9, 2009, oil prices have been fluctuating around $40 - $47 per barrel.
3. LITERATURE REVIEW
The need of long lag to improve the goodness of fit when we adopt the autoregressive
conditional heteroskedasticity (ARCH) model occurs at times. To overcome this problem,
Bollerslev (1986) suggested the generalized ARCH (GARCH) model, which means that it is a
generalized version of ARCH. The GARCH model considers conditional variance to be a linear
combination between squired of residual and a part of lag of conditional variance.
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This simple and useful GARCH is the dominant model applied to financial time series
analysis by the parsimony principle. GARCH (1,1) model can be summarized as follows:
rt = b0 + t2
t t 1 t/ ~ N(0, ) (1)
p q2 2 2
t i t i j t j
i 1 j 1
= =
= + + (2)
where, rt is the return series, t is the disturbance term at time t; and 2 is conditional variance
of t and > 0, 0 , 0 . Equation (2) shows that the conditional variance is explained by
past shocks or volatility (ARCH term) and past variances (the GARCH term). Equation (2) will
be stationary if the persistent of volatility shocks,p q
i j
i 1 j 1= =
+ is lesser than 1 and in the case
it comes much closer to 1, volatility shocks will be much more persistent. As the sum of and
becomes close to unity, shocks die out rather slowly (see Bollerslev (1986)). To complete the
basic ARCH specification, we require an assumption about the conditional distribution of the
error term . There are three assumptions commonly employed when working with ARCH
models: normal (Gaussian) distribution, Students t-distribution, and General Error
Distribution. Bollerslev (1986, 1987), Engle and Bollerslev (1986) suggest that GARCH(1,1) is
adequate in modeling conditional variance.
The GARCH model has a distinctive advantage in that it can track the fat tail of asset
returns or the volatility clustering phenomenon very efficiently (Yoon and Lee, 2008). The
normality assumption for the error term in (1) is adopted for most research papers using
ARCH. However, other distributional assumptions such as Students t-distribution and General
error distribution can also be assumed. Bollerslev (1987) claims that for some data the fat-tailed
property can be approximated more accurately by a conditional Student t distribution.
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A weakness of the GARCH model is that the conditional variance is merely dependent
on the magnitude of the previous error term and is not related to its sign. It does not account for
the skewness or asymmetry associated with a distribution. Thus, GARCH model can not reflect
leverage effects, a kind of asymmetric information effects that have more crucial impact on
volatility when negative shocks happen than positive shocks do (Yoon and Lee, 2008).
Because of this weakness of GARCH model, a number of extensions of the GARCH (p,
q) model have been developed to explicitly account for the skewness or asymmetry. The
exponential GARCH (EGARCH) model advanced by Nelson (1991) is the earliest extension of
the GARCH model that incorporates asymmetric effects in returns from speculative prices. The
EGARCH model is defined as follows:
p q r2 2t i t k t i j t j k
i 1 j 1 k 1t i t k
2log( ) log( )
= = =
= + + +
(3)
where , i, j and k are constant parameters. The EGARCH(p,q) model, unlike the GARCH
(p, q) model, indicates that the conditional variance is an exponential function, thereby
removing the need for restrictions on the parameters to ensure positive conditional variance.
The asymmetric effect of past shocks is captured by the coefficient, which is usually
negative, that is, cetteris paribus positive shocks generate less volatility than negative shocks
(Longmore and Robinson, 2004). The leverage effect can be tested if < 0. If 0, the news
impact is asymmetric.
Apart from EGARCH model, other models of asymmetric volatility includes Glosten,
Jogannathan, and Rankle (1992) GJR-GARCH model, asymmetric power ARCH (PARCH),
Zakoian (1994) threshold ARCH (TARCH) among others.
Various studies have done using family of GARCH models in the modeling of the volatility
of oil prices. Day and Lewis (1993) used both GARCH(1,1) and EGARCH(1,1) to model crude
oil volatility based on daily data from November 1986 to March 1991. They find that both
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GARCH and EGARCH conditional volatilities contribute incremental volatility information.
