1. Introduction

Commodity market denotes markets that trade in primary rather than industrial merchandises. Soft commodities are agricultural crops such as wheat, coffee and sugar. Hard commodities are mined goods such as gold, silver and crude oil. Stakeholders access about 50 main commodity markets internationally with chastely financial dealings progressively out stripping corporal trades in which goods are supplied. Derivatives contracts are the first way of participating in commodities. Commodity markets can include physical trading and derivatives trading using spot prices, forwards, futures, and options on futures. A financial derivative is a financial instrument whose value is derived from a commodity termed an underlying asset. Derivatives are either exchange-traded or in OTC markets. An increasing number of derivatives are traded via clearing houses some with Central Counterparty Clearing, which provide clearing and settlement services on a futures exchange, as well as off-exchange in the OTC market.

Derivatives such as futures contracts, Swaps and forward contracts have become the primary trading instruments in commodity markets. Futures are traded on regulated commodities exchanges.

Crude oil as a commodity, accounts for nearly 40% of the global energy demand and its consumption is estimated to be over 85 million barrels per day. Crude oil has wide application and global appeal. When refined, it gives an array of automobile fuels, lubricants, asphalt and petrochemical send-products like plastic, detergent, chemical fertilizer and rubber. Accordingly, changes in crude oil price have a sizeable impact on world economy. As price goes up, cost of transportation also amplifies. In turn, this raises costs of manufacturing and distribution, adversely affecting end-product prices. This has an industry-wide impact and adds to inflationary pressure. Thus, crude oil is the backbone of today's global economy and it is the largest traded commodity in the world. India is the world's 4th largest crude oil consumer with consumption at 3.1 million barrels per day. India imports almost 70% of its total consumption. Crude oil is the biggest component of India's import basket as well and its price affects overall economy. Rapid economic development is expected to further increase its consumption. Indian commodity market is growing phenomenally and crude oil is one of the most traded commodities on domestic bourses.

Crude abandoned thousands of investors when the prices tumbled from over $147 a barrel in July 2008 to less than $34 in Jan 2009. Many investors in India went bankrupt, but the fact that speculators concurrently made the price extraordinary and unrealistically high, is one major reason. Prices just exploded in a natural progression of demand and supply. Some earned while a majority lost their trust in Crude. But being a commodity without which the world can’t move, Crude is still in the top five. As is present in any market, traders of any commodity will also react to a change in global happenings by changing the price of said commodity. Especially in the wake of the recent financial crisis in US all the segments of worldwide financial markets reacted badly and have led to huge losses. The motive of the present study is to analyze this aspect of impact with regard to the Indian crude oil spot and futures markets by a causal study of the commodity futuresmarket.

2. Review of Literature

The review is done under two perspectives. viz., studies related to Commodity markets and on research applying econometric analysis.

^[12] tested the price discovery process of the nascent gold futures contracts in the Multi Commodity Exchange of India () over the period 2003 to 2007 by employing vector error correction models to show that futures prices of both standard and mini contracts lead spot price.

^[17] established a causal relationship between the nominal exchange rate and foreign direct investment in India using a time series data between 1992 and2010. They employed unit root test, Johansen cointegration test and Vector Auto regression (VAR) model to show whether the variables under consideration exhibit stationarity and a long run association respectively. The findings exhibited the absence of long term association between the two variables under consideration.

^[9] examined to discern the long-run relations between FDI and economic development in China in the comprehensive framework, which incorporates determinants as output, FDI, capital formation, employment, human capital and international openness using Impulse Response, Variance Decomposition, Johansen Co-integration and VECM. The findings indicated that in the long run, FDI tends to decrease economic growth; economic development in China seems to be fueled by domestic capital accumulation and employment growth; FDI inflows do crowd out domestic capitals, and reduce employment growth, whilst the latter increases the former.

