Oil Price Volatility

7/31/2019 Oil Price Volatility

1/38

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

October 16-17, 2009Cambridge University, UK

1
mailto:[email protected]:[email protected]


2/38


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


2


3/38


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).


3


4/38


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


4


5/38


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


5


6/38


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


6
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


7/38


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-


7
http://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Shippinghttp://var/www/apps/conversion/current/tmp/AppData/Local/Temp/Shipping


8/38


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


8


9/38


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


9


10/38


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.


10


11/38


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.


11


12/38


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


12


13/38


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


13


14/38


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,


14
http://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/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_Asia


15/38


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


15
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


16/38


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


16


17/38


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:


17


18/38


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


18


19/38


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


19


20/38


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.


20


21/38


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.

REFERENCES

Allen, C., & Robinson, W. (2004). Monetary Policy Rules and the Transmission Mechanism in

Jamaica. Working Paper, Bank of Jamaica.


21


22/38


Asseery, A., & Peel, D. (1991). The effects of exchange rate volatility on exports.Economic

Letters, 37,173177.

Arize, A., Thomas, O., & Slottje, D. (2000). Exchange-Rate volatility and foreign trade:

evidence from thirteenLDCs,.Journal of Business, Economics and Statistics,18,10-17.

Bacon, R., & Kojima, M. (2008). Coping with Oil Price Volatility. Energy Sector Management

Assistance Program, International Bank for Reconstruction and Development, Energy

Security Special report 005/08.

Bailey, M., Tavlas, G., & Ulan, M. (1986). Exchange-Rate variability and trade performance:

evidence from thebig seven industrial countries. Weltwirtschaftliches Archive, 122,

466-477.

Bailey, M., Tavlas, G., & Ulan, M. (1987). The impact of exchange-rate volatility on export

growth: some theoretical considerations and empirical results,Journal of Policy Model,

9, 225243.

Baillie, R. T., & Bollerslev, T. (1989). The message in daily exchange rates: A conditional-

variance tale.Journal of Business and Economic Statistics, 7 (3), 297-305.

Baillie, R. T., & Bollerslev, T. (1992). Prediction in Dynamic models with Time Dependent

Conditional Variances.Journal of Econometrics, 52, 91-132.

Bollerslev, T. 1986. Generalized Autoregressive Conditional Hetroscedasticity. Journal of

Econometrics, 31, 307-327.

Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH Modelling in Finance. Journal of

Econometrics, 52, 5-59.

Bollerslev, T., & Domowitz, I. (1993). Trading Patterns and Prices in the Interbank Foreign

Exchange Market.Journal of Finance, 48(4), 1421-1443.


22


23/38


Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH Models, Chapter 49 in Robert F.

Engle and Daniel L. McFadden (eds.), Handbook of Econometrics, Volume 4,

Amsterdam: ElsevierScience B.V.

Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1998). A Capital Asset Pricing Model with

Time Varying Covariances.Journal of Political Economy, 96 (1), 116 131.

Caporate, T., & Doroodian, K. (1994). Exchange rate variability and the flow of international

trade.Economics Letters, 46, 49-54.

Chong, C. W., Ahmad, M. I., & Abdullah, M. Y. (1999). Performance of GARCH Models in

Forecasting Stock Market Volatility.Journal of Forecasting, 18, 333-343.

Chowdhury, A. (1993). Does exchange rate volatility depress trade flows? evidence from error-

correction models,Review of Economics and Statistics, 75, 700706.

Choudhry, T. (2005). Exchange rate volatility and the united states exports: evidence from

Canada and Japan,Journal of Japanese International Economics, 19, 5171.

Choudhry T. (2008). Exchange rate volatility and United Kingdom trade: evidence from Canada,

Japan and New Zealand,Empirical Economics, Springer-Verlag, DOI 10.1007.