Kuper (2008) used the GARCH model to model the volatility of the price of a barrel Brent
crude, over the period 5 January, 1982 to 23 April, 2002. He found GARCH (1,3) as the
preferable model while rejecting asymmetric leverage effects. Davila-Perez, Nuez-Mora and
Ruiz-Porras (2007) used GARCH (1,1) model data to estimate the price volatility in of the
Mexican Export Crude Oil Blend. The analysis relies on the conditional standard deviations
obtained from a GARCH model using daily data over the period, January 2, 1998 to February
14, 2007. They did not detect asymmetric volatility effects. Some other studies on the volatility
of oil prices using GARCH framework include Fattouh (2007), Bacon and Kojima (2008)
among others. Most of the studies discussed so far focused attention on a particular crude of an
oil producing country. Since the latter part of the 1980s, a market-related oil pricing system has
been developed that links oil prices to the market price of certain reference crude, namely
Brent, Dubai or West Texas Intermediate. Oil producing countries used these as marker crudes
to price their products at a discount or premium, depending on the quality. Thus, there is a
variation in prices between various crudes among oil producing countries. Even among the
OPEC countries, there are variation prices. The volatility of oil prices could be different among
various crudes. This paper attempt to fill research gap by investigating the volatility of various
crudes.
This study will model the volatility of weekly oil prices using all countries average spot
price, Non-OPEC countries average spot price, Nigeria Bonny Light spot, Nigeria Forcados spot
price, OPEC countries average spot price and United States spot price using the EGARCH
model in the light of the Asian and global financial crises.
4. METHODOLOGY
4.1 The Data
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The time series data used in this analysis consists of the weekly oil prices of selected crudes for
all countries average spot price (ALL), Non-OPEC countries average spot price (NOPEC),
Nigeria Bonny Light spot price (BL), Nigeria Forcados spot price (FD), OPEC countries
average spot price (OPEC) and United States spot price (US) from January 3, 1997 to March 6,
2009 downloaded from the website of the Energy Information Administration. All the prices
are in dollars per barrel. The ALL, NOPEC and OPEC are prices weighted by export volume of
the member countries. OPEC and non-OPEC averages are based on affiliations for the stated
period of time. The return on oil price is defined as:
rit = logit
it 1
OP
OP
(4)
where OPit mean oil price of crude/category i at week t and OPit-1 represent oil price of
crude/category i at week t.
The rt of Equation (3) will be used in investigating the volatility of oil price using all
countries average spot price (ALL), Non-OPEC countries average spot price (NOPEC), Nigeria
Bonny Light spot price (BL), Nigeria Forcados spot price (FD), OPEC countries average spot
price (OPEC) and United States spot price (US) .
The Asian Financial crisis of 1997 and the Global Financial crisis of 2008 could have
affected oil price volatility. The Asian Financial Crisis which began in 1997 was a period of
financial crisis that affected much ofAsia raising fears of a worldwide economic meltdown due
to financial contagion. The crisis started in Thailand on July 2, 1997 with the devaluation of
Thai baht caused by the decision of the Thai government to float the baht, cutting its peg to the
United States dollar, after being unsuccessful in an attempt to support it in the face of a severe
financial overextension that was in part real estate driven. Prior to the crisis, Thailand economy
was in the glimpse of collapse as it had acquired a burden of foreign debt. The crisis spread to
other Southeast Asia countries (Philippine, Malaysian, Indonesian, Singapore, South Korea,
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Hong Kong and Taiwan) and Japan with their currencies slumping, stock markets collapsing
and otherasset prices declining, and a precipitous rise in private debt. The Asian crisis made
international investors reluctant to lend to developing countries, leading to economic
slowdowns in developing countries in many parts of the world. The economic slowdowns
affected the demand for oil reducing the price ofoil, to as low as $8per barrel towards the end
of 1998, causing a financial pinch in OPEC nations and other oil exporters. This reduction in
oil revenue led to the 1998 Russian financial crisis, which in turn caused Long-Term Capital
Management in the United States to collapse after losing $4.6 billion in 4 months(Wikipedia,
2009). In this study, July 2, 1997 is taken as the date of commencement of the Asian financial
crisis while December 31, 2008 is taken as the end of Asian financial crisis. To account for
Asian financial crisis (ASF) in this paper, a dummy variable is set equal to 0 for the period
before July 2, 1997 and after December 31, 1998; and 1 thereafter.
