chapter 2 review of literature - information and...
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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
61
(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
65
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
67
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.
74
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.
75
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.
76
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.
77
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.
78
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.
79
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
80
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
81
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
82
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
83
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
84
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
85
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.
86
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.
87
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.
88
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.
89
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.
90
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.
91
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.
92
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.
93
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.
94
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.
95
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.
96
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.
97
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
98
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
99
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
100
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.
101
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
102
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
103
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=
104
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.