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59 Chapter 2 REVIEW OF LITERATURE 2.1 Introduction In the introductory chapter, historical basis of oil-gas price linkage and the recent scenario of global and Indian crude oil and natural gas market have been discussed in detail followed by discussion on background, scope, and objectives of the present study. The present chapter, deal with review of literature, which is an important aspect of this research. It helps to trace out the past trends in research pertaining to crude oil and natural gas pricing relationship besides identifying the way in which research in the thrust area has been carried out with a focus on identifying an optimal model for oil-gas price relationship and price forecasting. Incidentally, on account of the importance of crude oil and natural gas to the economy, there has been an avalanche of studies since the oil shock of 1973-74 on forecasting crude oil price. There is also lot of studies conducted to understand the long- term and short-term relationship, identifying the inter-linkages between various commodities traded in leading bourses at National and International level. It is found that the issue of checking the efficiency and inter-linkages of various financial and commodity markets of a country and that of other countries are topics of interest for traders, stakeholders, academician and analysts. Though spot market price of various commodities, especially energy commodities react to the factors independent to their demand and supply, due to increased linkage and

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59

Chapter – 2

REVIEW OF LITERATURE

2.1 Introduction

In the introductory chapter, historical basis of oil-gas price linkage and the recent

scenario of global and Indian crude oil and natural gas market have been discussed in

detail followed by discussion on background, scope, and objectives of the present study.

The present chapter, deal with review of literature, which is an important aspect of this

research. It helps to trace out the past trends in research pertaining to crude oil and

natural gas pricing relationship besides identifying the way in which research in the thrust

area has been carried out with a focus on identifying an optimal model for oil-gas price

relationship and price forecasting.

Incidentally, on account of the importance of crude oil and natural gas to the

economy, there has been an avalanche of studies since the oil shock of 1973-74 on

forecasting crude oil price. There is also lot of studies conducted to understand the long-

term and short-term relationship, identifying the inter-linkages between various

commodities traded in leading bourses at National and International level. It is found that

the issue of checking the efficiency and inter-linkages of various financial and commodity

markets of a country and that of other countries are topics of interest for traders,

stakeholders, academician and analysts.

Though spot market price of various commodities, especially energy commodities

react to the factors independent to their demand and supply, due to increased linkage and

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close association of these products and markets there exits some relationship between

them. But, questions such as which market reacts first, whether there exist any

relationship between them, and whether there is any possibility of forecasting their

volatility etc., remains unanswered. Testing of the existence of long-term and short term

relationship is not new in developed markets97

. A great deal of research has been

conducted earlier to examine such relationships. There are many empirical studies that

provide indirect evidence on the relationship between volume and price as well as spot

and futures prices of crude oil and natural gas markets. In this chapter various previous

studies relating to long term and short term relationship, inter-linkage between national

and international markets, and forecasting of energy commodities prices have been

reviewed. This review has enabled the researcher to identify some new concepts,

methodological approaches, and current knowledge relevant to the present study. The

way in which the present study differs from the earlier ones is also discussed in detail.

Some of such relevant studies are documented for identifying the research gap, based on

which this thesis is built on.

For clear and easy understanding, encouraging logical flow of ideas, current and

relevant literature which consist a comprehensive view of the previous research on the

topic is presented in the following five sub-headings.

(a). Empirical studies on long and short term relationship across two or more

different markets

(b). Empirical studies on long and short term relationship between

commodities in a domestic market.

(c). Empirical studies on volatility and price forecasting across markets.

97

Huson Joher Ali Ahmed.(2009). The Equilibrium Relations between stock index and bond index:

Evidence from bursa Malaysia. International Research Journal of finance and economics, Issue

30. Retrieved from http://www.eurojournals.com/irjfe_30_01.pdf?&lang=en_us&output=json

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(d). Empirical studies on long and short term relationship between energy

commodities and other commodities / parameters

(e). Empirical studies involving energy commodities with focus on Indian

markets

A very brief review of the related and recent studies under each category is

documented below;

2.2 Empirical Studies on Long Term and Short Term Relationship across Different

Markets:

1. Jones and Kaul (1996)98

tested whether the reaction of international stock

markets to oil shocks can be justified by current and future changes in real

cash flows and/or changes in expected returns. They found that aggregate

stock market returns in the U.S., Canada, Japan and the U.K. were

negatively sensitive to the adverse impact of oil price shocks on those

economies. From the data collected from 1970 to 1995 they used the

GARCH and Granger causality test and argued that investors in stock

markets under react to oil price changes in the short run. They concluded

that in the postwar period, the reaction of U.S and Canadian stock prices to

oil shocks can be completely accounted for by the impact of these shocks

on real cash flows alone. In contrast, in both the United Kingdom and

Japan, innovations in oil prices appear to cause larger changes in stock

prices than can be justified by subsequent changes in real cash flows or by

changing expected returns.

98

Jones, C. &Kaul, G. (1996). Oil & stock markets[abstract]. Journal of Finance, 51 , 463-491. Retrieved

from http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1996.tb02691.x/abstract.

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2. Siliverstovs, Hegaret, Neumann and Hirschhausen (2005)99

investigated the

degree of integration of natural gas markets and their relation to the oil

price were explored through principal components analysis and Johansen

likelihood-based co-integration procedure for Europe, North America and

Japan markets for the period between the early 1990s and 2004. They

found in both the analysis a high level of natural gas market integration

within Europe, between the European and Japanese markets as well as

within the North American market. At the same time, the obtained results

suggested that the European and the North American as well as the

Japanese and North American markets were not integrated, confirming

with the earlier studies that the gas markets were not integrated across

continents.

3. Haesun, Mjelde and Bessler (2008)100

studied the relationships among eight

North American natural gas spot market prices. The study provided a

dynamic picture of daily information flow among natural gas spot markets

from 1998 to 2007. The study used the error correction model (VECM) as

the basic tool for analysis. Results indicated that the Canadian and U.S.

natural gas market was a single highly integrated market. Further results

indicated that price discovery tends to reflect both regions of excess

demand and supply. Across North America, Malin Hub in Oregon,

Chicago Hub, Illinois, West Texas Intermediate, Henry Hub and Louisiana

region were the most important markets for price discovery. Opal Hub in

99

International market integration for natural gas? A cointegration analysis of prices in Europe. North

America and Japan” Energy Economics 27 , 603-615. Retrieved from

http://web.mit.edu/ceepr/www/ publications/reprints/Reprint_194_WC.pdf. 100

Haesun Park, James W. Mjelde and David A. Bessler. (2008). Price interactions and discovery among

natural gas spot markets in North America. Energy Policy 36 , 290–302. Retrieved from

http://www.Science direct.com/science/article/pii/S0301421507004119.

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Wyoming was an information sink in contemporaneous time, receiving

price information but passing on no price information. Alberta Energy

Company (AECO) Hub in Canada received price signals from several

markets and passes on information to Opal and the Oklahoma region.

4. Maslyuk and Smyth (2009)101

studied co-integration between oil spot and future

prices of the same and different grade in the presence of structural change.

The purpose of the study was to examine whether crude oil spot and

futures prices of the same and different grades were co-integrated using a

residual-based co-integration test that allows for one structural break in the

co-integrating vector and high-frequency data. For the analysis, U.S. WTI

(West Texas Intermediate) and UK Brent was chosen as the representative

crudes since these two crudes have well-established spot and futures

markets. The results revealed that spot and future prices of the same grade

as well as spot and future prices of different grades were co-integrated.

5. Matthew, Jian and Kuan (2009)102

examined whether Dubai crude oil and Brent

crude oil futures prices were stationary as well as whether there exist a

long-run equilibrium relationship in the oil markets. Further, they

investigated the dynamic process of the endogenous variables and future

periods through VECM. The study period was from January 3, 2000

through October 1, 2009 with a total of 2481 daily samples. They found

that Brent crude oil prices lead Dubai crude oil prices, and in the long-

term, however, both Dubai and Brent crude oil prices will reach

101

Maslyuk Svetlana and Smyth Russell. (2009). Cointegration between oil spot and futures prices of the

same and different grades in the presence of structural change. Energy Policy, Volume 37, Issue 5,

1687-1693. Retrieved from http://www.sciencedirect.com/science/article/pii/S0301421509000433. 102

Matthew C. Chang, Shi-jie Jiang, Kuan Yi Lu. (2009). “Lead-lag Relationship between Different Crude

Oil Markets: Evidence from Dubai and Brent. Middle Eastern Finance and Economics , 17-24.

Retrived from http://www.eurojournals.com/mefe_5_02.pdf

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equilibrium. Their co-integration and VECM results were consistent with

the one-great-pool concept advocated by Adelman (1984).

6. Shaharudin, Samad, Fazilah, Bhat and Sonal (2009)103

examined the effect of

oil prices movements on the stock price of oil and gas companies in three

different markets (U.S., India ad UK) using daily data. The dynamic

interaction between oil prices and stock prices was investigated in the

presence of economic variables like interest rates and industrial

productions. They collected the daily data for the period August 08, 2003

to August 8th

, 2008. The oil price was the London Brent crude oil Index.

The oil stocks included the Exxon Mobil and Chevron stocks from the

NYMEX. Reliance Industries and Indian Oil Corporation Limited stocks

were collected from the NSE of India and Royal Dutch Shell and Gazprom

stocks from the LSE. They employed unit root tests, co-integration tests,

variance auto regression, error-correction models with variance

decomposition and impulse response and ARCH/GARCH models. The

results suggested that there exists significant short run and long run

relationship between oil price and the oil stocks including the effect of the

other variables such as interest rate and the stock index. The oil price

volatility transmission has a persistent effect on the volatility of the stocks

of the oil companies in all the countries that were taken up for the study.