Davila-Perez, J.; Nuez-Mora, J. A., & Ruiz-Porras, A. (2007). Volatilidad Del precio de la

mezcla mexicana de exportacin, Mexico DF (Mexico), ITESM-Accounting and

Finance Department, (June 2007), Working paper 2007/02 [Available at:

http://www.ccm.itesm.mx/dn/dfinanzas/archivos/dt_2007_02.pdf ].

Day, T. E., & Lewis, C. M. (1993). Forecasting Futures Markets Volatility, The Journal of

Derivatives, Winter, 33 -50.

Dickey, D. A., & Fuller, W.A. (1979). Distribution of the estimators for autoregressive time

series with a unit root.Journal of American Statistical Association, 74, 427-431.

Ding, Z., Engle, R. F., & Granger, C.W. J. (1993). Long Memory Properties of Stock Market

Returns and a New Model.Journal of Empirical Finance, 1, 83-106.


23


24/38


Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the

Variance of United Kingdom Inflation.Econometrica. 50(4), 987-1008.

Engle, R. F., & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH.

Econometric Theory, 11, 122-150.

Engle, R. F., Lilien, D. M., & Robins, R P. (1987). Estimating Time Varying Risk Premia in

the Term Structure: The ARCH-M Model,Econometrica, 55, 391-407.

Glosten, L. R., Jagannathan, R., & Runkle, D. (1993). On the Relation between the Expected

Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance.

48, 1779-1801.

Fattouh, B. (2007). The Drivers of Oil Prices: The Usefulness and Limitations of Non-Structural

model, the DemandSupply Framework and Informal Approaches, Oxford Institute for

Energy Studies, WPM 32, March.

Kim, C. M. & Kon, S. (1994). Alternative Model of Conditional Heteroscedasticity in Stock

Returns,Journal of Business, 67, 563-98.

Kroner, K., & Lastrapes, W. (1993). The impact of exchange rate volatility on international

trade: reduce from estimates using the GARCH-in-mean model, Journal of

International Money and Finance, 12, 298318.

Kuper, G. (2008). Measuring Oil Price Volatility,University of Groningen, The Netherlands ,

accessed May 28, 2008 [Available at http://som.rug.nl/].

Lee, J. (1999). The effects of exchange rate volatility on trade in durables,. Review of

International Economics, 7,189201.

Lee, J. H. H., & King, M.L. (1993). A Locally Most Mean Powerful Based Score Test for

ARCH and GARCH Regression Disturbances, Journal of Business and Economic

Statistics,11, 11-27.


24
http://som.rug.nl/http://som.rug.nl/


25/38


Longmore, R., & W. Robinson. (2004). Modelling and Forecasting Exchange Rate Dynamics: An

Application of Asymmetric Volatility Models. Bank of Jamaica. Working Paper

WP2004/03.

Luu, J. C., & M. Martens. (2002). Testing the mixture of Distribution H1986. Generalized

Autoregressive Conditional Hetroscedasticity.Journal of Econometrics, 31, 307-327.

Lyons, R. K. (2001). News Perspective on the FX Markets: Order Flow Analysis. International

Finance.

Mabro, R. (2000). Oil markets and prices, OIES Monthly Comment, August.

McKenzie, M.D. (1997). ARCH Modelling of Australian Bilateral Exchange Rate Data

Applied Financial Economics, 7, 147-164.

McKenzie, M. D. (1997). Forecasting Australian Exchange Rate Volatility: A Comparative Study

Of Alternate Modelling Techniques And The Impact of Power Transformations.

MelBourne-Centre in Finance Papers, 97-10.

McKenzie, M. (1999). The impact of exchange rate volatility on international trade flows.Journal

of Economics Survey, 13, 71-106.

Mark, N.C. (1995). Exchange Rates and Fundamentals: Evidence of Long-Horizon

Predictability. The American Economic Review, 85(1), 201-218.

McFarlene, L. (2002). Consumer Price Inflation and Exchange Rate Pass-Through in Jamaica.

Bank of Jamaica.

Meese, R., & Rogoff, K. (1983). Empirical Exchange Rate Models of the Seventies: Do They

Fit the Out of Sample?.Journal of International Economics. 14(1/2). 3-24.