^[18] carried an empirical research on the influence of real exchange rate of RMB’s volatility on US FDI in China, adopting GARCH model, the VAR model and cointegration theory, based on quarterly data from 1994 to 2009.The result revealed that there was a stable relationship among the volatility of real exchange rate, real exchange of RMB and US foreign direct investment in China.

^[19] focused on price dynamics of depositary receipts (DRs) issued by Taiwanese and Hong Kong firms. The empirical results using VECM and indicated that long-term equilibrium relationships between depositary receipts and underlying security prices exist for firms listed in Hong Kong, a free-entry market, but do not necessarily exist for firms listed in Taiwan with foreign ownership restrictions.

^[6] examined the role of futures market in the price discovery process using a two-regime threshold vector autoregression (TVAR) and a two-regime threshold autoregression for six commodities. The findings revealed that the rate of convergence of information is slow, particularly in the non-expiration weeks and also finds evidence for price discovery process happening in the futures market in five out of six commodities.

^[1] analyzed two competing models in price volume relationships in Indian commodity futures market using correlation coefficient and Granger causality test with vector auto regressive methodology. The Findings exhibited contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it followed that SIH is supported.

Financial investors are generally most active in futures markets, rather than spot markets, as they do not want to take delivery of the physical commodity, which is expensive to store and to finance. Instead, the role of financial investors is to act on informed views on the prospects for supply and demand as well as to be paid to take on the commodity price risk that producers, and to a lesser degree consumers, wish to hedge. There are two broad channels through which commodity futures markets can affect the production and consumption decisions of participants in spot markets: (i) they allow firms to hedge their exposures to movements in spot prices, thereby smoothing their consumption expenditure and/or production cash flows over time and lowering the cost of capital; and (ii) they provide a potential source of influence over spot prices. If the sole function of futures markets was to provide hedging services to producers and consumers, the welfare implications would be unambiguously positive ^[20].

Our contribution to the extant literature in the crude market analyses this issue by discussing the causal relationship between spot and futures prices during the financial crisis period.

The remainder of the paper is structured as follows. Our econometric model and elucidations of our technique are presented in section 3.Section 4 discusses our main empirical results while Section 5 concludes.

3. Methodology

The data set consists of daily closing futures price and trading volume of crude oil futures contracts between January 2007 and Dec 2009 and it is obtained from the MCX website www.mcxindia.com. Eviews 6.0 package is used for arranging the data and implementation of econometric analyses.

The vector autoregressive model (VAR) is estimated using time series that have been transformed to their stationary values. Stationarity of each data series is examined with ADF test with intercept and KPSS test. Both the tests confirm that price series and volume series are nonstationary but become stationary after taking the first difference. Hence, it can be concluded that both the series have unit root. Thus, as per VAR framework of Granger causality, it becomes necessary to carry out the causality test on their first difference so that both the series are stationary. For a set of n time series variables, a VAR model of order p (VAR(p)) can be written as:

(1)

where the are (nxn) coefficient matrices and is an unobservable i.i.d. zero mean error term.

^[21] has pointed out that the Granger causality test is very sensitive to the number of lags used in the analysis. ^[22] suggested using more rather than fewer lags.

In view of this, we have used lag length based on Akaike information criteria and Schwarz information criteria to have a robust conclusion. For a two-variable VAR(1) with k=2.,

(2)

(3)

with and

In matrix form:

(4)

Impulse response analysis inspects and evaluates the impact of shocks cross-section. Variance decomposition disintegrates unit incremental shock of each variable for certain proportion to its own reason and other variables contribution.

4. Discussion of Results

To model Contracts and Spot Price we have to choose the order, p, VAR (p) based on Akaike and Schwarz Criterion. The value of AIC, LR, HQ & FPE is lower in eighth lag (Table 1). So Lag length for VAR is eight.