The global financial crisis of 2008 , an ongoing majorfinancial crisis , was triggered by
the subprime mortgage crisis in the United States which became prominently visible in
September 2008 with the failure, merger, or conservatorship of several large United States-
based financial firms exposed to packaged subprime loans and credit default swaps issued to
insure these loans and their issuers (Wikipedia, 2009). On September 7, 2008, the United States
government took over two United States Government sponsored enterprises Fannie Mae
(Federal National Mortgage Association) and Freddie Mac (Federal Home Loan Mortgage
Corporation) into conservatorship run by the United States Federal Housing Finance Agency.
The two enterprises as at then owned or guaranteed about half of the U.S.'s $12 trillion
mortgage market. This causes panic because almost every home mortgage lender and Wall
Street bank relied on them to facilitate the mortgage market and investors worldwide owned
$5.2 trillion of debt securities backed by them (Wikipedia, 2009). Later in that month Lehman
Brothers and several other financial institutions failed in the United States. This crisis rapidly
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http://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Developing_countrieshttp://en.wikipedia.org/wiki/Oilhttp://en.wikipedia.org/wiki/Per_barrelhttp://en.wikipedia.org/wiki/OPEChttp://en.wikipedia.org/wiki/1998_Russian_financial_crisishttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Financial%20crisishttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Subprime%20lendinghttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20default%20swaphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Government%20sponsored%20enterpriseshttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Conservatorshiphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Lehman%20Brothershttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Lehman%20Brothershttp://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Developing_countrieshttp://en.wikipedia.org/wiki/Oilhttp://en.wikipedia.org/wiki/Per_barrelhttp://en.wikipedia.org/wiki/OPEChttp://en.wikipedia.org/wiki/1998_Russian_financial_crisishttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://en.wikipedia.org/wiki/Long-Term_Capital_Managementhttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Financial%20crisishttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Subprime%20lendinghttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Credit%20default%20swaphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Government%20sponsored%20enterpriseshttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Federal%20takeover%20of%20Fannie%20Mae%20and%20Freddie%20Machttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Conservatorshiphttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Lehman%20Brothershttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Lehman%20Brothers -
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evolved to global crisis. The financial crisis could have affected the uncertainty in the demand
for oil, thus, causing uncertainty in the price of oil. In this study, September 7, 2008 is taken as
the date of commencement of the global financial crisis. To account for global financial crisis
(GFC) in this paper, a dummy variable is set equal to 0 for the period before September 7, 2008
and 1 thereafter.
4.2 Properties of the Data
The summary statistics of the oil price return series is given in Table 3. The mean return for the
ALL, NOPEC, BL, FD, OPEC and US are 0.0010, 0.0010, 0.0009, 0.0010, 0.0011 and 0.0009
respectively while their standard deviations are 0.0437, 0.0459, 0.0496, 0.0474, 0.0433 and
0.0465 respectively. The mean return appears to be higher for Nigeria Forcados spot price while
it also has the lowest standard deviation. The skewness for ALL, NOPEC, BL, FD, OPEC and
US are -0.271, -0.2617, -0.4071, -0.2154, -0.289 and -0.3745 respectively. This shows that the
distribution, on average, is negatively skewed relative to the normal distribution (0 for the
normal distribution). The negative skewness is an indication of non-symmetric series. The
kurtosis for ALL, NOPEC, BL, FD, OPEC and US are larger than 3, the kurtosis for a normal
distribution. Skewness indicates non-normality, while the relatively large kurtosis suggests that
distribution of the return series is leptokurtic, signaling the necessity of a peaked distribution to
describe this series. This suggests that for the oil price return series, large market surprises of
either sign are more likely to be observed, at least unconditionally. The Lung-Box test Q
statistics for the ALL, NOPEC, BL, FD, OPEC and US are, on average, significant at the 5%
for all reported lags confirming the presence of autocorrelation in the oil price return series.