Meese, R.A., & Rose, A. K. (1991). An Empirical Assessment of Nonlinearities in Models of

Exchange Rate Determination.Review of Economic Studies, 58, 603-619.

Nelson, D.B. (1990a). ARCH models as Diffusion Approximations.Journal of Econometrics,

45, 7 - 38.


25


26/38


Nelson, D.B. (1990b). Stationarity and Persistence in the GARCH(1,1) model. Econometric

Reviews, 6, 318 - 334.

Nelson, D.B. (1990c). Conditional Heteroskedasticity in Asset Returns: A new approach

Econometrica, 45, 347 - 370.

Nelson, D.B. (1999). Conditional Heteroskedasticity in Asset Returns : A New Approach.

Econometrica, 59, 347-370.

Ng. V., Engle, R., & Rothschild, M. (1992). A Multi Dynamic Factor Model for Stock Returns.

Journal of Econometrics, 52, 245- 266.

Perez, J. D., Mora, J. A., & Porras, A. R. (2007). Price Volatility of Mexican Export Crude Oil

Blend, MPRA 3562, [Available at http://mpra.ub.uni-muenchen.de/3562/].

Pillips, P. C. B., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression.

Biometrika. 333-346.

Robinson, J., & Robinson, W. (1997). Monetary Policy and the Jamaican Economy: A Study of

the Transmission Mechanism. Bank of Jamaica.

Sanusi, J. O. (2004). Exchange Rate Mechanism: The Current Nigerian Experience.Paper

Presented At A Luncheon Organised By Nigerian-British Chamber of Commerce.

Schwert, G. W., & Seguin, P. J. (1990). Heteroskedasticity in Stock Returns. Journal of

Finance. 4., 1129 1155.

Schwert, W. (1989). Stock Volatility and Crash of 87,Review of Financial Studies, 3, 77102.

Stevens, P. (2005). Oil markets. Oxford Review of Economic Policy, 21(1), 1942.

Taylor, S. (1986). Modeling Financial Time Series John Wiley & Sons, Great Britain.

Taylor, S. J. (1987). Forecasting the Volatility of Currency Exchange Rates International

Journal of Forecasting, 3(1), 159 - 170.


26


27/38


UNCTAD. (2005). The Exposure Of African Governments To The Volatility Of International Oil

Prices, And What To Do About It. AU Extraordinary Conference of Ministers of Trade

of African Commodities, Arusha, Tanzania, November.

Wikipedia. (2009). 1997 Asian financial crisis. accessed March 30, 2009 [Available at

http://en.wikipedia.org].

Wikipedia. (2009). Global financial crisis of 20082009. Accessed on February 22, 2009.

[Available at http://en.wikipedia.org].

Yergin, D., Stobaugh, R., & Weeks, J. (2009). World Energy Supply, in Microsoft Corporation

(ed.). Microsoft Student DVD 2009.

Yoon, S. & K. S. Lee. (2008). The Volatility and Asymmetry of Won/Dollar Exchange Rate.

Journal of Social Sciences, 4 (1), 7-9.

Zakoan, J. M. (1994). Threshold Heteroskedastic Models.Journal of Economic Dynamics and

Control,18, 931-944.


27
http://en.wikipedia.org/http://en.wikipedia.org/http://en.wikipedia.org/http://en.wikipedia.org/


28/38


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.


28


29/38


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.


29


30/38


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.


30


31/38


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.


31


32/38


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


32


33/38


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


33


34/38


Figure 5: Quantile-quantile plot of oil price return series for Nigeria Forcados spot price,


-.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


34


35/38


Figure 7: Quantile-quantile plot of oil price return series for United States spot price


-.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


35


36/38


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)


36


37/38


.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)


37


38/38


.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

Date post:	05-Apr-2018
Category:	Documents
Upload:	usman-hamid
View:	222 times
Download:	0 times

Oil Price Volatility

Documents