103

Shaharudin, Roselee S, Samad, Fazilah, Bhat and Sonal. (2009). Performance and Volatility of Oil and

Gas Stocks: A Comparative Study on Selected O&G Companies. International Business Research

Vol.2, No.4. Retrieved from http://www.ccsenet.org/journal/index.php/ibr/article/view/2756/3439

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7. Svetlana and Smyth (2009)104

examined whether crude oil spot and future prices

of the same and different grades were co-integrated using a residual-based

co-integration test that allowed for one structural break in the co-

integrating vector and high-frequency data. They used daily spot and

futures prices at 1 and 3 months to maturity for the two benchmark crudes

over the period spanning January, 1991 to November, 2008. They chose

the U.S. WTI traded at NYMEX and the UK Brent traded at ICE as the

representative crude oil for this analysis. The source for the spot prices

was the Energy Information Administration (EIA), while future prices

were taken from NYMEX and ICE. They found that spot and future prices

of the same grade as well as spot and future prices of different grade were

co-integrated.

8. Demirer and Kutan (2010)105

examined the informational efficiency of crude oil

spot and futures markets with respect to OPEC conference and U.S.

Strategic Petroleum Reserve (SPR) announcements. Daily spot and futures

prices for light sweet crude oil from March, 1983 to June, 2005 were taken

up for the analysis. They concentrated on crude oil contracts traded in the

U.S. and employed the event study methodology to examine the abnormal

returns in crude oil spot and futures markets around OPEC conference and

SPR announcement dates between 1983 and 2008. Their findings

regarding OPEC announcements indicated an asymmetry in that only

OPEC production cut announcements yield a statistically significant

104

Svetlana Maslyuk, Russell Smyth. (2009). Cointegration between oil spot and future prices of the same

and different grades in the presence of structural change. Energy Policy, 37 , 1687-1693. Retrieved

from http://www.sciencedirect.com/science/article/pii/S0301421509000433. 105

Rıza Demirer, Ali M. Kutan. (2010). The behavior of crude oil spot and futures prices around OPEC and

SPR announcements: An event study perspective. Energy Economics, 32 , 1467–1476. Retrieved

from http://www.sciencedirect.com/science/article/pii/S0140988310001027.

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impact with the impact diminishing for longer maturities. They also found

that the persistence of returns following OPEC production cut

announcements created substantial excess returns to investors who take

long positions on the day following the end of OPEC conferences.

9. Ravichandran and Alkhathlan (2010)106

studied the impact of oil prices on

GCC (Gulf Cooperation Council) stock market and found that their stock

markets were likely to be susceptible to change in oil prices because they

were the major suppliers of oil. Data employed in this study were daily

stock market price indices and NYMEX oil price during the period March

2008 to April 2010. They used the Johansen’s Co-integration, VAR and

VECM. The results confirmed that there was an influence of oil price

change on GCC stock market returns in the long-term.

10. Almadi and Zhang (2011)107

examined whether world’s crude oil benchmarks

(West Texas Intermediate in North America, Brent crude in Europe, and

Dubai and Oman crude oil prices in Asia were stationary as well as

whether there exist a long-run equilibrium relationship between these

markets. The study period was from January 1st, 1990 through November

19th

, 2010 with 5450 daily samples. They found that the prices of the four

main crude oil benchmarks were co-integrated indicating that in the long

run the world oil market was unified rather than regionalized. They also

found that Western oil markets (WTI and Brent) lead East-of-Suez (EOS)

106

Ravichandran. K, Alkhathlan, Abdullah, and Khalid. (2010). Impact of Oil Prices on GCC Stock Market.

Research in Applied Economics, 2(1). Retrieved from

http://www.macrothink.org/journal/index.php /rae/article/view/435/321. 107

Mohammad S. AlMadi and Basheng Zhang. (2011). Lead-lag relationship between world Crude oil

Benchmarks: Evidence from Wet texas Intermediate, Brent, Dubai and Oman. International

Research Journal of Finance and Economics, Issue 80 , 13-26 retrieved from

http://www.internationalresearchjournaloffinanceandeconomics.com/ISSUES/IRJFE_80_02.pdf

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markets (Dubai and Oman). Specifically, this study found that WTI

significantly leads Brent, Dubai and Oman crude oil prices; Brent

significantly leads Dubai and Oman crude oil prices; and Oman

moderately leads Dubai crude oil prices. They concluded that in the long-

term the prices of the four (WTI, Brent, Dubai and Oman) crude oil main

market will reach equilibrium.

11. Pushpa, Chakraborty and Mathur (2011)108

investigated the existence of long-

term relationships between oil prices and stock market prices of two big

emerging economies in Asia viz., India and China. Since India and China

were the major oil consuming market, their stock markets were likely to be

susceptible to oil price fluctuations. A data series from January, 2000 to

May, 2011 was considered. The stationarity of the data series were

checked using ADF Test. Johansen’s co-integration model was applied to

find out the co-integration among the oil prices and stock prices of India

and China. VECM was employed to trace the existence of long run

relationship between the variables109

. The results of the co-integration

analysis found the existence long-run relationship between oil prices and

stock market prices for both the countries. The trace and maximum Eigen

value test results also revealed the existence of unique co-integrating

vectors between test variables. They provided evidence on the existence of

at least one co-integrating vector in the model and therefore concluded that

the variables exhibit a long-run association between them.

108

Pushpa Negi, Anindita Chakraborty and Garima Mathur. (2011). Long term price linkages between the

Equity markets and oil prices: A Study of two big oil consuming countries of Asia. Middle Eastern

Finance and Economics, Issue 14 Retrieved from http://www.eurojournals.com/MEFE_14_13.pdf 109

Retrieved from http://www.eurojournals.com/mefe_5_02.pdf?&lang=en_us&output=json

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2.3 Empirical Studies on Long Term and Short Term Relationship between

Commodities in Domestic Markets.

12. Serletis (1994)110

examined the number of common stochastic trends in a system

of three petroleum futures prices (crude oil, heating oil and unleaded

gasoline) using daily data from 3rd

December, 1984 to 30th

April, 1993 in

Canada. Johansen’s maximum likelihood approach, for estimating long-run

relations in multivariate vector autoregressive models was used. The

research concluded that all three prices were driven by only one common

trend, suggesting that it was appropriate to model energy futures prices as a

co-integrated system.

13. Huang, Masulis and Stoll (1996)111

investigated the dynamic interactions

between oil futures prices traded on the NYMEX and U.S. stock prices by

examining the effects of energy shocks on financial markets. In particular,

they examined the extent to which these markets were correlated, with

particular attention paid to the association of oil price indexes with the S&P

500 index, 12 major industry stock price indices and 3 individual oil

company stock price series. The vector autoregressive VAR approach was

used to examine the lead-lag relation between oil futures returns and stock

returns while controlling for interest rate effects, seasonality, and other

effects. The conclusions from the VAR approach were the same as from the

simpler bivariate cross correlations estimated earlier in the study. Oil

futures returns were not correlated with stock market returns, even

110

Apostolos Serletis. (1994). A cointegration analysis of petroleum futures prices. Energy Economics 1994

16 (2) , 93-97. Retrieved from http://www.sciencedirect.com/science/article/pii/01409883949000

27. 111

Huang, R., Masulis., R and Stoll., H. (1996). Energy Shocks & Financial Markets. Journal of Futures

Markets , 1-27. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=

900741&lang= en_us&output=json

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contemporaneously, except in the case of oil company returns. Despite the

frequently cited importance of oil for the economy, there was little evidence

of such a link in the prices of stocks other than oil companies.

14. Gunnarshaug and Ellerman (1997)112

examined natural gas pricing at five city

gate locations in the Northeastern U.S, using daily and weekly price series

for the period 1994-97. In particular, the effects of the natural gas price at

Henry Hub, weather and the natural gas inventory levels in the region were

examined by regression. The results indicated that natural gas spot city gate

prices in the Northeastern U.S. were influenced mainly by the Henry Hub

spot price and local heating degree-days. Storage inventory level supplying

to the Northeast appears to have little influence.

15. Ache, Gjolberg and Volker (2000)113

examined the relationship between crude

oil and refined product prices in a multivariate framework. This allowed

them to test several assumptions of earlier studies. They used the OLS and

co-integration approach during the period from January, 1992 to November,

2000. They focused on the North West European crude oil market and

found that the crude oil price was weakly exogenous and that the spread

was constant in some but not all relationship. Moreover, the multivariate

analysis showed that the link between crude oil prices and several refined

product prices implies market co-integration for these refined products.

112

Jasmin Gunnarshaug and Denny Ellerman. (1997). Natural Gas Pricing in the Northeastern U.S. Center

for Energy and Environmental policy research. Retrieved from http://web.mit.edu/ceepr/www/

publications/workingpapers/98012.pdf 113

Frank ache, Ole Gjolberg and Teresa Volker. (2000). Price relationship in the petroleum market: an

analysis of crude oil and refined product prices. Stavanger University, Stavanger, Norway.

Retrieved from http://www2.nlh.no/ios/publikasjoner/d2001/d2001-

04.pdf?&lang=en_us&output=json.

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16. Moosa and Silvapulle (2000)114

examined the price and volume relationship in

the crude oil futures market using linear and nonlinear causality testing for

the presence of a causal relationship between price and volume in the crude

oil futures market. The data sample used in this study consists of daily

observations on futures prices and volumes of the WTI crude oil covering

the period between 2nd

January, 1985and 11th

July, 1996. The results of

linear causality testing revealed that presence of causality running from

volume to price but not vice versa. While the result of testing for nonlinear

causality was inconsistent, most of the evidence showed that causality runs

in both directions.