Jarque-Bera normality test rejects the hypothesis of normality for the ALL, NOPEC, BL, FD,
OPEC and US. Figures 2, 3, 4, 5, 6 and 7 show the quantile-quantile plots of the oil price
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returns for the ALL, NOPEC, BL, FD, OPEC and US. Figures 2, 3, 4, 5, 6 and 7 clearly show
that the distribution of the oil price return series shows a strong departure from normality.
The Ljung-Box test Q2 statistics for the Figures 2, 3, 4, 5, 6 and 7 are, on average,
significant at the 5% for all reported lags confirming the presence of heteroscedasticity in the
stock return series.
Table 2 shows the results of unit root test for the oil price return series. The Augmented
Dickey-Fuller test and Phillips-Perron test statistics for the oil price return series are less than
their critical values at the 1%, 5% and 10% level. This shows that the oil price return series has
no unit root. Thus, there is no need to difference the data.
In summary, the analysis of the oil price return indicates that the empirical distribution
of returns in the oil price returns market is non-normal, with very thick tails for the all countries
average spot price (ALL), Non-OPEC countries average spot price (NOPEC), Nigeria Bonny
Light spot price (BL), Nigeria Forcados spot price (FD), OPEC countries average spot price
(OPEC) and United States spot price (US). The leptokurtosis reflects the fact that the market is
characterised by very frequent medium or large changes. These changes occur with greater
frequency than what is predicted by the normal distribution. The empirical distribution
confirms the presence of a non-constant variance or volatility clustering. Volatility clustering is
apparent in Figure 8. This implies that volatility shocks today influence the expectation of
volatility many periods in the future.
4.3 Models used in the Study
This study will attempt to model the volatility of weekly oil price return using the EGARCH
model in the light of the global financial crisis for ALL, NOPEC, BL, FD, OPEC and US spot
prices. EGARCH has been chosen due non-symmetry of the distribution of oil price return
series. Section 4.2 shows that ALL, NOPEC, BL, FD, OPEC and US spot prices have negative
skewness. The mean and variance equations that will be used are given as:
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Rt = b0+b1Rt-1+b2ASF+b3GFC+t2
t t 1 t t/ ~ N(0, , v ) (5)
2 2t 1 t 1t 1 t 1
t 1 t 1
2log( ) log( )
= + + +
+1ASF+2GFC (6)
where vt is the degree of freedom
The lag length of the oil price return series used in accounting for autocoorelation of returns
has been chosen on the basis of Akaike information Criterion.
The variance equation has been augmented to account for the shift in variance as a
result of the Asian financial crisis and global financial crisis.
The volatility parameters to be estimated include , , and . As the oil price return
series shows a strong departure from normality, all the models will be estimated with Student t
as the conditional distribution for errors. The estimation will be done in such a way as to
achieve convergence.
5. THE RESULTS
The results of estimating the EGARCH models as stated in Section 4.3 for the ALL, NOPEC,
BL, FD, OPEC and US are presented in Tables 4. In the mean equation, b1 (coefficient of lag of
oil price returns) are significant in the ALL, NOPEC, BL, FD, OPEC and US confirming the
correctness of adding the variable to correct for autocorrelation in the oil price return series.
The coefficients b2 representing coefficients of the global financial crisis are all statistically
significant at the 5% level as reported in the ALL, NOPEC, BL, FD and US. This implies that,
on average, the Asian financial crisis have an impact on oil price returns. The coefficients b 2
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representing coefficients of the global financial crisis are all statistically significant at the 5%
level as reported in the ALL, NOPEC, BL, FD, OPEC and US. This implies that the global
financial crisis have an impact on oil price returns.
The variance equation in Table 3 shows that the coefficients are positive and
statistically significant in the ALL, NOPEC, BL, FD, OPEC and US. This confirms that the
ARCH effects are very pronounced implying the presence of volatility clustering. Conditional
volatility tends to rise (fall) when the absolute value of the standardized residuals is larger
(smaller) (Leon, 2007).
Table 3 shows that the coefficients (the determinant of the degree of persistence) are
statistically significant in the ALL, NOPEC, BL, FD, OPEC and US. The values of
coefficients in the ALL, NOPEC, BL, FD, OPEC and US 0.935, 0.9353, 0.9546, 0.9681,
0.9388 and 0.9407 respectively. This appears to show that there is high persistence in volatility
as the value of s are, on average, close to 1.
