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1 CHAPTER III REVIEW OF LITERATURE

2 CHAPTER III REVIEW OF LITERATURE In this chapter, the past studies related to the subject of the current research are reviewed. It includes studies on commodity market in India as well as the studies in the context of other developed and emerging markets. Most of these studies examine the interrelationship between the spot and futures market or the volatility in the individual commodities. A review of these studies bring out the methodological tools and the implications of futures market efficiency in various markets. 3.1 STUDIES IN THE INTERNATIONAL COMMODITY MARKETS The predictive content of commodity futures was reported by Chinn and Coibion (2013). The study examined the predictive content of futures prices for energy, agricultural, precious and base metal commodities. In particular, the authors examined whether futures prices are (1) unbiased and/or (2) accurate predictors of subsequent prices. The study documented significant differences, both across and within commodity groups. Precious and base metals fail most tests of unbiasedness and are poor predictors of subsequent price changes, but energy and agricultural futures fare much better. A little evidence that these differences reflect liquidity conditions across markets was found. In addition, a broad decline in the predictive content of commodity futures prices since the early 2000s was reported. Al-Fattah (2013) developed a new predictive model, WTI futures price volatility and WTI spot prices volatility, to forecast the global oil price volatility applying artificial intelligence with artificial neural network (ANN) modelling technology. Using historical 45

3 oil market data these models were designed, verified and tested. The approximations and predictions from the ANN models, intimately match the historical data of WTI from January 1994 to April The outcome of this work is that greater price stability of oil prices reduces uncertainty in energy markets, which leads to the benefits for consumers and producers. Karali (2012) simultaneously measured the impact of selected USDA reports on the conditional variances and co-variances of returns on corn, lean hogs, soybeans, soybean meal, and soybean oil futures contracts using a multivariate GARCH model. It was shown that the largest movements in co-variances were observed on the release days of Feed Outlook, Grain Stocks, and Hogs and Pigs reports. The results suggested that the selected USDA reports have stronger implications for soybean meal return variance in the near term than in the longer term. Dwyer, Holloway and Wright (2012) debated whether financial investors have caused inordinate gains in the level and volatility of commodity prices. These investors are considered, by some as constituting less pertained with fundamentals than traditional market players and hence, obstructing the price discovery process which are destabilising speculators or noise traders. The relationship between the futures markets for commodities and the spot markets are discussed in this research work. It concludes that the evidence does not support the hypothesis that financialisation has been the main driver of commodity price developments in the 2000s. Chinn and Coibion (2012) examined the predictive content of futures prices for a broad range of commodities, including energy, precious and base metals, and agricultural 46

4 commodities. This study investigated whether futures prices were an unbiased and/or accurate predictor of subsequent spot prices. Significant differences, both across and within commodity groups were documented. Precious and base metals fail in most of tests of unbiasedness and were poor predictors of subsequent price changes. In contrast, energy futures and to a lesser extent, agricultural futures fare much better. Little evidence was found that these differences reflect liquidity conditions across markets. In addition, a broad decline in the predictive content of commodity futures prices since, the early to mid-2000s was authenticated. Bredin, Ciagain, and Muckley (2012) studied the EU Emissions Trading Scheme options and futures market dynamics during the period 2005 to The observations on returns, volatilities and volumes of derivative instruments were analyzed. In addition to that the study investigated the spot/future correlations, term structures and option entailed volatility smiles and surfaces. The authors ascertained whether the behavior of the EU ETS derivatives markets can be equated to that of commodity markets, specifically the formulated West Texas Intermediate (WTI) crude oil derivatives market. The results suggested that, since, the start of Phase 2 of the Scheme, the EU Emissions Trading Scheme derivatives markets have matured markedly with rising volumes and declining return volatilities. With certain discrepancies, the spot/future correlations, term structures and option volatility smiles and surfaces demonstrated comparable behavior over time. Wong et al., (2011) considered calibration to forward-looking betas by extracting information on equity and index options from prices using Levy models. The resulting calibrated betas are called Levy betas. The objective was to capture market expectations for future betas through option prices, as the betas estimated from historical data may fail 47