17. Bahram, Chatrath, Raffiee and Ripple (2001)115

analyzed the price dynamics of

Alaska North Slope crude oil and L.A. diesel fuel prices by employing

VAR methodology and bivariate GARCH model to show that there was a

strong evidence of a unidirectional causal relationship between the two

prices. The L.A. diesel market was found to bear the majority of the burden

of convergence when there was a price spread. This finding may be seen as

being consistent with the general consensus that price discovery emanated

from the larger, more liquid market where trading volume was

concentrated. The contestability of the West Coast crude oil market tends to

cause it to react relatively competitively, while the lack of contestability for

the West Coast diesel market tends to limit its competitiveness, causing

114

Imad A. Moosa, Param Silvapulle. (2000). The price–volume relationship in the crude oil futures market

Some results based on linear and nonlinear causality testing. International Review of Economics

and Finance, 9 , 11–30. Retrieved from http://finance.martinsewell.com/stylized-facts/

nonlinearity/? &lang=en_us&output=json. 115

Bahram Adrangi, Arjun Chatrath, Kambiz Raffiee and Ronald D. Ripple. (2001). Alaska North Slope

crude oil price and the behavior of diesel prices in California. Energy Economics 23 , 29-42.

Retrieved from http://www.sciencedirect.com/science/article/pii/S0140988300000682.

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price adjustment to be slow but to follow the price signals of crude oil.

Their findings also suggested that the derived demand theory of input

pricing may not hold in this case and Alaska North Slope crude oil price

was the driving force in changes of L.A. diesel price.

18. Chinn, Leblanc and Coibion (2005)116

examined the relationship between spot

and futures prices for energy commodities (crude oil, gasoline, heating oil

markets and natural gas) obtaining the data for four variants viz., WTI

crude oil, Henry Hub natural gas, ,Gulf Coast gasoline, and No.2 Gulf

Coast heating oil. All the futures prices were collected from NYMEX

from1st January, 1999 to 31st October, 2004 as reported by Bloomberg.

Using ARIMA and used OLS, they concluded that futures prices were an

unbiased and accurate predictor of subsequent spot prices..

19. Menzie, Blan and Coibion (2005)117

examined the relationship between spot and

futures prices for four energy commodities in USA viz., crude oil (WTI),

gasoline (Gulf Coast), heating oil (No.2 Gulf Coast) and natural gas (Henry

Hub). In particular, they examined whether futures prices were an unbiased

and/or accurate predictor of subsequent spot prices. Using NYMEX futures

price data as reported by Bloomberg and were tested using OLS. They

found that while futures prices were unbiased predictors of future spot

prices for crude oil, gasoline and heating oil but not for natural gas prices at

116

Menzie D. Chinn, Michael Le Blan and Olivier Coibion. (2005). The Predictive Content of Energy

Futures: An Update On Petroleum, NG, Heating Oil And Gasoline. working Paper No. 11033,

National Bureau Of Economic Research. Retrieved from http://www.ssc.wisc.edu/~mchinn

/w11033.pdf?&lang= en_us&output=json. 117

Menzie D. Chinn, Michael Le Blan and Olivier Coibion. (2005). The Predictive Content of Energy

Futures: An Update On Petroleum, NG, Heating Oil And Gasoline. working Paper No. 11033,

National Bureau Of Economic Research. Retrieved from http://www.nber.org/papers/w11033.

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the 3-month horizon. Futures prices appeared to predict subsequent

movements in energy commodity prices only to a small extent.

20. Jose and Joutz (2006)118

examined the time series econometric relationship

between the Henry Hub natural gas price and the WTI crude oil price.

Typically, this relationship has been approached using simple correlations

and deterministic trends. When data have unit roots as in this case, such

analysis was faulty and subject to spurious results. The analysis supported

the presence of a co-integrating relationship between the crude oil and

natural gas price time series, providing significant statistical evidence that

WTI crude oil and Henry Hub natural gas prices have a long-run co-

integrating relationship. A key finding of the analysis was that natural gas

and crude oil prices historically have had a stable relationship, despite

periods where they may have appeared to decouple. A VECM of crude oil

and natural gas prices was estimated, and facilitated the analysis of the

long-run co-integration relation and short-run adjustments in prices. The

estimation of the model resulted in identifying evidence of a stable

relationship between natural gas and crude oil prices.

21. Rukmani and Bartleet (2006)119

aimed to examine what impact the changes in

the world price of oil had on New Zealand’s economic growth over the

period 1989-2006. Several hypotheses concerning the impacts of oil price

shocks on economic growth were empirically examined for petroleum

118

Jose A Villar and Frederick l.Joutz. (2006). The relationship between crude oil and natural gas prices.

Working paper Energy Information Administration, The George Washington University. Retrieved

from http://www.eia.gov /pub/oil_gas/natural_gas/ feature_articles/2006/reloilgaspri

/reloilgaspri.pdf? &lang=en_us&output=json 119

Rukmani gounder and Matthew Bartleet. (2006). Oil price Shocks and Economic Growth: evidence for

New Zealand. Department of Applied and International Economics, Massey University, Ministry

of Economic development, Wellington. Retrieved fromhttps://editorialexpress.com/cgi-

bin/conference/ download.cgi? db_name = NZAE2007&paper_id=108.

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market deregulation, i.e. post-1989 period. The issue of oil price shock

impacts on economic growth was considered using the VAR methodology

based on quarterly data. The short run impact of oil price shocks on

economic growth has been considered in a multivariate framework. This

allowed analyzing the direct economic impact of oil price shocks, as well as

the indirect linkages. The models estimated employed the linear oil price

and two leading nonlinear oil price transformations to examine various

short run impacts. Utilizing the Wald and Likelihood Ratio tests of Granger

Causality, the later results indicated that linear price change, the

asymmetric price increase and the net oil price variables were significant

for the system as a whole, whereas the asymmetric price decrease was not.

Following the causality analysis of oil price-growth nexus, the generalized

impulse responses and error variance decompositions reaffirm the direct

link between the net oil price shock and growth, as well as the indirect

linkages.

22. Switzer and Mario (2006)120

studied extreme volatility, speculative efficiency

and the hedging effectiveness of the oil futures markets. They investigated

the efficiency of the NYMEX light sweet crude oil futures contract during

the period from January, 1986 to April, 2005 and also the sub-period of

extreme conditional volatility that covered the onset of the Iraqi war

(March, 2003) to the formation of the new Iraqi government (April, 2005)

and also analyzed the effectiveness of alternative hedging models during

such periods. Using the econometric tools of Fama’s (1984) regression

120

Switzer Lorne N. and El-Khoury Mario. (2007). Extreme Volatility, Speculative Efficiency, and the

Hedging Effectiveness of the Oil Futures Markets. Journal of Futures Markets, Volume 3 , 61-84.

Retrieved from http://www.docin.com/p-294480597.html.

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approach with monthly as well as the Johansen’s (1998) co-integration

techniques, they found that crude oil futures contract prices were co-

integrated with spot prices and unbiased predictors of future spot prices,

including over the period prior to the onset of the Iraqi war and until the

formation of the new Iraqi government in April 2005. Univariate GARCH

models of the distribution of the spot and futures series revealed evidence

of long-term volatility persistence and volatility clustering. The

examination of GJR-GARCH model revealed significant volatility

asymmetries in the futures and spot prices, which showed improvement in

hedging performance when asymmetries were accounted.

23. Stelios and Cees (2008)121

investigated the linear and nonlinear causal linkages

between daily spot and futures prices for maturities of one, two, three and

four months of WTI crude oil. The data covered two periods October, 1991

to October, 1999 and November, 1999 to October, 2007. They examined

the nonlinear causal relationships of VECM filtered residuals and

investigated the hypothesis of nonlinear non-causality after controlling for

conditional heteroskedasticity in the data using a GARCH-BEKK model.

Whilst the linear causal relationships disappear after VECM co-integration

filtering, nonlinear causal linkages in some cases persist even after GARCH

filtering in both periods. These indicate that spot and futures returns may

exhibit asymmetric GARCH effects and/or statistically significant higher

order conditional moments.

121

Stelios D. Bekiros and Cees G.H. Diks. (2008). The relationship between crude oil spot and futures

prices: Cointegration, linear and nonlinear causality. Energy Economics, 30 , 2673-2685.

Retrievedm from http://www1.fee.uva.nl/cendef/publications/papers/BD_Manuscript_EE.pdf.

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24. Raymond Li (2010)122

evaluated in a multivariate framework the leading and

lagging relationship among the spot prices for crude oil, gasoline, heating

oil, jet fuel and diesel to assess whether or not the direction of price

information flow that was to be predicted from derived demand theory was

observed. Monthly spot prices of WTI light sweet crude oil, New York

Harbor conventional gasoline, No.2 heating oil, kerosene-type jet fuel and

Los Angeles No.2 diesel were used in the empirical analysis for a period

from June, 1990 to May, 2010, with 240 observations. Econometric tools

such as ADF & PP test, Residual Diagnostic Test, Johansen multivariate

co-integration test and Granger causality test were used for the study which

showed strong evidence that the price of crude oil and its refinery products

were co-integrated. At the same time, the weak exogeneity test revealed

that crude oil price transmitted exogenous shocks to the system in the long-

run and changes in oil price were passed through to the refined product

prices in the long run.

25. Westgaard, Estenstad, Seim and Frydenberg (2011)123

investigated the

relationship between Gas oil and Brent Crude oil futures prices. The

analysis was based on daily price series for five different contract lengths

traded on ICE futures Europe. The price series and their first differences

were tested for stationarity. Linear relationships between the pair-wise Gas

oil and Crude oil contracts were then tested for co-integration. 1 and 2

month contracts covering data from 1994 to 2009 and Error Correction

122

Raymond Li. (2010). The Relationships among Petroleum Prices. International Conference On Applied

Economics – ICOAE. Retrieved from http://kastoria.teikoz.gr/icoae2/wordpress/wp-content/

uploads /articles/2011/10/051.pdf. 123

Sjur Westgaard, Maria Estenstad, Maria Seim and Stein Frydenberg. (2011). Co-itegration of ICE Gas

oil and Crude oil futures. Energy Economics 33 , 311-320. Retreived from http://www.science

direct. com/ science/article/pii/S0140988310001982.

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Models were established to estimate the relationships. No co-integrated

relationships were found for the 3, 6 and 12 month contracts covering the

period 2002–2009, nor for the 1 and 2 month contracts for this period.