The coefficient 1 representing the coefficient of the Asian financial crisis in the variance
equation is insignificant in ALL, NOPEC, BL, FD, OPEC,and US. This appears to indicate that
the Asian financial crisis, on average, has no impact on volatility equation and as such did not
account for the sudden change in variance.
The coefficient 2 representing the coefficient of the global financial crisis in the
variance equation is significant only in BL while it is insignificant in ALL, OPEC, NOPEC, FD
and US. This appears to indicate that the global financial crisis, on average, has no impact on
volatility equation and as such did not account for the sudden change in variance.
Table 3 shows that the coefficients of , the asymmetry and leverage effects, are
negative and statistically insignificant at the 5% level in the ALL, NOPEC, BL, FD, OPEC and
US. In the BL and FD, is negative and statistically insignificant. This appears to show that the
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asymmetry and leverage effects are, on average, rejected in the ALL, NOPEC, BL, FD, OPEC
and US supporting the work of Kuper (2008).
The estimated coefficients of the degree of freedom, v are significant at the 5-percent
level in ALL, NOPEC, BL, FD, OPEC and US implying the appropriateness of student t
distribution.
Diagnostic checks
Table 4 shows the results of the diagnostic checks on the estimated GARCH model for the
ALL, NOPEC, BL, FD, OPEC and US. Table 4 shows that the Ljung-Box Q-test statistics of
the standardized residuals for the remaining serial correlation in the mean equation shows that
autocorrelation of standardized residuals are statistically insignificant at the 5% level for the
ALL, NOPEC, BL, FD, OPEC and US confirming the absence of serial correlation in the
standardized residuals. This shows that the mean equations are well specified. The Ljung-Box
Q2-statistics of the squared standardized residuals in Table 4 are all insignificant at the 5% level
for the ALL, NOPEC, BL, FD, OPEC and US confirming the absence of ARCH in the variance
equation. The ARCH-LM test statistics in Table 4 for the ALL, NOPEC, BL, FD, OPEC and
US further showed that the standardized residuals did not exhibit additional ARCH effect. This
shows that the variance equations are well specified in for the ALL, NOPEC, BL, FD, OPEC
and US. The Jarque-Bera statistics still shows that the standardized residuals are not normally
distributed. In sum, the EGARCH model is adequate for forecasting purposes. The volatilities
are plotted in Figures 9, 10, 11, 12, 13 and 14 showing the conditional standard deviation of the
EGARCH(1, 1) model for the ALL, NOPEC, BL, FD, OPEC and US respectively.
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6. CONCLUSION
This paper investigated the weekly oil price volatility of all countries average spot price, Non-
OPEC countries average spot price, Nigeria Bonny Light spot price, Nigeria Forcados spot
price, OPEC countries average spot price and United States spot price using EGARCH (1,1)
model in the light of the Asian and global financial crises. Volatility persistence, asymmetric
and clustering properties are investigated for the oil market. It is found that the oil price returns
series show high persistence in the volatility and clustering properties. Nigeria Forcados spot
price slightly has the highest volatility persistence. The asymmetric and leverage effects are
rejected for all the selected crudes. The result shows that the Asian and global financial crises
have an impact on oil price return. The Asian and global financial crisis, on average, are not
found to have accounted for the sudden change in variance. The results are the same for
different oil markets All Countries average spot price, OPEC average spot price, Non-OPEC
average spot price, Nigeria Bonny Light, Nigeria Forcados and United States.
The activities of speculative traders in the futures market could have accounted for high
volatility in the oil market which push up the crude oil price to $147 per barrel in July 2008.
The high oil prices contributed to global recession which led to a reduction in demand for oil.
The reduction in demand for oil led to falling oil prices which push down oil prices to about
$36 per barrel in December 2008.