5 to reflect structural changes in the market. A continuous-time capital asset pricing model (CAPM) with Levy processes was assumed and derived an analytical solution to index and stock options, thus permitting the betas to be implied from observing option prices. One application of Levy betas was to construct a static hedging strategy using index futures. Employing Hong Kong equity and index option data from September 16, 2008 to October 15, 2009, it was shown empirically that the Levy betas during the sub-prime mortgage crisis period was much more volatile than those during the recovery period. Evidence was found to suggest that the Levy betas improve static hedging performance relative to historical betas and the forward-looking betas implied by a stochastic volatility model. Kumar and Pandey (2011), with futures markets outside India, looked into the cross market linkages of Indian commodity futures for nine commodities. These commodities range from highly merchandisable commodities for less merchandisable agricultural commodities. The cross market linkages in terms of return and volatility spillovers were analyzed. The return spillover was examined using the Johansen s cointegration test, error correction model, Granger causality test and variance decomposition techniques. Bivariate GARCH model (BEKK) was applied to investigate volatility spillover between India and other World markets. It was found that futures prices of agricultural commodities traded on the National Commodity Derivatives Exchange, India (NCDEX) and Chicago Board of Trade (CBOT), prices of precious metals traded on the Multi Commodity Exchange, India (MCX) and NYMEX, prices of industrial metals traded at MCX and the London Metal Exchange (LME) and prices of energy commodities traded at MCX and NYMEX were cointegrated. It was found, in the 48

6 case of commodities, that world markets have unidirectional impact on Indian markets. The bi-directional return spillover between MCX and LME markets was found. Juhl et al., (2011) compared two alternative regression specifications for sizing hedge positions and measuring hedge effectiveness: a simple regression on price changes and an error correction model (ECM). It was shown that, when the prices of the hedged item and the hedging instrument are co-integrated, both specifications yield similar results which depend on the hedge horizon (i.e., the time frame for measuring price changes). The estimated hedge ratio and regression (R 2 ) will both be small when price changes were measured over short intervals, but as the hedge horizon is lengthened both measures will converge to one. The results implied that, when prices were co-integrated, a longer hedge horizon will yield an optimal hedge ratio closer to one, while at the same time enhancing the ability to qualify for hedge accounting. Wu et al., (2010) used a volatility spillover model and found evidence of significant spillovers from crude oil prices to corn cash and futures prices, and that these spillover effects were time-varying. The result revealed that corn markets have become much more connected to crude oil markets after the introduction of the Energy Policy Act of Furthermore, when the ethanol gasoline consumption ratio exceeds a critical level, crude oil prices transmit positive volatility spillovers into corn prices and movements in corn prices were more energy-driven. Based on this strong volatility link between crude oil and corn prices, a new cross-hedging strategy for managing corn price risk using oil futures was examined and its performance was studied. Results showed that this cross-hedging strategy provided only slightly better hedging performance compared with traditional hedging in corn futures markets. 49

7 Sari, Hammoudeh and Soytas (2010) examined the co-movements and data transmission between the spot prices of four precious metals (gold, silver, platinum, and palladium), oil price, and the US dollar/euro exchange rate. The evidence of a weak longrun equilibrium relationship, but strong feedbacks in the short run was found. The spot precious metal markets acknowledge temporarily significant to a shock in the exchange rate. Moreover, evidence of market over reactions in the palladium and platinum cases as well as in the exchange rate market was discovered. The study conclusions that the investors may radiate a portion of the risk off by investing in precious metals. Lizardo and Mollick (2010) found by adding oil prices to the monetary model of exchange rates that oil prices importantly justify movements in the value of the U.S. dollar (USD) against major currencies from the 1970s to The long-run and forecasting results were outstandingly reproducible with an oil-exchange rate relationship. Increases in real oil prices contributed to a significant depreciation of the USD versus net oil exporter currencies, such as Canada, Mexico, and Russia. Kawamoto and Hamori (2010) investigated market efficiency and unbiasedness among futures was defined and the concept of consistently efficient (or consistently efficient and unbiased) market within n-month maturity was introduced. Market efficiency and unbiasedness among WTI futures with different maturities were tested using cointegration analysis, and short-term market efficiency using an error correction model and GARCH-M-ECM. The results showed that WTI futures were consistently efficient within 8-month maturity and consistently efficient and unbiased within 2-month maturity. 50

8 Chang et al., (2010) examined the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC, BEKK and diagonal BEKK, for the crude oil spot and futures returns of Brent and WTL to calculate optimal portfolio weights and optimal hedge ratio, and to suggest a crude oil hedge strategy. The results showed that the optimal portfolio weights of all multivariate volatility models for Brent suggested holding futures in larger proportions that spot. It was found for WTL that DCC, BEKK and diagonal BEKK suggested holding crude oil futures to spot, but CCC and VARMA-GARCH suggested holding crude oil spot to futures. The effect of maturity, trading volume and open interest in crude oil futures price range-based volatility was determined by Ripple and Moosa (2009). The determinants of the volatility of crude oil futures prices were examined using an intra-day range-based measure of volatility. The paper employed two distinct approaches: one was to present a contract-by-contract analysis within the sample period, and the second was based on constructing series for the near-month and next-to-near month contracts over the entire sample period. The contract-by contract analysis revealed that trading volume and open interest were significant determinants of volatility that dominate the Samuelson maturity effect. The results supported earlier findings of a positive and significant role for trading volume, and they also showed the importance of open interest in determining volatility, exerting a significant negative effect. The full-period time series analysis also demonstrated the significant role played by open interest in the determination of futures price volatility, further confirming the importance of trading volume. Long and Wang (2008) studied the dynamic relationship among futures price, spot price of Shanghai metal and the futures price of London with the co-integration 51