26. Lee, Huang and Yang (2012)124

employed the momentum threshold error-

correction model with generalized autoregressive conditional

heteroskedasticity to investigate asymmetric co-integration and causal

relationships between WTI crude oil and gold prices in the U.S. futures

market. They collected the data from May 1st, 1994 to November 20

th,

2008. The empirical results showed that an asymmetric long-run adjustment

exists between gold and oil. Furthermore, the causality relationship shows

that WTI crude oil played a dominant role.

2.4 Empirical Studies on Volatility and Price Forecasting Across Markets.

27. Kumar and Manmohan (1992)125

investigated the efficiency of the NYMEX

futures market and the forecasting accuracy of crude oil futures prices

during the period June, 1985 to May, 1990. They analyzed the efficiency

of the market in terms of the expected returns from trading in the futures

contracts while accuracy of futures price forecasts has been analyzed by

comparing it with the accuracy of forecasts obtained using random walk,

time-series models, econometric models, and judgmental forecasts.

Further, they examined the improvement in forecasting accuracy when

futures prices were combined with forecasts from alternatives sources. The

results suggested that there did not appear any systematic bias in crude oil

124

Yen-Hsien Lee, Ya-Ling Huang and Hao-Jang Yang. (2012). The asymmetric long-run relationship

between crude oil and gold futures. Global Journal of Business Research Vol. 6 No.1. Retrievd

from https://papers.ssrn.com/sol3/Data_Integrity_Notice.cfm?abid=1945967. 125

Kumar Manmohan S. (1992). The Forecasting Accuracy of Crude Oil Futures Prices. Staff Papers –

International Monetary Fund, Volume 39, No.2 , 432-461. Retrieved from http://www.jstor.org/

stable/ pdfplus/3867065.pdf?acceptTC=true.

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futures prices. Also, crude oil prices provided forecast that was, on an

average, superior to those obtained from alternative techniques for short

term horizons.

28. Moosa and Al-Loughani (1994)126

in their research paper presented empirical

evidence on market efficiency and un-biasedness in the crude oil futures

market. On the basis of monthly observations on spot and futures prices of

the WTI crude oil, several tests were carried out on the relevant

hypotheses. The data sample consists of monthly observations on three

price series related to the WTI crude oil: spot price, three-month futures

price and six-month futures price. The sample covers the period 1st

January, 1986 to 31st July, 1990. The analysis of the results suggested that

futures prices were neither unbiased nor efficient forecasters of spot prices.

Furthermore, a GARCH-M model revealed the existence of a time varying

risk premium.

29. Herbert (1995)127

studied relationship between the volatility of the natural gas

futures price and maturity of the futures contract and estimated the volume

of trading in the futures contact. Only data for the nearby month contracts

was considered which gave approximately 20 observations per contract

from January, 1990 to December, 1994 futures price from UK. By using

regression analysis, it was found that the volume of trade rather than

maturity explains the variance of the volatility. It was also discovered that

past levels of volume of trade influence current variability of price volatility

126

Imad A. Moosa and Nabeel E. Al-Loughani. (1994). Unbiasedness and time varying risk premia in the

crude oil futures market. Energy Economics 1994 16 (2) , 99-105. Retrieved from

http://www.sciencedirect .com/ science/article/pii/0140988394900035. 127

John H Herbert. (1995). Trading Volume, maturity and Natural Gas Futures price Volatility. Energy

Economics, Vol. 17, No. 4 , 293-299. Retrieved from http://www.sciencedirect.com/science/

article/pii/ 014098839500033Q.

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but that past variability of price volatility has much less of an influence on

current levels of trade.

30. Abosedra and Hamid (2004)128

evaluated the predictive accuracy of 1, 3, 6, 9

and 12-month crude oil futures prices for January, 1991-December, 2001

from NYMEX- WTI. In addition to testing for un-biasedness, a naive

forecasting model was constructed to generate comparable forecasts, as

benchmarks. The empirical findings revealed that futures prices and

forecasts were unbiased at all forecast horizons. However, the 1st and 12-

month futures prices were the only forecasts outperforming the naive,

suggesting their potential usefulness in policy making. However, they also

argued that continuing political instability of the Middle East and the

inability of OPEC to offset market sentiment, among other factors, may in

the future adversely affect the predictive accuracy of the 1 month and 12-

month ahead futures prices.

31. Coimbra and Soares (2004)129

evaluated the OPEC crude oil price volatility

forecasting performance of futures market prices from July, 1991 (9

months), from April, 1994 (12 months) and from January, 1998 (18

months). The stationarity of the series were tested using ADF test. Two

alternative samples were considered (i) a full sample, from January, 1989

to December, 2003; (ii) a partial sample that excludes the Golf War

effects, from January 1992 to December 2003. The results revealed the

presence of a non-stationary variable integrated of order one, suggesting

128

Abosedra Salah and Baghestani Hamid. (2004). On the predictive accuracy of crude oil futures prices.

Energy Policy, volume 32, Issue 12, 1389-1393. Retrieved from http://www.sciencedirect.com

/science/article/pii/ S0301421503001046. 129

Coimbra Carlos and Esteves Paulo Soares. (2004). Oil Price Assumptions in Macroeconomic Forecasts:

Should We Follow Futures Market Expectations? OPEC Review, Volume 28, No.2 , 87-

106.[Abstract]. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=565122.

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the use of its first difference and suggested that it’s very difficult to

forecast oil prices given its volatility. Finally, there was evidence of a

positive correlation between futures market oil prices errors and the market

expectation errors concerning the evolution of world economic activity.

These results suggested an adjustment of the oil prices forecast based on

futures markets whenever the market expectations on economic growth

were different from the values underlying the macroeconomic projections.

32. Moshiri and Foroutan (2006)130

forecasted daily crude oil futures prices that

were listed in NYMEX from 1983 to 2003. Traditional linear structural

models have not been promising when used for oil price forecasting.

Although linear and nonlinear time series models have performed much

better in forecasting oil prices, there was still room for improvement. If the

data generating process was nonlinear, applying linear models could result

in large forecast errors. Finally they applied linear and nonlinear time series

models. They used the software EViews-4 to estimate and forecast crude oil

futures prices and discovered that linear ARMA (1, 3) and nonlinear

GARCH (2, 1) models were the most suitable. However, the GARCH

model outperformed the ARMA model.

33. Yun (2006)131

tried to answer the relative predictability of futures prices

compared to the forecasts based on experts’ system and econometric models,

using WTI crude oil spot and futures prices, for the period of October, 1998

to June, 2004. For regression models, this sample was divided to the first

130

Moshiri, S. and Foroutan, F. (2006). Forecasting Nonlinear Crude Oil Futures Prices. The Energy

Journal 27(4) , 81-95. Retrieved June 29, 2012, from http://cat.inist.fr/?aModele=affiche N&

cpsidt=18136310 131

Won-Cheol Yun. (2006). Predictability of WTI Futures Prices Relative to EIA Forecasts and

Econometric Models. Journal of Economic Research 11 , 49-72. Retrieved from http://jer.hanyang

.ac.kr/ issue/11_1/ 11-1-03.pdf

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half of October, 1998 to December, 2000 and the second half of January

2001 to June 2004. This study performed simple statistical comparisons in

forecasting accuracy and a formal test of differences in forecasting errors.

The study has shown new records of price hikes unseen for last two decades.

One of the reasons for this phenomenon could be the difficulty in forecasting

crude oil prices. According to statistical results, WTI crude oil futures

market turns out to be efficient relative to EIA experts’ system and

econometric models. Consequently, WTI crude oil futures market could be

utilized as a market-based tool for price forecasting and/or resource

allocation.

34. Narayan and Narayan (2007)132

examined the volatility of crude oil price using

daily data for the period 1991–2006 from Australia by Exponential GARCH

(EGARCH) model. They found that across the various sub-samples, there

was inconsistent evidence of asymmetry and persistence of shocks. And over

the full sample period, evidence suggested that shocks have permanent

effects and asymmetric effects, on volatility and the behaviour of oil prices

tends to change over short periods of time.

35. Mastrangelo (2007)133

presented an analysis of price volatility in the spot natural

gas market, with particular emphasis on the Henry Hub in Louisiana

considering the Henry Hub spot price from 1994 to 2006. The purpose was

to address whether natural gas prices have been more volatile in recent years

and identify potential market factors that may contribute to price volatility.

132

Narayan, P. K. and Narayan, S. (2007). Modeling Oil Price Volatility. Energy Policy 35 , 6549-6553.

Retrieved June 25, 2012, from http://www.sciencedirect.comscience/article/pii/S0301421507003

31X 133

Erin Mastrangelo. (2007). An Analysis of Price Volatility in Natural Gas Markets. Office of Oil and Gas,

Energy Information Administration. Retrieved from http://www.eia.gov/pub/oil_gas/natural_gas/

feature_articles/2007/ngprivolatility/ngprivolatility.pdf

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In addition to a first-order autoregressive error model, several graphical and

statistical tools were used to examine trends and determine influencing

factors. Although there was no demonstrated long-term trend in volatility,

there were seasonal patterns and volatility was correlated strongly with

storage dynamics.

36. Cuaresma, Jumah and Karbuz (2007)134

proposed a new time series model

aimed at forecasting crude oil prices by concentrating around three marker

crudes – WTI crude oil, NYMEX Brent and Dubai crude oil using monthly

data for the period January, 1983-August, 2007. The proposed specification

was an unobserved components model with an asymmetric cyclical

component. The asymmetric cycle was defined as a sine-cosine wave where

the frequency of the cycle depends on past oil price observations. The results

of the study suggest that oil price forecasts improve significantly when this

asymmetry was explicitly modeled.

37. Marzo and Zagalia (2007)135

studied the forecasting properties of linear GARCH

models for closing-day futures prices on crude oil, first position, traded at

NYMEX from January, 1995 to November, 2005. In order to account for

fat tails in the empirical distribution of the series, they compared models

based on the normal Student’s t and Generalized Exponential distribution.