Table 1. VAR Lag Order Selection

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Table 2. VAR Estimates

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The crude price series are well fitted by the VAR model. The VAR model manages to capture the behavior of the crude trading volume and the crude spot prices. The explanatory power of VAR CONTRACT equation is good as the value of R² is high (0.740341)( Table 1). It shows that CONTRACT influence on its own variable is strong and remains throughout 8 lags. But none of the lag of contract had any significant effect on Spot prices which shows that contracts do not influence spot prices.

For SPOT prices equation the R² value is low (0.070029) which shows that the variability of spot price depends on other variables. One day lag is significant for spot prices. Spot price does not influence Contract price. From the tabulated values we can conclude that Contracts influence contract alone and spot prices don’t influence Contract and only one day influence is present with its own variable. Other variables influence on Spot price is more evident.

Table 3. VAR Granger Causality/Block Exogeneity Wald Tests

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The two hypotheses relating to contracts and spot prices; that SPOTPRICE do not granger cause CONTRACT, CONTRACT do not granger cause SPOTPRICE are rejected due to significant Chi square value (Table 3). Therefore the causality appears to be bidirectional between spot and contract.

Table 4a. Variance Decomposition of Contracts

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The error variance in forecasting (Table 4a) starts due to innovations in contract to contract and is close to 100% at all time horizons but contract to spot starts with zero and it rises slowly. The error variance in Spot price (Table 4b) due to innovation in Spot price is close to 100% throughout the time horizon but the spot price to contract starts with very less and started increasing steadily reflecting time to build effect.

The cross variable impulse response function (Figure 1) shows that an innovation in contract produces no movement in spot price at first and has very slight variation till ten days. In the same manner Spot Price changes contracts in third day slightly and remains slight variation. The spot price impulse response own variable is large for one day and decays after that. It confirms table -2 VAR statistics. Contract price response to its own variable is very strong up movement on first day and came down and reverses on the next day. It implies the relationship is very volatile.

Table 4b. Variance Decomposition of Spot price

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Figure 1. Cross Variable Impulse Response Function

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5. Conclusion

The present study explored the relationship between spot and future prices in the Indian crude oil market especially during the recent US financial crisis, applying econometric techniques. This analysis is relevant to understand whether during turbulent times trading volume overplays fundamentals in the crude market as it is a highly sought after commodity with consistently increasing demand. From the Granger causality results it is evident that for Crude oil bidirectional causality is found from spot prices to futures trading volume for all the data series. The VAR results show that future Contracts’ influence on its own variable is strong. But none of the lag of contract had any significant effect on Spot prices which shows that future contracts do not influence spot prices. The cross variable impulse response function shows that an innovation in futures contract produces no movement in spot price at first and has very slight variation till ten days. Overall, we conclude that during crisis, there is no clear evidence that the futures trading in the Indian crude oil market has had an enveloping effect on crude prices; instead, the evidence is consistent with the fact that other global and macroeconomic factors could be the key determinants of crude oil prices.