9 theory, Granger causality tests, residue analysis, impulse response function, and variance decomposition on the VECM. The findings showed that futures price, spot price of Shanghai metal and the futures price of London has the long equilibrium relationship: the copper futures price of Shanghai has internalities to the futures of London; the Aluminium futures price has externalities; the three have different price discovery functions. Lien and Yang (2008) assessed different hedging strategies for aluminium and copper futures contracts traded at Shanghai Futures Exchange. In addition to usual candidates such as the traditional regression hedge ratio and the hedging strategy constructed from bivariate fractionally integrated generalized autoregressive conditional heteroskedasticity (BFIGARCH) model, two advanced specifications were proposed to account for impacts of the basis on market volatility and co-movements between spot and futures returns. Empirical results suggested that the basis has asymmetric effects and an optimal hedging strategy constructed from the asymmetric BFIGARCH model tends to produce the best in-sample and out-of-sample hedging performance. Ferretti and Gilbert (2008) considered the dynamic representation of a spot and three-month Aluminium and copper volatilities. Aluminium and copper are the two most important metals traded on the London Metal Exchange. They share common business cycle factors and were traded under identical contract specifications. The bivariate FIGARCH model, which allowed parsimonious representation of long memory volatility processes, was applied. The results show that spot and three-month aluminium and copper volatilities follow long memory processes, that they exhibit a common degree of fractional integration and that the processes were symmetric. However, there was no evidence that the processes were fractionally cointegrated. 52

10 Cotter and Hanly (2008) modelled whether hedging effectiveness was affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using crude oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value at Risk (CVAR). Comparisons were applied to a number of hedging strategies, including Ordinary Least Squares (OLS) and both Symmetric and Asymmetric GARCH models. It showed that asymmetry reduces in-sample hedging performance and that there were significant differences in hedging performance between short and long hedgers. Thus, tail specific performance metrics were applied in evaluating hedging effectiveness. It was also found that the OLS model provides consistent good performance across different measures of hedging effectiveness and estimation methods irrespective of the characteristics of the underlying distribution. Bekiros and Diks (2008) investigated the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data covered two periods October October 1999 and November 1999-October 2007, with the latter being significantly more turbulent. The conventional linear Granger test, a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration and traditional pairwise analysis were used. To check the nonlinearity, the nonlinear causal relationships of VECM filtered residuals were examined. The hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model was investigated. When the linear causal relationships disappeared after VECM cointegration filtering, nonlinear causal linkages in some cases persisted even after 53

11 GARCH filtering in both periods. The results indicated that spot and futures returns may exhibit asymmetries and statistically significant higher order moments. Moreover, the results implied that if nonlinear effects were accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time. Ripplea and Moosa (2007) examined the effect of the maturity of the futures contract used as a hedging instrument. Daily and monthly data on the West Texas Intermediate (WTI) crude oil futures and spot prices were used to work out the hedge ratios and the measures of hedging effectiveness.the study used the near-month contract and a more distant (6-month) contract in order to calculate various measures. The results showed that futures hedging were more effective when the near-month contract was used. The hedge ratios were lower for near-month hedging. Silvapulle and Moosa (1999) examined the relationship between the spot and futures prices of WTI crude oil using a sample of daily data. Linear causality testing revealed that futures prices lead spot prices, but nonlinear causality testing revealed a bidirectional effect. Results of the study suggested that both spot and futures markets react simultaneously to new information. Regnier (2007) conceived that since, 1973 oil crisis, oil and energy prices have been more volatile than other commodity prices. This study reviewed monthly producer prices for thousands of products over the period January 1945 through August The results indicated that, over prices for about 95% of products sold by domestic producers, crude oil, refined petroleum, and natural gas prices were more volatile. The price and open interest in Greek stock index futures market was studied by Floros (2007). This article examined the relation between price and open interest in the 54