They focused on out-of-sample predictability by ranking the models

according to a large array of statistical loss functions. The results from the

134

Jesus Crespo Cuaresma, Adusei Jumah and Sohbet Karbuz. (2007). Modeling and Forecasting Oil

Prices: The Role of Asymmetric Cycles. Working papers in Economics and Statistics, University

of Innsbruck. Retrieved Septomber 28, 2012, from http://www.appliedforecasting.com/content/

view/402/32/?& lang=en_us&output=json 135

Massimiliano Marzo and Paolo Zagaglia.(2007). “Volatility Forecasting for Crude oil futures”. Norway;

Department of Economics, Stockholm University. Retrieved from http://www2.ne.su.se/paper/

wp07 _ 09.pdf

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tests for predictive ability showed that the GARCH-G model fares best for

short horizons from one to three days ahead. For horizons from one week

ahead, no superior model can be identified. They also considered out-of

sample loss functions based on Value-at-Risk that mimic portfolio

managers and regulators’ preferences. EGARCH models display the best

performance in this case.

38. Ying, Zhang, Tsai and Wei (2008)136

estimated volatility using GARCH-type

models, based on the Generalized Error Distribution (GED), for both the

extreme downside and upside Value-at-Risks (VaR) of returns in the WTI

and Brent crude oil spot markets. They collected daily spot WTI and Brent

crude oil prices from May, 20th

in 1987 to August, 1st in 2006, which were

quoted in US dollars per barrel by EIA, U.S. Furthermore, according to a

new concept of Granger causality in risk, a kernel-based test was proposed

to detect extreme risk spillover effect between the two oil markets. Results

of an empirical study indicated that the GED-GARCH-based VaR

approach appears more effective than the well-recognized HSAF (i.e.

historical simulation with ARMA forecasts). WTI return appeared more

volatile in some periods. During the Gulf War, volatility of the Brent

return turns out to be sharper than that of the WTI return. The speed of

decay of volatility shock in both WTI and Brent returns was very slow.

They also found the volatility of the Brent return did not have a leverage

136

Ying Fan, Yue-Jun Zhang, Hsien-Tang Tsai and Yi-Ming Wei. (2008). Estimating ‘Value at Risk’ of

crude oil price and its spillover effect using the GED-GARCH approach. Energy Economics, 30 ,

3156-3171. Retreived from http://epge.fgv.br/we/MFEE/GerenciamentodeRisco/2011?action=

AttachFile&do=get & target=Artigo42.pdf

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effect and WTI and Brent returns have significant two-way Granger

causality in risk.

39. Pigildin (2009)137

evaluated the closing spot prices of WTI crude oil and Henry

Hub natural gas spanning from 1994 to 2009. The VAR series obtained

with this method were statistically accurate and were the least likely to

result in forgone profits from speculation. This work examined the

performance of four risk quantification methodologies (widely known as

value at risk) and assesses them against several accuracy and efficiency

criteria. The results indicated that at 95 percent confidence level GED-

GARCH method were used further to investigate the extreme risk spillover

between crude oil and natural gas markets perform better than any other

alternative. They concluded that irrespective of whether oil or gas risk

quantification and management in energy markets were an indispensable

way of business survival and prosperity.

40. Nian (2009)138

studied daily NYMEX WTI crude oil prices volatility using the

data obtained from EIA for the period from 2nd

January, 1986 to 30th

September, 2009. They used the Box-Jenkins methodology and GARCH

approaches. GARCH(1,1) was found to be a better model than ARIMA

(1,2,1) model. Based on the study, they concluded that GARCH (1,1) was

the better model for daily crude oil prices due to its ability to capture the

volatility by the non-constant of conditional variance.

137

Dmitriy Pigildin. (2009). Value at Risk Estimation and Extreme Risk Spillover between Oil and Natural

Gas Markets. Department of Economics, Central European University, 1-61. Retrieved from

www.etd. ceu.hu/ 2009/pigildin_dmitriy.pdf. 138

Lee Chee Nian. (2009). Application of ARIMA and GARCH models in forecasting Crude oil prices.

Universiti Teknologi Malaysia. Retrieved from http://eprints.utm.my/12354/3/LeeCheeNian

MFS2009ABS.pdf

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41. Agnolucci (2009)139

assessed the accuracy of the forecasting models on the basis

of integrated volatility. They have estimated GARCH models by using

daily returns from the generic light sweet crude oil future based on the

WTI traded at the NYMEX. Data on the price of the contract have been

sourced from the Bloomberg database for the period 31st December, 1991

to 2nd

May, 2005. The results suggest that the mean return from oil futures

can be assumed to be constant across time, although not statistically

different from zero. Secondly, shocks to the conditional variance of the

series have been found to be highly persistent. Thirdly, the parameters in

the models were robust to the distribution assumed for the errors. In

addition, no leverage effect can be observed in the oil future series.

42. Liu, Chen, and Su (2011)140

examined short term and long run relationships

between the petroleum futures and spot prices. The data used in this study

comprises of daily data on the closing spot and futures price of WTI

sourced mainly from the databank of NYMEX from January 1, 2004

through September 30, 2009 and thus 1,500 observations. They used the

TECM GJR-GARCH. The empirical result revealed that the petroleum

spot and futures markets have interactive effect and there was information

flow (transmission) between the two markets. Accordingly, the

speculators, hedgers, and financial managers can obtain more insights into

the management of their short run investing and hedging strategies on

petroleum futures contracts.

139

Paolo and Agnolucci. (2009). Volatility in crude oil futures: A comparison of the predictive ability of

GARCH and implied volatility models. Energy Economics 31, 316-321.Retrieved from

http://www. science direct.com/science/article/pii/S0140988308001655 140

Yu-Shao Liu, Long Chen and Chi-Wei Su. (2011). The Price Correlation between Crude Oil Spot and

Futures –Evidence from Rank Test. Energy Procedia, 5 , 998-1002. Retrieved from http://www.

sciencedirect.com/science/article/pii/S187661021101112X

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43. Bakanova (2011)141

evaluated the information content of an option-implied

volatility of the light, sweet crude oil futures traded at NYMEX. Dataset

for this study contains daily time series of light, sweet crude oil futures and

American-style options written on these futures which were traded at

NYMEX for the period from January 2nd

, 1996 through December 14th

,

2006. This measure of volatility was calculated using model-free

methodology that was independent from any option pricing model. They

found that the option prices contain important information for predicting

future re-analyzed volatility. They also found that implied volatility

outperforms historical volatility as a predictor of future realized volatility

and subsumes all information contained in historical data.

44. Salisu and Fasanya (2012)142

examined crude oil price volatility using daily data

for the period from 4th

April 2000 to 20th

March 2012. They considered

both the symmetric models GARCH(1,1) and GARCH-M (1,1)) and

asymmetric models TGARCH(1,1) and EGARCH(1,1). One interesting

innovation of the study was that it evaluated the volatility over three period

namely pre-global financial crisis, global financial crisis and post-global

financial crisis. They found that oil price was most volatile during the

global financial crises compared to other sub samples. Based on the

appropriate model selection criteria, the asymmetric GARCH models

appear superior to the symmetric ones in dealing with oil price volatility.

This finding indicated that evidence of leverage effects in the oil market

141

Asyl Bakanova. (2011). The information content of implied volatility in the crude oil futures market.

University of Lugano and Swiss Finance Institute February 28, 2011 , 1-18. Retrieved from http://

www.irmc.eu/public/files/Bakanova.pdf. 142

Afees A. Salisu and Ismail O.Fasanya. (2012). Comparative performance of volatility models for Oil

price. International Journal of Energy Economics and Policy, Vol.2, No.3 , 167-183. Retrieved

from http://www.econjournals.com/index.php/ijeep/article/view/235.

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and ignoring these effects in oil price modeling would lead to serious

biases and misleading results.

2.5 Empirical Studies on Long and Short Term Relationship between Energy

Commodities and Other Commodities / Parameters

45. Huang, Masulis and Stoll (1996)143

were of the opinion that crude oil price

volatility is having an important real effect on the U.S. economy. If such

an effect was presented, returns in oil futures should affect aggregate stock

returns. They examined the contemporaneous and lead-lag correlations

between daily returns of oil futures contracts and stock returns. The

association between oil volatility and stock market volatility was also

investigated. Surprisingly, in the period of the 1980s, there was virtually

no correlation between oil futures returns and the returns of various stock

indexes. In the case of specific oil stocks, there was contemporaneous

correlation and a statistically significant one day lead of oil futures returns.

However the economic significance of the lead was small. A simple

bivariate correlation of raw returns produces the same conclusions as a

more sophisticated multivariate VAR approach.

46. Beckmann and Czudaj (2002)144

analyzed the link between oil prices and dollar

exchange rates. Monthly dataset including the oil price and consumer

price index of USA as the foreign country as well as the CPI’s of ten

different states regarded as domestic countries. Their sample starts right

after the occurrence of a major oil price shock due to the Arab oil embargo

in 1973/74 and the breakdown of Bretton Woods. They considered Russia,

143

Huang, R., Masulis., R and Stoll., H. (1996). Energy Shocks & Financial Markets. Journal of Futures

Markets , 1-27. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=900741. 144

Joscha Beckmann and Robert Czudaj.(2012).Gold as an Inflation Hedge in a time-varying Coefficient

Framework. Retrived from http://dx.doi.org/10.4419/86/88416.

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Mexico, Canada, Norway, and Brazil as the major oil-exporting countries

as well as the Euro Area, Japan, South Africa, Sweden and the UK as oil-

importing countries and use their nominal exchange rates against the U.S.

dollar. They concluded that in nominal terms, a rise of the oil prices

coincided with an appreciation of the dollar against the Japanese yen, the

British pound, the Norwegian krone, the Mexican peso and the South

African rand. For Russia, Brazil, Canada, the Euro Area and Sweden a

nominal appreciation can be observed. Hence, a pattern of nominal

appreciation against the dollar can mostly be observed for oil exporting

countries while the nominal depreciation was detected for importing and

other exporting countries.