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Table 1: Summary statistics and autocorrelation of the raw oil price return series over the
period, January 2, 2004 January 16, 2009
1ALL NOPEC BL FD OPEC US
Summary StatisticsMean 10.0010 0.0010 0.0009 0.0010 0.0011 0.0009Median 0.0026 0.0044 0.0049 0.0052 0.0029 0.0038Maximum 0.2210 0.2336 0.2132 0.2256 0.2098 0.2267Minimum -0.1702 -0.1780 -0.2705 -0.2007 -0.1645 -0.1894Std. Dev. 0.0437 0.0459 0.0496 0.0474 0.0433 0.0465Skewness -0.2731 -0.2617 -0.4071 -0.2154 -0.2890 -0.3745Kurtosis 4.9298 5.0101 5.8288 4.8332 4.7298 4.9544Jarque-Bera 106.0933 113.7911 228.5408 93.5302 87.7335 115.5391Probability (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Observations 633 633 633 633 633 633
Ljung-Box Q Statistics
Q(1) 137.9810 35.8980 17.1980 32.1330 39.1190 36.2690(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*
Q(6) 52.6170 46.4520 28.0760 41.3670 53.4070 49.5600(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*
Q(12) 59.4600 54.2160 36.2840 50.2030 57.9100 55.5470(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*
Q(20) 63.5500 58.3350 43.1430 55.7340 60.7940 59.4440(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*
Ljung-Box Q2 Statistics
Q2
(1) 112.1570 6.9589 40.5740 13.6950 19.6070 6.2596(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Q2(6) 53.5550 54.6620 56.8570 30.2190 56.3200 60.4620
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Q2(12) 97.8650 95.5220 69.8530 50.0490 96.9970 106.8200
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Q2(20) 120.2500 117.9400 82.7190 77.4910 115.9300 127.3500
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Notes: p values are in parentheses. * indicates significance at the 5% levelALL denotes all countries average spot price. NOPEC denotes Non-OPEC countries average spot price. BLdenotes Nigeria Bonny Light spot price. FD denotes Nigeria Forcados spot price. OPEC denotes OPEC countriesaverage spot price average and United States spot price.
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Table 2: Unit Root Test of the Oil price return series over the period, January 3, 1997
March 6, 2009
Augmented Dickey-Fuller test Phillips-Perron test
Statistic Critical Values (%) Statistic Critical Values (%)1% level 5% level 10% level 1%
level
5%
level
10%
level
ALL 1-19.524 -2.569 -1.941 -1.616 -19.712 -2.569 -1.941 -1.616NOPEC -19.681 -2.569 -1.941 -1.616 -19.644 -2.569 -1.941 -1.616BL -21.234 -2.569 -1.941 -1.616 -21.214 -2.569 -1.941 -1.616FD -19.965 -2.569 -1.941 -1.616 -19.829 -2.569 -1.941 -1.616OPEC -11.498 -2.569 -1.941 -1.616 -19.689 -2.569 -1.941 -1.616US -19.640 -2.569 -1.941 -1.616 -19.741 -2.569 -1.941 -1.616
Notes: The appropriate lags are automatically selected employing Akaike information Criterion. ALL denotesall countries average spot price. NOPEC denotes Non-OPEC countries average spot price. BL denotes NigeriaBonny Light spot price. FD denotes Nigeria Forcados spot price. OPEC denotes OPEC countries average spot
price average and United States spot price.