12 Greek stock index futures market. The focus was on GARCH effects and the long-run information role of open interest. The results showed that current open interest helps in explaining GARCH effects, while a negative impact on returns is reported. Furthermore, evidence of the cointegration tests showed that there is a long-run relation between open interest and the futures price. It was suggested that one can use the information of open interest to predict futures prices in the long run. These findings were strongly recommended to financial managers dealing with Greek stock index futures. The futures trading activity and commodity cash price volatility were reported by Yang, Balyeat and Leatham (2005). The study examined the lead-lag relationship between futures trading activity (volume and open interest) and cash price volatility of major agricultural commodities. Granger causality tests and generalized forecast error variance decompositions showed that an unexpected increase in futures trading volume unidirectionally causes an increase in cash price volatility in most commodities. Likewise, there was a weak causal feedback between open interest and cash price volatility. It was found that the destabilizing effect on futures trading in agricultural commodity markets. Watkins and McAleer (2005) investigated the volatility of a market index relative to the volatility of its underlying assets by analyzing correlation matrices derived from rolling AR(1)-generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model estimates. The second moment properties of a linear aggregate of ARMA processes with GARCH errors were analyzed and compared with the properties of the individual return series. Empirical application was made in the markets for non-ferrous metals on the London Metal Exchange (LME). The volatility of the LME Base Metals 55

13 Index (LMEX) was modelled and compared with the volatility of the 3-month futures contracts for aluminium, copper, lead, nickel, tin, and zinc. It was found that the long-run persistence correlation between the index and both of the major components was high, although the correlation for copper was lower than the correlations for α and β estimates. In contrast, the long-run persistence correlation between the index and aluminium was substantially greater than for both the α and β estimates. This paper has demonstrated that, by examining the correlation of the α and β estimates, their robust t-ratios, second and fourth moment conditions, and estimated conditional variances, disparate volatility effects were evident across the main industrially used non-ferrous metals futures markets. Such disparate effects result in complex relationships when individual futures prices were aggregated to a market index, such as the LMEX. Kenourgios et al., (2004) investigated that the joint hypothesis of market efficiency and unbiasedness of futures prices for the copper futures contract traded on the London Metal Exchange. This contract was of particular importance given to the usage and properties of the underlying commodity and its highest share of trading during the last decade, in an exchange which was the centre of the world s trading in copper. The data contained prices from two different copper futures contracts (three and fifteen months maturity) covering the decade of 1990s, a very volatile and turbulent period for the copper market worldwide. Unlike previous studies, both long-run and short-run efficiency using cointegration and error correction model were tested. The results showed that the market was not efficient and do not provide unbiased estimates of future copper spot prices, which has important implications for the users of this market. 56

14 Chen and Lin (2004) applied linear and nonlinear Granger causality tests to examine the dynamic relation between the London Metal Exchange (LME) cash prices and three possible predictors. The analysis used to match the quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. The effects of cointegration on both linear and nonlinear Granger causality tests were also examined. When cointegration was not modelled, evidence was found of both linear and nonlinear causality between cash prices and predictor variables. However, after controlling for cointegration, evidence of significant nonlinear causality was no longer found. These results contributed to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. The importance of interest rate and inventory as well as futures price in forecasting cash prices was also illustrated. Failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply. Giota and Laurent (2003) documented Value-at-Risk models relevant for commodity traders who have long and short trading positions in commodity markets. In a 5-year out-of-sample study on aluminium, copper, nickel, Brent crude oil and WTI crude oil daily cash prices and cocoa nearby futures contracts, the study assessed the performance of the Risk Metrics, skewed Student APARCH and skewed student ARCH models. The skewed Student APARCH model performed best in all cases. The skewed 57

15 Student ARCH model delivered good results and its estimation did not require non-linear optimization procedures. Heaney (2002) noted the use of futures prices to predict commodity cash prices was important both to practitioners and researchers yet the literature provided conflicting results on the ability of futures prices to predict cash prices. Brenner and Kroner (1995) argued that if the cost of carry model applies to commodity futures pricing then current futures prices may not accurately predict subsequent cash prices. Inventory, cash price return variance, cash price return first order auto-correlation and interest rates were used to proxy carrying costs in a test of the ability of commodity futures prices to predict cash prices. Various predictive models relating futures price to cash price were described, including univariate and multivariate error correction models. London Metal Exchange lead cash prices, lead futures prices, lead inventory and UK Treasury bill rates were collected over the period 1964 to Analysis of this data confirmed the importance of the cost of carry model elements as well as the futures price in forecasting cash prices. The result was supportive of the predictions of Brenner and Kroner (1995). Further, there was evidence of statistically significant short run effects, including a negative relationship between cash price change and lagged inventory level change and a positive relationship between cash price change and lagged change in futures price. The price volatility, trading volume and market depth: evidence from the Japanese stock index futures market was undertaken by Watanabe (2001). This article examined the relation between price volatility, trading volume and open interest for the Nikkei 225 stock index futures traded on the Osaka Securities Exchange (OSE) using the method developed by Bessembinder and Seguin (1993). The results for the period beginning 58