47. Shawkat, Diboogl and Aleisa (2004)145

suggested that the pure oil industry

equity system and the mixed oil price/equity index system offered more

opportunities for long-run portfolio diversification and less market

integration than the pure oil price systems. On a daily basis, in the oil price

systems all oil prices with the exception of the 3-month futures could

explain the future movements of each other. In the mixed system, none of

the daily oil industry stock indices could explain the daily future

movements of NYMEX futures prices, whereas these prices can explain

the movements of independent companies engaged in exploration,

refining, and marketing. The day effect for volatility transmission

suggested that Friday has a calming effect on the volatility of oil stocks in

general. The effect for Monday was not significant.

145

Shawkat Hammoudeh , Sel Diboogl and Eisa Aleisa. (2004). Relationships among U.S. oil prices and oil

industry equity indices. International Review of Economics and Finance, 13 , 427-453.Retrieved

from http://www.sciencedirect.com/science/article/pii/S105905600300011X.

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48. Chen, Finney and Lai (2005)146

studied long term and short term relationship of

spot markets of crude oil and refinery gasoline but also through their future

markets. They used the threshold error-correction model. The data from

overlapping future contracts were compiled, with rollover to the next

contract in the first week of each month. The retail prices were national

averages for self-serve regular unleaded gasoline reported by the EIA

weekly Motor Gasoline Price Survey. The sample data cover the period

January, 1991 through March, 2003. The start date of the sample was

dictated by the availability of the EIA survey data on retail prices.

Evidence showed that the observed asymmetry in price transmission

primarily occurred downstream – not upstream – of the transmission

process. They found this evidence in short and long term adjustment and

also across the spot and futures markets.

49. Cunado and Gracia (2005)147

studied the oil prices vs. macro economy

relationship by means of studying the impact of oil price shocks on both

economic activity and consumer price indexes for six Asian countries

(Japan, Singapore, South Korea, Malaysia, Thailand and Philippines) over

the period from 1st quarter of 1975 to 2

nd quarter of 2002. They used the

GARCH(1,1) model. The results suggested that oil prices have a

significant effect on both economic activity and price indexes, although

the impact was limited to the short run and more significant when oil price

shocks were defined in local currencies.

146

Li-Hsueh Chen, Miles FinneyT and Kon S. Lai. (2005). A threshold cointegration analysis of

asymmetric price transmission from crude oil to gasoline prices. Economics Letters 89 , 233-239.

Retrieved from http://www.sciencedirect.com/science/article/pii/S016517650500217X. 147

Cunado.J, F.Perez de Gracia. (2005). Oil prices, Economic activity and Inflation evidence for some

Asian countries. the Quarterly review of Economics and Finance , 65-83. Retrieved from

http://www.aiecon.org/advanced/suggestedreadings/PDF/sug53.pdf.

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50. Alizadeh, Nomikos and Pouliasis (2008)148

investigated the hedging

effectiveness of the Markov Regime Switching (MRS) models for

NYMEX WTI crude oil futures contracts. The data set for this study

comprised weekly spot and futures prices for three energy commodities

traded on NYMEX: WTI crude oil, unleaded gasoline and heating oil,

covering the period January 23, 1991to December 27, 2006, resulting 832

weekly observations. They introduced MRS VECM with GARCH error

structure. This specification linked the concept of disequilibrium with that

of high uncertainty across high and low volatility regimes. The results

indicated that using MRS models, market agents may be able to obtain

superior gains, measured in terms of both variance reduction and increase

in utility.

51. Miller and Ratti (2008)149

analyzed the long-run relationship between the world

price of crude oil and international stock markets over 1st January1971 to

31st March 2008 using a co-integrated VECM with additional regression.

Allowing for endogenously identified breaks in the co-integrating and

error correction matrices, they found evidence for breaks after May 1980,

January, 1988 and September, 1999. They found a clear long-run

relationship between these series for six OECD countries for January,

1971 to May, 1980 and February, 1988 to September, 1999, suggesting

that stock market indices respond negatively to increases in the oil price in

the long run. During June 1980 to January 1988, they found relationships

148

Alizadeh, A. H., Nomikos, N. K. and Pouliasis, P. K. (2008). A Markov Regime Switching Approach for

Hedging Energy Commodities. Journal of banking & Finance 32 , 1970–1983. 149

Isaac .J Miller and Ronald Ratti .A. (2008). Crude Oil and Stock Markets stability, Instability, and

Bubbles. Department of Economics, University of Missouri, Columbia. Retrieved from

http://economics .missouri.edu/ working-papers/2008/WP0810_millerz_ratti.pdf.

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that were not statistically significantly different from either zero or from

the relationships of the previous period. The expected negative long-run

relationship appears to disintegrate after September 1999. This finding

supported a conjecture of change in the relationship between real oil price

and real stock prices in the last decade compared to earlier years. This

suggests the presence of several stock market bubbles and/or oil price

bubbles since the turn of the century.

52. Ripple and Moosa (2009)150

analyzed the relationship between the volatility of

futures prices and the maturity of contracts, trading volume, and open

interest. The concept of open interest was introduced to find out whether

or not this additional measure of market activity was useful for explaining

volatility. The daily crude oil futures data were sourced directly from the

NYMEX for the period January, 1995 to December, 2005. They examined

the relation on a contract-by-contract basis or via time series analysis over

an eleven year period, open interest contributed significantly to the

explanation of futures volatility for the NYMEX crude oil contract.

53. Papapetrou (2009)151

studied the relationship between oil prices and economic

activity in Greece during the period 1st January, 1982 to 31

st August, 2008.

They used regime-switching model (RSR) and a threshold regression

modeling (TA-R) to estimate whether oil price changes affect

asymmetrically the economic activity. These models have the advantage to

150

Ronald D. Ripple, Imad A. Moosa. (2009). The effect of maturity, trading volume, and open interest on

crude oil futures price range-based volatility. Global Finance Journal, 20 , 209-219. Retrieved

from http:// www .sciencedirect.com/science/article/pii/S1044028309000568. 151

Evangelia Papapetrou. (2009). Oil Price Asymmetric Shocks and Economic Activity: The Case of

Greece. Economic Research Department, Bank of Greece. Retrieved from

http://www.aaee.at/2009-IAEE /uploads/ fullpaper_iaee09/P_489_Papapetrou_Evangelia_4-Sep-

2009,%2015:13.pdf.

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capture the dependence structure of the series both in terms of constant and

variance. The empirical evidence suggested that the degree of negative

correlation between oil prices and economic activity strengths during

periods of rapid oil price changes and high oil price change volatility.

54. Hamilton and James (2000)152

examined oil price volatility from 2003 and mid-

2008 driven by global demand shocks, whereas earlier oil price shocks

were primarily driven by exogenous oil supply shocks in the Middle East.

They used the GARCH(1,1). Though, this interpretation was not consistent

with a wide range of evidence, however, it indicated a central role for oil

demand shocks in all previous oil price shock episodes since 1972 except

the oil price shock triggered by the outbreak of the Iran-Iraq war in late

1980. They concluded that movements in oil prices affect exchange rates.

55. Wang, Ping and Huang (2010)153

used daily data and time series method to

explore the impacts of fluctuations in crude oil price, gold price, and

exchange rate of the US dollar vs. various currencies on the stock price

indices of the United States, Germany, Japan, Taiwan and China

respectively, as well as the long and short-term correlations among these

variables between January, 2006 to February, 2009. Empirical results

showed that there existed co-integration among fluctuations in oil price,

gold price and exchange rates of the dollar vs. various currencies and the

stock markets in Germany, Japan, Taiwan and China. They indicated that

there exist long-term stable relationships among these variables. Whereas

152

Hamilton and James D. (2000). What is an Oil Shock? Journal of Econometrics,113 (2) , 363-398.

Retrieved from http://www.nber.org/papers/w7755. 153

Mu-Lan Wang, Ching-Ping Wang and Tzu-Ying Huang. (2010). Relationships among Oil Price, Gold

Price, Exchange Rate and International Stock Markets. International Research Journal of Finance

and Economics , 80-89.

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there was no co-integration relationship among these variables and the

U.S. stock market indices which indicated that there was no long-term

stable relationship among the oil price, gold price and exchange rate and

the US stock market index. In addition, empirical results of the causal

relation showed that in Taiwan, for example, oil price, stock price and gold

price have two-way feedback relations.

56. Fattouh (2010)154

studied the WTI, Dubai, Brent and Brent-Dubai crude oil price

differentials which were modeled as a two-regime threshold autoregressive

(TAR) process during 1994 to 2008. While standard unit root tests

suggested that the prices of crude oil of different varieties move closely

together such that their price differential was stationary, the TAR results

indicated strong evidence of threshold effects in the adjustment process to

the long-run equilibrium. These findings suggested that crude oil prices

were linked and thus at the very general level, the oil market was ‘one

great pool’. However, differences in the dynamics of adjustment

suggested that within this one pool, oil markets were not necessarily

integrated in every time period and hence the dynamics of crude oil price

differentials may not follow a stationary process at all times.

57. Burçak (2010)155

studied six non-ferrous metals (aluminum, copper, lead, nickel,

tin, and zinc) to assess the forecasting performance of GARCH models. In

order to move as far away from the effects of 9/11, daily data for the

period from December 12, 2003 to December 15, 2008 was used for the

154

Bassam Fattouh.(2009). The dynamics of crude oil price differentials.Energy Economics 32, 334-342.

Retrieved July 25, 2012, from http://ac.els-cdn.com/S0140988309000978/1-s2.0-S01409883090

00978-main.pdf?_tid=f5bf0eb2-7369-11e2-82d600000aab0f27&acdnat=1360491150_59acb6ed9

d6bb6998a35b135da7670fc 155

Bulut Burçak. (2010). Volatility Spillover from World Oil Market To Non-Ferrous Metal Markets.