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Table 3: Parameter estimates of the EGARCH model, January 3, 1997 March 6, 2009
1 ALL NOPEC BL FD OPEC US
Mean Equation
b0 10.0034 0.0036 0.0036 0.0035 0.0034 0.0037
(0.0307)* (0.0294)* (0.0290)* (0.0330)* (0.0332)* (0.0310)*b1 0.2716 0.2575 0.2384 0.2537 0.2593 0.2512
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*b2 -0.0104 -0.0116 -0.0118 -0.0112 -0.0097 -0.0118
(0.0337)* (0.0245)* (0.0310)* (0.0487)* (0.0611) (0.0224)*b3 -0.0463 -0.0466 -0.0480 -0.0443 -0.0404 -0.0502
(0.0019)* (0.0023)* (0.0012)* (0.0011)* (0.0025)* (0.0015)*
Variance Equation
1-0.5130 -0.5109 -0.3997 -0.2902 -0.4879 -0.4796(0.0955) (0.0771) (0.0395)* (0.0705) (0.0859) (0.0599)
0.1123 0.1203 0.1461 0.1105 0.1086 0.1247
(0.0404)* (0.0253)* (0.0029)* (0.0126)* (0.0545) (0.0266)* 0.9350 0.9353 0.9546 0.9681 0.9388 0.9407
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*1 -0.0624 -0.0683 -0.0518 -0.0359 -0.0629 -0.0697
(0.0941) (0.0716) (0.1655) (0.2819) (0.0677) (0.0590)1 0.0240 0.0272 0.0299 0.0257 0.0315 0.0265
(0.3506) (0.3164) (0.2574) (0.1881) (0.2432) (0.3120)2 0.1645 0.1581 0.1395 0.1010 0.1367 0.1642
(0.0632) (0.0699) (0.0357)* (0.0553) (0.0699) (0.0547) 7.1758 7.1070 6.0467 7.0847 8.1446 7.5885
(0.0000)* (0.0001)* (0.0000)* (0.0000)* (0.0001)* (0.0000)*Persistence 0.9350 0.9353 0.9546 0.9681 0.9388 0.9407LL 11155 1122 1082 1097 1156 1115AIC -3.6190 -3.5161 -3.3886 -3.4361 -3.6233 -3.4943SC -3.5416 -3.4386 -3.3111 -3.3586 -3.5459 -3.4169HQC -3.5889 -3.4860 -3.3585 -3.4060 -3.5933 -3.4642
N 633 633 633 633 633 633
Notes: Standard errors are in parentheses. * indicates significant at the 5% level.LL, AIC, SC, HQC and N are the maximum log-likelihood, Akaike information Criterion, Schwarz Criterion,Hannan-Quinn criterion and Number of observations respectively. ALL denotes all countries average spot price.
NOPEC denotes Non-OPEC countries average spot price. BL denotes Nigeria Bonny Light spot price. FD
denotes Nigeria Forcados spot price. OPEC denotes OPEC countries average spot price average and United Statesspot price.
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Table 4: Autocorrelation of standardized residuals, autocorrelation of squared
standardized residuals and ARCH LM test for the EGARCH Models over the
period, January 3, 1997 March 6, 2009.
1 ALL NOPEC BL FD OPEC US
Ljung-Box Q Statistics
Q(1) 10.0002 0.0266 2.1162 0.0030 0.0073 0.0192(0.9890) (0.8700) (0.3470) (0.9570) (0.9320) (0.8900)
Q(10) 14.8630 13.7890 17.7650 17.1970 16.4040 13.8230(0.1370) (0.1830) (0.0870) (0.0700) (0.0890) (0.1810)
Q(15) 17.2330 16.2670 20.2180 18.4130 19.0420 17.2280(0.3050) (0.3650) (0.2110) (0.2420) (0.2120) (0.3050)
Q(20) 22.3570 20.8510 25.3330 22.3550 24.6160 20.7680(0.3210) (0.4060) (0.1890) (0.3220) (0.2170) (0.4110)
Ljung-Box Q2 Statistics
Q2(1) 10.2012 0.0520 0.2116 0.8008 0.2582 0.5403(0.6540) (0.8200) (0.6460) (0.3710) (0.6110) (0.4620)
Q2(10) 2.8725 2.7570 17.6150 4.0115 3.0667 2.9544(0.9840) (0.9870) (0.0620) (0.9470) (0.9800) (0.9820)
Q2(15) 8.4057 12.6640 19.7420 8.2919 4.9179 7.0774
(0.9060) (0.6280) (0.1820) (0.9120) (0.9930) (0.9550)Q2(20) 11.4070 15.0850 25.4380 9.1279 7.5378 9.3805(0.9350) (0.7720) (0.1850) (0.9810) (0.9950) (0.9780)
ARCH-LM TEST
ARCH-LM (5) 10.3518 0.4026 1.1236 0.5915 0.3323 0.4138(0.8812) (0.8471) (0.3465) (0.7065) (0.8935) (0.8393)
ARCH-LM (10) 0.2842 0.2730 0.6523 0.3908 0.2890 0.2889(0.9847) (0.9869) (0.7689) (0.9509) (0.9836) (0.9837)
ARCH-LM (20) 0.5075 0.6994 0.4696 0.4425 0.3346 0.4192(0.9641) (0.8288) (0.9770) (0.9838) (0.9974) (0.9884)
Jarque-Berra 1189.174
6
148.2246 324.5623 173.7138 145.1378 211.2796
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0000)*Notes: p values are in parentheses. ALL denotes all countries average spot price. NOPEC denotes Non-OPECcountries average spot price. BL denotes Nigeria Bonny Light spot price. FD denotes Nigeria Forcados spot
price. OPEC denotes OPEC countries average spot price average and United States spot price.