16 14 February 1994 confirmed the findings by Bessembinder and Seguin (1993) of a significant positive relation between volatility and unexpected volume and a significant negative relation between volatility and expected open interest. However, no relation between price volatility, volume and open interest was found for the period prior to 14 February 1994, when the regulation increased gradually. The result provided evidence that the relation between price volatility, volume and open interest may vary with the regulation. Pindyck (2001) examined the role of volatility in short-run commodity market dynamics, as well as the determinants of volatility itself. This paper provided preliminary evidence regarding the role of volatility as a determinant of commodity market dynamics. Specifically, the author developed a model describing the joint dynamics of inventories, spot and futures prices, and volatility, and estimated using daily and weekly data for the petroleum complex: crude oil, heating oil, and gasoline. The estimation results presented gave limited support to the theory of commodity price dynamics. For heating oil, the results fit the theory well all estimated coefficients have the predicted signs and were significant. For crude oil, the opportunity cost variable has the wrong sign, and for gasoline, both volatility and the opportunity cost variable were either insignificant or have the wrong sign. Ciner (2001) examined the long run trend between the prices of gold and silver futures contracts traded on the Tokyo Commodity Exchange. The data cover the period from the first trading day in 1992 until the last trading day in 1998, for a total of 1720 observations. The prices were collected from the most deferred (farthest) contracts since typically; they were the most active contracts. Johansen cointegration analysis and 59

17 Augmented Dickey Fuller tests were used to test for long-run interdependence. The Augmented Dickey Fuller tests indicated the presence of unit roots in the prices of gold and silver futures contracts, which were consistent with the notion that prices determined in efficient speculative markets contain unit roots. Statistical findings indicate that the frequently cited long-term stable relationship between the prices of gold and silver has disappeared. The study concluded that the stable relationship between gold and silver prices has disappeared in the 1990s. Lucey and Tully (2006) re-examines the results of Ciner (2001), who claims that the historically stable relationship between gold and silver has broken down in the 1990s. It was shown, using a longer run of data, for both cash and futures, that the finding may be unwarranted. In particular a recursive cointegration model was used to extract the evolution of the relationship over a 25 year period. The findings were that while there were periods when the relationship was weak, overall a stable relationship prevails. Kellard et al., (1999) tested unbiasedness and efficiency across a range of commodity and financial futures markets, using a cointegration methodology, and developed a measure of relative efficiency. In general, the findings suggested that spot and futures prices were cointegrated with a slope coefficient that was close to unity, so that the postulated long-run relationship was accepted. However, there was evidence that the long-run relationship does not hold in the short run; specifically, changes in the spot price were explained by lagged differences in spot and futures prices as well as by the basis. It was suggested that market inefficiencies exist in the sense that past information can be used by agents to predict spot price movements. 60

18 Booth et al., (1998) studied the relationship between US and Canadian wheat futures prices in order to analyze the degree of information spillover between the futures exchanges of both countries. Little work has been conducted on their respective future markets, though substantial research has focused on the relationship between US and Canadian equity markets. The increase in market-oriented trade agreements and the decrease of governmental presence in the agricultural sector added to the importance and timeliness of such a study. The results showed that both the US and Canadian wheat futures prices were an integrated series of order one, and that the two series were cointegrated. The evidence showed an equilibrium relationship in the long run, but shortrun dynamics exhibited no such dependencies. These results were relevant for various market participants, including farmers, grain merchants, speculators, exchanges and regulatory agencies. Huang, Masulis and Stoll (1996) viewed oil as featuring a significant effect on the U.S. economy. The returns in oil futures should concern aggregate stock returns if such an effect is present. This paper analyzed the contemporaneous and lead-lag correlations between daily returns of oil futures contracts and stock returns. In the period of the 1980s, astoundingly, there was almost no correlation between oil futures returns and the returns of respective stock indexes. There was contemporaneous correlation and a statistically substantial one day lead of oil futures returns in the case of specific oil stocks. An investigation was also made for the association between oil volatility and stock market volatility. As a more intellectual multivariate vector autoregressive approach similar conclusions were exhibited using a bivariate correlation of raw returns. 61