Thesis, Middle East Technical University , 1-50.

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data analysis. They found that the forecasting performances of GARCH,

EGARCH and TGARCH models were similar. However, they suggested

the use of the GARCH model because it was more parsimonious and has a

slightly better statistical performance than the other two. In the second

part, the prices of six non-ferrous metals and the price of crude oil were

used to examine the dynamic links between oil and metal returns by using

the BEKK specification of the multivariate GARCH model and the

Granger causality-in-variance tests. Results of their study agreed with the

previous studies in that the crude oil market volatility leads all non-ferrous

metal markets.

58. Zhang and Wei (2010)156

examined cointegration and causality, and investigate

their respective contribution, from the perspective of price discovery, to

the common price trend so as to interpret the dynamics of the whole large

commodity market and forecast the fluctuation of crude oil and gold prices

with significant positive correlation coefficient of 0.9295 during the

sampling period from January 2000 to March 2008 from China. Second,

there can be seen a long-term equilibrium between the two markets and the

crude oil price change linearly Granger causes the volatility of gold price,

but not vice versa. Moreover, the two market prices faced a significant

nonlinear Granger causality, which overall suggested their fairly direct

interactive mechanism. Finally, with regard to the common effective price

between the two markets, the contribution of the crude oil price seems

larger than that of the gold price, which implies that the influence of crude

156

Yue-Jun Zhang Yi-MingWei. (2010). The crude oil market and the gold market: Evidence for

cointegration, causality and price discovery. Resources Policy 35 , 168-177. Retrieved from

http://www.Science direct.com/science/article/pii/S0301420710000231.

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oil on global economic development proved more far-reaching and

extensive, and its role in the large commodity markets has attracted more

attention in recent years.

59. Simakova (2010)157

focused on the relationship between oil and gold prices with

a focus on analyzing and determining the character of the co-movement

between price levels using the monthly average price data from 1970 to

2010. They used WTI crude oil price quoted in dollars per barrel in the

Federal Reserve Economic Data portal. The price of gold was listed in

dollars per troy ounce which were collected from Kitco website. With 491

available observations in total, the data were analyzed using Granger

causality test, Johansen’s co-integration test and Vector Error Correction

model. They concluded that existence of a long-term relationship between

analyzed variables.

60. Ghaith and Awad (2011)158

attempted to investigate long-term relationship

between the prices of crude oil and food commodities represented by

maize, wheat, sorghum, soybean, barley, linseed oil, soybean oil and palm

oil. Time series econometric techniques such as unit root tests, co-

integration, and Granger causality were applied. The study utilizes

monthly data over the period from April, 1980 to March, 2009. The results

of this study revealed that there was a strong evidence of long-term

relationship between crude oil and the food commodity prices. A

157

Jana Šimáková. (2010). Analysis of the Relationship between Oil and Gold Prices. Silesian University in

Opava School of Business Administration in Karvina, Department of Finance, Czech Republic.

Retrieved from http://www.opf.slu.cz/kfi/icfb/proc2011/pdf/58_Simakova.pdf. 158

Ziad Ghaith and Ibrahim M.Awad. (2011). Examining the long term relationship between crude oil and

food commodity prices: co-integration and causality. International Journal of Economics and

Management Sciences, Vol.1, No.5 , 62-72.Retreived from http://www.managementjournals.org/

ijems/5/IJEMS-11-1425d.pdf.

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traditional Granger causality was used to check whether causality exists

between two product prices. The outcome suggested that there was

unidirectional causality between the price of crude oil and some of the

food commodities examined.

61. Le and Chang (2011)159

investigated the relationship between the prices of two

strategic commodities viz gold and oil in USA, using the monthly data

spanning from January, 2004 to April, 2004 with total of 304 observations

for each series. They used the pair wise Granger causality analysis, VAR

and Regression. Different oil price proxies were used for their

investigation and found that the impact of oil price on the gold price was

not asymmetric but non-linear. Further, results showed that there was a

long-run relationship existing between the prices of oil and gold. The

findings implied that the oil price can be used to predict the gold price.

62. Ojebiyi and Wilson (2011)160

assessed the existence of correlation between

exchange rate of Nigerian Naira and Unites States dollar and oil price on

the basis of monthly data from 1999 to 2009 using fundamental variables

viz., the monthly spot crude oil price, monthly exchange rate of Nigeria

Naira and monthly exchange rate of U.S. Dollar. The empirical result

adopted the ordinary least square using regression analysis and also the

correlation model. It is identified that there was a weak/negative

relationship between exchange rate and oil price as there were other

factors that brings about changes in oil price other than the exchange rate.

159

Thai-Ha Lei, Youngho Chang. (2011). Oil And Gold: Correlation Or Causation. Division of Economics,

Nanyang Technological University Singapore. Retrieved from http://depocenwp.org/upload/pubs/

22-%20Thai%20Ha%20Le-Oil_and_Gold_Correlation_or_causation_2011_yh_th.pdf. 160

Ademola Ojebiyi and david Olugbenga Wilson. (2011).Exchange rate volatility: an analysis of the

relationship between the Nigerian naira, oil prices, and US dollar. Passion and Science. Gotland

University. Retrieved from hgo.diva-portal.org/smash/get/diva2:420652/FULLTEXT01.

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The study also pointed out that the activities of cartel pricing policy and oil

speculators too have come to greatly affect the price of crude oil.

2.6 Empirical Studies in Energy Commodities with Focus on Indian Markets:

63. Khan and Salman (2010)161

investigated the relationship between the crude oil

and the stock market in terms of returns and volatility-spillover for the

BRIC countries by using co-integration and the VECM-MGARCH

technique. The data was obtained from Data Stream for the period from 2nd

February 2003 to 31st March 2010 as daily closing prices. The total

number of observation for each index was 1870. The data consisted of

Brent crude oil price basket, Bolsa Oficial de Valores de Sao Paula

(BOVESPA Index), Russian Trading System (RTS Index), Bombay Stock

Exchange (BSE Sensex Index) and Shanghai Stock Exchange (SSE

Composite Index). The results revealed that the oil and the market returns

were co-integrated in all the markets. The results from VECM indicated

stable, bidirectional, long-run relationship between oil prices and market

returns while short-run linkages were found to be absent in all the cases

except Russia where it significantly affects the Brent prices. They found

that overall BRICs have strong, stable, bidirectional and long-term

relationship with the Brent price index. They also studied the volatility

spillover effects and found that BRICs equity markets were highly

interconnected with crude oil market where shocks and spillover were

found to be significant and bidirectional.

161

Khan and Salman. (2010). Crude Oil Price shocks to Emerging Markets: Evaluating the BRICs Case.

MPRA (Munich Personal RePEc Archive) Paper No. 22978. Retrieved from http://mpra.ub.uni-

muenchen.de/22978/1/MPRA_paper_22978.pdf.

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64. Pushpa, Chakraborty and Mathur (2011)162

investigated the existence of long-

term relationships between oil prices and stock market prices of two big

emerging countries of Asia, India and China. Since India and China were

the major oil consuming market, their stock markets were likely to be

susceptible to oil price fluctuations. A data series from January, 2000 to

May, 2011 was considered. The stationarity of the data series were checked

using ADF test. Johansen co-integration model was applied to find out the

co-integration among the oil prices and stock prices of India and China. At

last the VECM was employed to trace the existence of long run relationship

between the variables. The results of the co-integration analysis depicted

long run relationship between oil prices and stock market prices for both

the countries. The Trace and maximum Eigen value tests results also

revealed the existence of unique co-integrating vectors between test

variables. This provided evidence on the existence of at least one co-

integrating vector in the model and therefore it could be concluded that the

variables exhibit a long-run association between them.

65. Chittedi (2011)163

investigated long run relationships between oil prices and stock

prices in India for the period April, 2000 to June, 2011. The oil price data

was collected from Petroleum Planning & Analysis Cell, Ministry of

petroleum, Government of India, where as BSE and NSE stock prices were

collected from respective websites. They employed autoregressive

162

Pushpa Negi, Anindita Chakraborty and Garima Mathur. (2011). Long term price linkages between the

Equity markets and oil prices: A Study of two big oil consuming countries of Asia. Middle Eastern

Finance and Economics, Issue 14. Retrieved from http://www.eurojournals.com/MEFE_14_13

.pdf. 163

Krishna Reddy Chittedi. (2012). Does oil price matters for Indian stock markets? An empirical analysis.

International journal of social sciences and interdisciplinary research. Vol.54. Retrieved from

http:// www.aebrjournal.org/uploads/6/6/2/2/6622240/1._chittedi.pdf

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distributed lag (ARDL) approach test to explore the long-run and short

relationships. The results projected India’s aggressive economic growth in

the past fifteen years, and the volatility of stock prices in India have a

significant impact on the volatility of oil prices and change in the oil

prices had impact on stock prices.

66. Bhunia and Mukhuti (2012)164

examined the short-term and long-term

relationships between BSE 500, BSE 200 and BSE 100 Indices of Bombay

Stock Exchange and crude oil price by using Johansen’s co-integration

test, VECM and Granger causality test. The study covered the period from

2nd

April, 2001 to 31st March, 2011. With data consisting of 2496 days.

The empirical results shown there was a co-integrated long-term

relationship between three index and crude oil price. Granger causality

results reveal that there was one way causality relationship from all index

of the stock market to crude oil price, but crude oil price was not causal to

each of the three indices.

67. Sharma, Singh, Manisha and Gupta (2012)165

analyzed effects of crude oil

prices on Indian economy. They collected the oil data from 1970 to 2012

and employed the Trend Analysis. The immediate effect of the oil price

shock was the increased cost of production due to increased fuel cost.