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Figure 1: Trends in crude oil prices per barrel over the period, January 3, 1997 March 6,
2009
0
20
40
60
80
100
120
140
160
97 98 99 00 01 02 03 04 05 06 07 08
ALL
NOPEC
BL
FD
OPEC
US
Figure 2: Quantile-quantile plot of oil price return series for All countries spot price,
January 3, 1997 March 6, 2009
-.15
-.10
-.05
.00
.05
.10
.15
-.2 -.1 .0 .1 .2 .3
Quantiles of ALL
Quantileso
fNormal
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Figure 3: Quantile-quantile plot of oil price return series for Non OPEC countries averagespot price, January 3, 1997 March 6, 2009
-.15
-.10
-.05
.00
.05
.10
.15
-.2 -.1 .0 .1 .2 .3
Quantiles of NOPEC
QuantilesofNormal
Figure 4: Quantile-quantile plot of oil price return series for Nigeria Bonny light spot
price, January 3, 1997 March 6, 2009
-.16
-.12
-.08
-.04
.00
.04
.08
.12
.16
-.3 -.2 -.1 .0 .1 .2 .3
Quantiles of BL
QuantilesofNormal
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Figure 5: Quantile-quantile plot of oil price return series for Nigeria Forcados spot price,
January 3, 1997 March 6, 2009
-.16
-.12
-.08
-.04
.00
.04
.08
.12
.16
-.3 -.2 -.1 .0 .1 .2 .3
Quantiles of FD
QuantilesofNormal
Figure 6: Quantile-quantile plot of oil price return series for OPEC countries average spot
price, January 3, 1997 March 6, 2009
-.15
-.10
-.05
.00
.05
.10
.15
-.2 -.1 .0 .1 .2 .3
Quantiles of OPEC
Quantileso
fNormal
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Figure 7: Quantile-quantile plot of oil price return series for United States spot price
January 3, 1997 March 6, 2009
-.16
-.12
-.08
-.04
.00
.04
.08
.12
.16
-.2 -.1 .0 .1 .2 .3
Quantiles of US
QuantilesofNormal
Figure 8: Log-differenced of weekly price of crude oil (US$ per barrel),
-.3
-.2
-.1
.0
.1
.2
.3
97 98 99 00 01 02 03 04 05 06 07 08
ALL
NOPEC
BL
FD
OPEC
US
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Figure 9: EGARCH (1,1) conditional standard deviation for All Countries average spot
Price (ALL)
.02
.03
.04
.05
.06
.07
.08
.09
.10
.11
97 98 99 00 01 02 03 04 05 06 07 08
Figure 10: EGARCH (1,1) conditional standard deviation for non OPEC average spot price
(NOPEC)
.03
.04
.05
.06
.07
.08
.09
.10
.11
97 98 99 00 01 02 03 04 05 06 07 08
Figure 11: EGARCH (1,1) conditional standard deviation for Nigerian Bonny Light spot
price (BL)
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.02
.04
.06
.08
.10
.12
97 98 99 00 01 02 03 04 05 06 07 08
Figure 12: EGARCH (1,1) conditional standard deviation for Nigeria Forcados spot price
(FD)
.02
.03
.04
.05
.06
.07
.08
.09
.10
97 98 99 00 01 02 03 04 05 06 07 08
Figure 13: EGARCH (1,1) conditional standard deviation for OPEC average spot price
(NOPEC)
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.02
.03
.04
.05
.06
.07
.08
.09
.10
97 98 99 00 01 02 03 04 05 06 07 08
Figure 14: EGARCH (1,1) conditional standard deviation for the United States spot price
(US)
.02
.04
.06
.08
.10
.12
97 98 99 00 01 02 03 04 05 06 07 08