19 Brunetti and Gilbert (1995) used a complete record of daily price quotations from the London Metal Exchange (LME) to construct a set of monthly volatility measures over the 24-year period for the six LME metals - aluminium, copper, nickel, lead, tin and zinc (data are from for aluminium and nickel). Despite a widely held opinion to the contrary, the volatility has shown no tendency to increase over this period. In particular, except in the case of tin, volatility levels were beneath their historic average levels over , a period of increased speculative interest in the metals markets. Nevertheless, volatility is itself very volatile. A model which relates metals volatility to the metals balance, as manifest in the stock-consumption ratio was developed. This model appeared to account for much of the medium-term movement in volatility, in particular in the aluminium, nickel and zinc markets. The model attributed the modest rise in volatilities over relative to to tighter metals balances. 3.2 STUDIES IN THE INDIAN COMMODITY MARKETS The commodity futures market efficiency in India and inflationary impacts were analyzed by Gupta and Ravi (2013). The authors explored whether the efficiency exists between commodity futures and spot markets using the data derives for three commodity exchanges MCX, NMCE and NCDEX. The study further examined the association between the spot price of commodities and WPI. The evidences of efficiency in most of the sample commodities, though it may depart in some time periods and establish that the huge volatility of spot prices and other market imperfections and irregularities were responsible for lifting WPI was found. 62

20 The relationship between price and open Interest in Indian futures market was studied empirically by Gulati (2012). This paper examined the relationship between the closing price and open interest in Indian stock index futures market. The study investigated the relationship between futures closing price and open interest for the indices BANKNIFTY, MINIFTY, CNXIT, NIFTY and NIFTYMIDCAP50. The evidence of Granger Causality showed that the information of open interest can be used to predict future prices in the long run. Moreover, the long-run information role of open interest is a good indicator of the usefulness of a technical analysis in future markets. The study provided the financial managers in the Indian futures market some very useful input. Bohl and Stephen (2012) stayed propelled by repeated price spikes and crashes over the recent past years. The authors investigated the destabilization of commodity spot prices by the growing market shares of futures speculators. The study approximates conditional volatility and analyze the extent it is altered by expected and unexpected speculative open interest. Two equally long sub-periods were divided and made as the sample for the study, and the speculative impact on conditional volatility increases was documented. With the aspect of six intemperately traded agricultural and energy commodities, robust evidence was not found in this case. Hence the study concludes that the increasing financialization of the raw material markets does not make them more volatile. Byrne et al., (2012) contributed to the empirical evidence on the co-movement and determinants of commodity prices. Using non stationary panel methods, the study documented a statistically significant degree of co-movement due to a common factor. Within a Factor Augmented VAR approach, real interest rate and uncertainty, as postulated by a simple asset pricing model, were both found to be negatively related to 63

21 the common factor. Evidence was robust to the inclusion of demand and supply shocks, which both positively impact on the co-movement of commodity prices. Chaarlas et al., (2012) documented commodity derivative market as one of the important and flourishing markets in India which supports the agricultural sector. The commodity derivative market has shown to be the most desirable investment option for both the small and large investors. Investments were considered to be safe and well when they were presumed to give maximum return with minimum risk. In this study, the volatility of prices of Six Base Metals during the study period from June 01, 2010 to May 31, 2011 was examined. It was found that the commodity: Aluminium, had suffered less volatility when compared to other base metals. From the analysis of the volatility of the prices of Base metals, Aluminium has been considered to be the safest metal to invest in. Therefore, it was suggested that the investors who trade in Multi Commodity Exchange were advised to invest in this commodity. Srinivasan (2012) tested the price discovery process and volatility spillovers in Indian spot-futures commodity markets through the Johansen cointegration test, Vector Error Correction Model and the bivariate EGARCH model. The study used four futures and spot indices of the Multi Commodity Exchange of India, representing relevant sectors like agriculture, energy, metal, and the composite index of metals, energy and agro-commodities. The presence of long-term equilibrium relationships between the futures price and its underlying spot price of the commodity markets was confirmed from the Johansen cointegration test. Commodity spot markets of agriculture, energy, metal, and the composite index of metals, energy and agro-commodities played a dominant role and serve as effective price discovery vehicle, implying that there was a flow of 64