Whenever there was an over inflation in the economy, the cost of

production would also rise causing a decrease in supply. On the other

164

Amalendu Bhunia, Somnath Mukhuti. (2012). An Empirical Association between Crude Price and

Indian Stock Market. International Journal of Business and Management Tomorrow Vol. 2 No. 1 ,

1-7. Retrieved from http://www.ijbmt.com/issue/156.pdf 165

Anshul Sharma, Gurmmeet singh, Manisha Sharma and Pooja Gupta. (2012). Impact of crude oil price

on Indian economy. International journal of social sciences and interdisciplinary research Vol No.

54, ISSN 2277 , 95-99. Rtrieved from http://www.indianresearchjournals.com/pdf/IJSSIR/2012/

April/15.pdf

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hand, inflation implies a fall in the purchasing power of people. In short,

oil price fluctuation has adverse effects on the economy.

68. Goyal and Tripathi (2012)166

assessed the role of fundamentals compared to

liquidity and innovation driven expansion in net long positions, and the

effect of integration across exchanges. Data was taken over February, 2005

to June, 2010, since this was a period of high volatility in oil prices and

MCX commenced trading in crude oil futures on February 9, 2005. The

dataset included US WTI crude oil spot prices, UK Brent spot, MCX WTI

spot, monthly or daily close nearby futures prices on the three commodity

exchanges. They used first test for mutual Granger causality and lead-lags

in vector error correction models (VECM), between crude oil spot and

nearby futures prices on two international and one Indian commodity

exchange. If futures were found to affect spot, but not vice versa, it could

support the dominance of expectations mediated through financial markets

on prices. There was mutual Granger causality between spot and futures,

and in the error correction model for mature exchanges, spot leads futures.

Mature market exchanges lead in price discovery. But there was stronger

evidence of short-term or collapsing bubbles in mature market futures

compared to Indian market, although mature markets have a higher share

of hedging. Indian regulations such as position limits may have mitigated

short duration bubbles. Further they suggested that well-designed

regulations could improve the functioning of the market

166

Ashima Goyal and Shruti Tripathi. (2012). Regulations and price discovery: oil spot and futures markets.

Indira Gandhi Institute of Development Research, Mumbai, WP-2012-016 , 1-29. Retrieved from

http://www.igidr.ac.in/pdf/publication/WP-2012-016.pdf

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69. Sharma and Khanna (2012)167

analyzed the reaction of the stock market towards

the crude oil price changes. The study was based on the daily percentage

changes in oil prices and percentage changes in daily market returns as per

the stock market indices from 2008 to 2011. For this they considered

mainly three stock markets viz., NYSE, BSE, and LSE representing USA,

India and UK respectively. To judge the impact of crude oil price changes

on stock market, the study focused on establishing the relationship

between the market returns and oil prices. The study has taken the

percentage changes in the figures of both variables. For this purpose, tools

such as correlation, regression, and coefficient of determination were used

through SPSS statistical software. They found that the NYSE & LSE

returns were more relatively affected by the oil prices during the period

than Indian BSE market. As the study period also covered the crucial

period of recession, Euro zone financial crisis and some other political

imbalances, the NYSE and LSE markets have reacted very rapidly towards

such events.

70. Kumar and Pandey (2012)168

analyzed the cross market linkages in terms of

return and volatility spillovers in nine commodities consisting of two

agricultural commodities: Soybean, and Corn, three metals: Aluminum,

Copper and Zinc, two precious metals: Gold and Silver, and two energy

commodities: Crude oil and Natural gas. For agricultural commodities

daily prices of near month futures contracts from NCDEX and for non-

167

Sharma and Kirti Khanna. (2012). Crude Oil Price Velocity and Stock Market Ripple A Comparative

Study of BSE with NYSE & LSE. IJEMR-Vol 2 Issue 7 , 1-7.Retrieved from http://papers.ssrn.

com/sol3/ papers.cfm?abstract_id=2122258. 168

Brajesh Kumar and Ajay Pandey. (2011). International linkages of the Indian Commodity Futures

Markets. Indian Institute of Management Ahmedabad, Vastrapur, Ahmedabad , 1-26. Retrieved

from http:// www.scirp.org/Journal/PaperInformation.aspx?paperID=6414.

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agricultural commodities daily prices of near month futures contracts

traded on MCX were used. Return spillover was investigated through

Johansen’s co-integration test, error correction model, and Granger

causality test and variance decomposition techniques. They used Bivariate

GARCH model (BEKK) to investigate volatility spillover between India

and other world markets. They found that futures prices of agricultural

commodities traded at NCDEX and CBOT, prices of precious metals

traded at MCX and NYMEX, prices of industrial metals traded at MCX

and LME, and prices of energy commodities traded at MCX and NYMEX

were co-integrated. Results of return and volatility spillovers indicated

that the Indian commodity futures markets function as a satellite market

and assimilate information from the world market.

71. Tandon, Abuja and Neelamtandon (2012)169

established the effect of price

movements in the futures / derivative market of Brent crude oil on the

prices of shares of the companies involved in the exploration and

extraction of oil and natural gas and was listed on Indian stock markets.

The crude oil futures and the spot prices of oil production companies had

been collected from NSE, MCX, and NCDEX. The data for the study was

the value of future contract of Brent crude and the spot prices of the 2

upstream companies which were Oil and Natural Gas Corporation

(ONGC) and Gas Authority of India Limited (GAIL) and 2 downstream

companies which were Hindustan Petroleum Corporation Limited (HPCL)

169

Deepak tendon and Pulkit Ahuja and Neelamtndon. (2012).An empirical analysis of the price

movements of futures prices of Brent Crude Oil on the energy Sector companies in Indian bourses.

International Journal of Multidisciplinary Research, Vol.2 Issue 1, 32-47. Retrieved from

http://www.zenithresearch.org.in/images/stories/pdf/2012/Jan/ZIJMR/3%20Pulkit%20Ahuja%20

An%20Empirical%20Analysis%20of%20the%20Price%20movements%20of%20Futures%20Pric

es%20of%20Brent%20crude%20oil.pdf

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and Indian Oil Corporation Limited (IOCL). The near month futures

contracts were found to have the maximum influence on the spot prices of

the markets. The lead-lag relationship between spot prices and the near

month contract prices was then found using cross correlations and Granger

causality test. Co-integration was performed for the purpose of confirming

the co-integration of the variables analyzed. The results indicated that

crude oil futures variation has the maximum effect on the spot prices of

shares of energy sector companies and a lag period of 16 days is the most

appropriate prediction time which influences present day's spot prices of

shares of downstream oil and natural gas exploration companies. The spot

prices of upstream energy sector companies were not affected by the

futures contract of Brent crude.

2.7 Research Gap:

To summarize, although the body of oil and natural gas literature is substantial,

most of the earlier studies were concentrating on U.S. energy commodity derivatives

market especially the NYMEX. Further, most of the studies on U.S. markets concentrated

on WTI crude oil and Hunry hub natural gas. There is still substantial gap in literature

relating to studies pertaining to a comparative study on long and short term relationship of

Indian market and U.S. markets. This is particularly the case in the relation between spot

prices between crude oil and natural gas. While most studies agree on the importance of’

futures prices for financial markets, only a few studies, if any, agree on how, and why it is

important. The relation between futures and spot price has been the center of attention for

a large number of studies, and the literature is rich with several studies covering a range

of aspects with respect to this relationship. Lead-lag, efficiency, price prediction is the

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most studied areas in futures-spot literature170

. Most studies on international linkages

across spot and futures markets of the same underlying suggest that there are stronger

international market linkages in highly traded commodities like crude oil as compared to

relatively less traded commodities especially in natural gas. Moreover, the developed

markets with large volume play dominant role in price discovery process. Although the

body of literature is substantial related to crude oil and natural gas, there is still a great

deal of inconsistency in the findings. This is particularly the case in the relation between

spot prices171

.

Given the research gap on International linkages of spot markets in emerging

markets, this research work attempts to investigate the cross-market linkages of Indian

crude oil and natural gas spot market with developed world market viz. the U.S. oil and

gas market. From the reviews of earlier research focused on crude oil and natural gas

markets, we could able to figure out that research studies on analyzing the relationship

between Crude Oil and Natural Gas spot prices in a developing economy like India is not

yet attempted. Hence, the present study “Estimating Relationship and Forecasting

volatility & Spot Price of Crude Oil and Natural Gas in India and USA” attempts to fill

the research gap by focusing mainly to determine the existence of long term and short

term relationship between Indian and U.S. spot prices of crude oil & natural gas and the

possibility of forecasting Volatility and spot prices. More specifically this study attempts

to contribute to the empirical analysis on ascertaining the causal relationship (short term)

170

Kulkarni and Haidar.(2009).Forecasting model for crude oil price using artificial neural networks and

commodity futures prices. International Journal of computer science and Information security,

Vol.2, no.1.Retrieved from http://arxiv.org/ftp/arxiv/papers/0906/0906.4838.pdf?&lang= en_us

&output=json 171

Heping Pan, imad Haidar, siddhivinayak Kulkarni.(2009). Daily Prediction of short-term trends of crude

oil prices using neural networks exploiting multimarket dynamics. Higher Education Press and

Spriger-Verlag. Retrieved from http://download.springer.com/static/pdf/761/art%253A10.1007

%252Fs11704-009-0025-3.pdf?auth66=1362210351_ae02c31884d20d1d4a531632fbb15ab&ext=

.pdf

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and co-integration (long term) between crude oil & natural gas spot markets. Further, in

spite of several research findings which highlighted the importance and usefulness of

GARCH(1,1) model for forecasting of spot prices, there is sparse empirical research on

Indian crude oil & natural gas markets using this model. The study will also attempt to

contribute in terms of specifying an appropriate model to forecast volatility and spot price

using the econometric tool GARCH(1,1).

2.8 Concluding remarks:

After having reviewed the methodologies and approaches pursued in the earlier

researches pertaining to crude oil and natural gas markets, in the next chapter, we proceed

with furnishing details of the data sets and methodology adopted in the present study for

determining the nature of relationship and the possibility of forecasting U.S. and Indian

crude oil and natural gas spot prices.