References

[1]	Biswas, S., & Rajib, P. (2011),"Testing price volume relationships for Indian commodity futures", Journal of Indian Business Research, Vol. 3 Iss: 2 pp. 117-131.
	In article		CrossRef
[2]	Ekeland, I., Lautier, D., & Villeneuve, B. (2012).A simple equilibrium model for a commodity market with spot trades and futures contracts.
	In article
[3]	Frankel JA and AK Rose (2009),‘Determinants of Agricultural and Mineral Commodity Prices’, in R Fry, C Jones and C Kent (eds), Inflation in an Era of Relative Price Shocks, Proceedings of a Conference, Reserve Bank of Australia, Sydney, pp 9-43.
	In article		PubMed
[4]	Garbade, K.D. and Silber, W.L. (1983), ‘‘Price movement and price discovery in futures and cash markets’’, The Review of Economics and Statistics, Vol. 65 No. 2, pp. 289-97.
	In article		CrossRef
[5]	Granger, C. (1969), “Investigating causal relations by econometric models and cross-spectral methods”, Econometrica, Vol. 37, pp. 424-38.
	In article		CrossRef
[6]	Iyer, V., & Pillai, A. (2010),"Price discovery and convergence in the Indian commodities market", Indian Growth and Development Review, Vol. 3 Iss: 1 pp. 53-61.
	In article		CrossRef
[7]	Karpoff, J. (1987), “The relation between price changes and trading volume: a survey”, Journal of Financial and Quantitative Analysis, Vol. 22, pp. 109-26.
	In article		CrossRef
[8]	Kumar, S. (2004), ‘‘Price discovery and market efficiency: evidence from agricultural commodities futures markets’’, South Asian Journal of Management, Vol 11 No. 2.
	In article
[9]	Liyan, L. (2011) FDI and Economic Development in China: 1978-2008, Management and Service Science (MASS), 2011 International Conference on 2011, IEEE, pp. 1-4.
	In article
[10]	Miller, J. I., &Ratti, R. A. (2009). Crude oil and stock markets: Stability, instability, and bubbles. Energy Economics, 31(4), 559-568.
	In article		CrossRef
[11]	Moosa I A (2002), “Price Discovery and Risk Transfer in the Crude Oil Futures. Market: Some Structural Time Series Evidence”, Economic Notes, Vol. 31, pp. 155-165.
	In article		CrossRef
[12]	Pavabutr, P., &Chaihetphon, P., (2010), Price discovery in the Indian gold futures market, Journal of Economics and Finance 34:455-467.
	In article		CrossRef
[13]	Shihabudheen M T and Padhi P (2010), “Price Discovery and Volatility Spillover Effect in Indian Commodity Market”, Indian Journal of Agricultural Economics, Vol. 65, No.1, pp. 101-117.
	In article
[14]	Praveen D G and Sudhakar A (2006), “Price Discovery and Causality in the Indian Derivatives Market”, ICFAI Journal of Derivatives Markets, Vol. 3, No.1, pp. 22-29.
	In article
[15]	Srinivasan P (2009), “An Empirical Analysis of Price Discovery in the NSE Spot and Future Markets of India”, The ICFAI Journal of Applied Finance, Vol. 15, No.11, pp. 24-36.
	In article
[16]	Srinivasan, P., Price Discovery and Volatility Spillovers in Indian Spot-Futures Commodity Market (September 27, 2012). The IUP Journal of Behavioral Finance, Vol. IX, No. 1, March 2012, pp. 70-85.
	In article
[17]	Gokhale, M. S., &Raju, J. R. (2012). Empirical Analysis of the Causal Relationship between Nominal Exchange Rate and Foreign Direct Investment in India Using VAR (Vector Autoregression Model). Global Journal of Management And Business Research, 12(22).
	In article
[18]	Weiguo, X., & Yang, Z. (2010, December). Empirical research on the influence of real exchange rate volatility on US FDI in China. In Information Science and Engineering (ICISE), 2010 2nd International Conference on (pp. 222-225). IEEE.
	In article
[19]	Tsai, B. H., & Li, S. H. (2004). The effect of foreign ownership restrictions on the price dynamics of depositary receipts—evidence from the Taiwan and Hong Kong markets. Journal of Accounting, Auditing & Finance, 19(3), 301-330.
	In article
[20]	Dwyer, A., Holloway, J., & Wright, M. (2012). Commodity market financialisation: A closer look at the evidence. The Recent Economic Performance of the States 1 Trends in National Saving and Investment 9 The Distribution of Household Wealth in Australia: Evidence from the 2010 HILDA Survey 19 India’s Steel Industry 29, 65.
	In article
[21]	Gujarati, D., 1995. Basic Econometrics, New York: McGraw Hill, Inc.
	In article
[22]	Davidson, R., and J. G. MacKinnon, Estimation and Inference in Econometrics. Oxford University Press, 1993.
	In article