22 information from spot to futures commodity markets was found using Vector Error Correction Model. The bivariate EGARCH model indicated that although bidirectional volatility spillover persists, the volatility spillovers from spot to the futures market were dominant in the case of all MCX commodity markets. The impact of derivative trading on spot market volatility: evidence from the Indian derivative market was investigated by Ray and Panda (2011). The study analyzed the effect of the introduction of derivatives on the volatility of the Indian stock exchange. This study also addressed the stock market volatility in the pre and post derivative period and the derivative effect. The results showed that some of the stocks experienced changes in the structure, volatility after implementation of derivatives and experiencing a stronger persistence of volatility in comparison to the pre derivative period. Most of the stocks became disintegrated with market benchmark index after introduction of derivatives. Natanelov et al., (2011) argued that significant attempts have appeared in literature, the current perception of co-movement of commodity prices appeared inadequate and static. In particular, the study focused on price movements between crude oil futures and a series of agricultural commodities and gold futures. A comparative framework was applied to identify changes in relationships through time. Johansen cointegration test, causality from Vector Error Correction Mode, and Threshold cointegration were employed. The results indicated that co-movement was a dynamic concept and that some economic and policy development may change the relationship between commodities. Furthermore, biofuel policy buffers the co-movement of crude oil and corn futures until the crude oil prices surpass a certain threshold were discussed. 65

23 Mukherjee (2011) noted besides the well-established fact towards the requirement of market based instrument, there has always been a doubt, as expressed by different bodies, on the usefulness and suitability of futures contracts in developing the underlying agricultural commodity market, especially in the agricultural based economy like India. In this study, an attempt has been made to re-validate the impact of futures trading on agricultural commodity market in India. The daily price information in spot and futures markets, for a period of 7 years ( ), for 9 major agricultural commodities, taken from different categories of Agri-products, were incorporated into various econometric models, like: Multiple Regression, Vector Auto Regression, Granger Causality Test, and GARCH model to test the concerned objective. The study has exhibited that even though the inflationary pressure on commodity, especially agricultural commodity, prices have gone up sharply after the introduction of commodity futures contracts, the destabilizing effect of the futures contract is casual in nature and tends to vary over a long period of time. The empirical findings significantly showed that the comparative advantage of futures market in disseminating information, leading to a significant price discovery and risk management that can again help to successfully develop the underlying commodity market in India. Therefore, it can always be suggested to strengthen the market structure to achieve the broader target instead of curbing the commodity futures market. Basu and Miffre (2011) constructed factor-mimicking portfolios that capture the hedging-pressure-based risk premium of commodity futures considering single sorts based on the open interests of hedgers or speculators, as well as double sorts based on both positions. Strong evidence was found for a risk premium arising from the combination of the two positions. Further tests demonstrated that the hedging pressure- 66

24 based risk premium rises with the lagged volatility of commodity markets and that the crosssectional price of commodity risk was positive. Risk premium was also found to explain part of the performance of active portfolios based on momentum and term structure. Kumar and Pandey (2011) investigated the cross market linkages of Indian commodity futures for nine commodities with futures markets outside India. These commodities range from highly tradable commodities to less tradable agricultural commodities. The cross market linkages in terms of return and volatility spillovers were analyzed. The nine commodities consisted 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. Using the Johansen s cointegration test, error correction model, Granger causality test and variance decomposition techniques return spillover were investigated. The bivariate GARCH model was applied to investigate volatility spillover between India and other World markets. It was found that futures prices of agricultural commodities traded on the National Commodity Derivatives Exchange, India and Chicago Board of Trade, prices of precious metals traded on the Multi Commodity Exchange, India and NYMEX, prices of industrial metals traded on the Multi Commodity Exchange and the London Metal Exchange and prices of energy commodities traded at MCX and NYMEX were cointegrated. For commodities, the world markets showed to have bigger (unidirectional) impact on Indian markets. Bi-directional return spillover between Multi Commodity Exchange and London Metal Exchange markets was found using a bivariate model. However, effect of London Metal Exchange on Multi Commodity Exchange was stronger than the effect of Multi Commodity Exchange on London Metal Exchange. Results of 67

25 return and volatility spillovers indicated that the Indian commodity futures markets function as a satellite market and assimilate information from the world market. Caporale et al., (2010) investigated the role of crude oil spot and futures prices in the process of price discovery by using a cost-of-carry model, unit root test, and Vector Error Correction Model with an endogenous convenience yield and daily data over the period from January 1990 to December The study provided evidence that futures markets play a more important role than spot markets in the case of contracts with shorter maturities, but the relative contribution of the two types of market turns out to be highly unstable, especially for the most deferred contracts. The implications of these results for hedging and forecasting crude oil spot prices were also discussed. The price volatility, trading volume and open interest: evidence from Indian commodity futures markets were investigated by Kumar and Pandey (2010). The study empirically investigated the relationship between volatility and trading activity, including trading volume and open interest, for agricultural, metals, precious metals and energy commodities in the Indian commodity derivatives market. Trading volume and open interest were included in the paper to distinguish between speculators/day traders and hedgers. The relationship between volatility and trading activity was more important in an emerging market context where derivatives markets are generally criticized for speculative activity and destabilizing effect on the spot market. This study used three different measures of volatility: (1) daily volatility measured by close-to-close returns, (2) non-trading volatility measured by close-to-open returns and (3) trading volatility measured by open-to-close returns. The contemporaneous as well as a dynamic relationship between volatility and trading activity were investigated. The positive and 68

26 significant correlation between volatility and trading volume of all commodities under consideration was found. Although volume parameters were significant, the study found volatility to be mainly explained through its own lagged values. For most of the commodities were found have insignificant relationship between volatility and open interest. The results of the dynamic relationship between volatility and trading activity showed that only overnight volatility drives the trading volume but not open interest Kamaiah and Sakthivel (2009) attempted to investigate the relationship between stock market volatility and trading activity in Nifty futures market using GARCH framework. The results of the study showed that the spot market volatility was found to have a positive relationship with unexpected trading volume and open interest in Nifty futures market. Kumar (2009) studied the effect of futures trading on spot market volatility in the Indian commodity derivatives market. The study adopted Bessembinder and Senguin (1992) methodology. The study found no effect of spot volatility on futures trading activity for most of the commodities. Kumar (2009) studied the relationship between futures trading activity and spot market volatility for agricultural, metal, precious metals and energy commodities in the Indian commodity derivatives market. Whether the futures trading in the Indian commodity futures market stabilizes or destabilizes the spot market was debated in this paper. By modeling contemporaneous as well as the dynamic relationship between spot volatility and futures trading activity, including trading volume (speculative/day trading) and open interest (hedging) the mentioned issue was explored. The contemporaneous 69

27 relationship was examined through the augmented GARCH model in which spot volatility was modeled as GARCH (1,1) process and trading activity were used as explanatory variables adopting Bessembinder and Senguin (1992). Through the VAR model, the lead-lag relationship between spot price volatility and futures trading volume and open interest was tested. To empathize the dynamic relationship between these variables Granger causality tests, forecast error variance decompositions and impulse response function were employed. Both expected and unexpected futures trading volume affects contemporaneous spot volatility positives were found. The Granger causality tests, forecast error variance decompositions and impulse response function affirmed for all commodities that the lagged unexpected volatility causes spot price volatility. Hedging activity, however assessed by open interest does not show a substantial effect on spot market volatility..power and Vedenov (2009) analysed the problem of multi-commodity hedging from the downside risk perspective. The lower partial moments (LPM2)-minimizing hedge ratios for the stylized hedging problem of a typical Texas panhandle feedlot operator were calculated and compared with hedge ratios implied by the conventional minimum variance (MV) criterion. A kernel copula was used to model the joint distributions of cash and futures prices for commodities included in the model. The results were consistent with the findings in the single-commodity case in that the MV approach leads to over-hedging relative to the LPM2-based hedge. The unexpected result was that minimization of a downside risk criterion in a multi-commodity setting may lead to a Texas hedge (i.e. Speculation) being an optimal strategy for at least one commodity. 70

28 Srinivasan and Deo (2009) examined the temporal lead lag and causality between Mini gold spot and futures market by taking daily closing values for both the indices from the sample period January 01, 2005 to December 31, 2008 for the Multi Commodity Exchange of India. Stationarity of the spot and futures market variables were determined using Augmented Dickey Fuller and Phillip Perron tests which indicated that the two series were integrated at I. Johansen s Cointegration test and Vector Error Correction Model were employed to analyse the long run and speed of equilibrium between the bivariate variables. The findings revealed that both the markets are cointegrated and there exists a causal relationship between these two markets in the long run. Unidirectional causality was running from spot to futures market in long-run dynamics and spot market served as a primary market for price discovery. Kumar and Singh (2008) empirically studied the volatility, risk premium and seasonality in risk-return relation of the Indian stock and commodity markets. This investigation was conducted by means of the General Autoregressive Conditional Heteroscedasticity in the mean model (GARCH-in-Mean). A systematic approach to model volatility in returns was presented. Volatility clustering and asymmetric nature were examined for Indian stock and commodity markets. The risk-return relationship and seasonality in risk-return were also investigated through GARCH-in-Mean modelling in which seasonal dummies are used to return as well as volatility equation. An empirical work has been carried out on market index S&P CNX Nifty for a period of 18 years from January 1990 to December Gold prices from 22nd July 2005 to 20th February 2008 and Soybean from October 2004 December 2007 was also considered. The stock and commodity market returns showed persistence as well as clustering and asymmetric 71