Meta-Analysis of Efficiency of Indian Spot and Commodity Futures Markets

Meta-Analysis of Efficiency of Indian Spot and Commodity Futures Markets Pankaj Kumar Gupta Jamia Millia Islamia University, India Abstract Examination of market efficiency has conventionally being an area of interest to the market participants, analysts, investors and regulators We find a large number of researches in commodity markets on a global basis covering various aspects of efficiency like macro-economic impact of trading of spot and futures, discovery mechanism of prices, volatility spillover effects In many developing countries including India, the issue of market efficiency has been mind boggling for researchers since research outcomes are confusing making it extremely difficult to derive reliable implications for the market participants and regulators The researchers have used a variety of techniques in statistics and econometrics These include e.g descriptives, F-ratios and various parametric and non-parametric tests The econometric estimations include examination of Casualty, Error correction, Integration, Auto regressions, GARCH models etc It is seen that generalization of results is a complex and difficult task since the results derived show varying behavior and complexity In Indian markets, our examination of researches reveals that inferences derived therefrom on the issue of efficiency and interrelationships connote a contrasting view It seems difficult to decide whether the operation of commodity derivatives affects the volatility and efficiency of spot markets and also the trading response In addition, the macro-impact of commodity derivative trading is an unresolved issue We analyze the methodologies used in these researches in Indian and other countries to find out the major causes of contrast in the inferences on selected parameters like selection of exchange or asset (commodity), time frames, statistical or econometric technique used We also analyze the policy responses of the regulators and attempt to suggest a workable solution to solve these problems We derive that Indian commodity markets are inefficient for majority of commodity sets and the policy response is weak, sub-optimal and haphazard Keywords: Market Efficiency, Price Discovery, Volatility Spillover, Error Correction, Integration JEL Classification: Q02, G13, E31, G12 Introduction Forecast of Futures prices of traded assets essentially involves the notion of market efficiency In commodity markets, using the time series data, many researchers have examined varying perspectives of market efficiency Fama (1970) has established that growth of commodity futures market is dependent upon the level of market efficiency Efficiency examination essentially involves the analysis of the co-integrating relationships, causality effects and ability of the markets to conform to the forecasts of the selected models Three principle forms of market efficiency are classified by Fama (1991) - weak, semi strong form and strong form The question of efficiency is still unsolved in the large number of studies throughout the world For 237 example Gupta and Mayer (1981) tested semi-strong form by comparing futures market and ARIMA models forecasts and found them to be outperforming Similar result has been obtained by Rausser and Carter (1983) with a caution that “unless the forecast information from the models is sufficient to provide profitable trades, their superior forecasting performance in a statistical sense has no economic significance” Elam and Dixon (1988) reject the validity of results using conventional F tests in a non-stationary price series Maberly (1985) reveal that “the inference that the market is inefficient for the more distant futures contracts is a direct result of the bias inherent in using OLS to estimate the parameters in models with censored data” Engle and Granger (1987) provide a co-integration based method for efficiency test give the non-stationery nature of time series Allen and Som (1987) research on London rubber market support the hypothesis of weak form efficiency Various researches have been carried out conducted globally to examine commodity futures market efficiency of particularly futures like Canarella and Pollard (1987), Baura (1987), Dickinson and Muragu (1994), Chan et al (1997), Min et al (1999) for Korea and Taiwan market which establish the weak form of efficiency Similarly, Groenewold and Kang (1993) establish that Australian market is efficient in semi strong form Yalawar (1988) have used correlation and run test to test the stock market efficiency and found to be efficient Brorsen, Oellermann and Farris (1989) argue that futures trading introduction improves the efficiency of spot market efficiency though increasing the price risk Kaminsky and Kumar (1990) find excess return to be positive for some commodities time horizon being greater than three months Chowdhury (1991) suggest the use of co-integration over conventional methods of testing efficiency of futures markets Studies conducted by Aulton, Ennew and Rayner (1997) and Kellard et al (1999) respectively show the partial integration and shortrun efficiency of markets respectively But, Andrew and Mathew (2002) using GQARCH-in-mean processes find that markets are unbiased in long run Phukubje and Moholwa (2010) on South African Futures commodity markets and Paschali (2007) on Bulgarian agricultural Commodity Markets during transition show that weak policy interventions results in poor integration between local and international markets with a functional lacuna Researches on Indian commodity futures markets show contrasting results Thiripalraju et al (1997) shows that commodity markets in India are efficient on various parameters like price discovery and inter market feedback Singh (2005) argue that opinions on market efficiency are mixed and vary across commodities Sahi and Raizada (2006) conclude poor price discovery and volume trading impact of futures fueling inflation (in spot market) Bose (2008) find two way price discovery function of spot and futures commodity market, reducing the volatility thus helping the hedging function Contrary results have been indicated by Eswarana and Ramasundaram (2008) Singh, (2009) have realized the “genuine shortcomings accruing due to lack of detailed investigation of seasonality, overlapping data and unspaced observations in examining the efficiency and spill overs” Goyari and Jena (2010) find the Indian commodity markets to be inefficient in weak form and sufficient opportunities exits to make profits through a structured trade Singh (2010) have shown the persistence of a long-run equilibrium relationship between futures and spot price series of selected commodities Kaur and Rao (2010), Chakrabarty and Sarkar (2010) find support for market integration that is suitable for hedging However, Dinkar and Nagpal (2011), Ali and Gupta (2011) find the market to be partially efficient Agnihotri and Sharma (2012) argue that quality of market need not to be compromised with separation of contracts It is, therefore, inferred that the issue of co-integration and market efficiency is still unresolved We therefore find interesting to explore the reasons for these varying results of these researches and possible explanation of the methodological implications For this purpose, we split the issue of market efficiency into three components – (a) Price discovery and Volatility Spill over, (b) Interrelationship between futures and spot markets and (c) Macro economic impact of futures commodity markets We use the published researches on commodity markets with a focus on Indian markets 1 Research Papers/Articles in various table indicated in bold represent researches on Indian commodity markets 238 Price Discovery and Volatility Spillover An important function of commodity futures market is discovery of price that aids the producers to plan their various operational activities starting from production to distribution of commodities Analysis of volatility spillover coupled with price discovery function of market continuously attracts the interest of analysts and investors In spite of similar availability of information to both spot and futures markets the reactions are not identical causing a spillover effect Various researches in India and abroad have been conducted on the examining the issues of price discovery and volatility spillovers Table shows a gist of these researches focusing the research technique used, issues examined and the implications Table – Researches on Price discovery and Volatility spillover Author Technique Issue Examined Implications Leuthold (1974) Co-integration Kamara (1982) Co-integration Garbade and Silber (1983) Granger Casualty, Co-integration Impact of Spot market on futures market Impact of Futures trading on spot market before and after its introduction Impact of Futures market on spot market Oellermann et al (1985) Granger causality Information Efficiency reduces spot price volatility Futures trading introduction either reduced or did not increase volatility of spot prices Flow of information from spot to futures market is reverse and important role of liquidity and market size in price discovery Futures market discover price Stein (1987) Conditional Model Oellermann and Farris (1989) Bessembinder and Seguin (1993) Garbade Silber framework Co-integration Yang and Leatham (1999) Singh (2000) Kanas (2000) Yang et al (2001) Thomas and Karande (2001) Lead lag relationship between spot and futures prices Formulation of a pricing model based on prices interactions of informed speculators and hedgers Impact of Futures trading on spot markets Open interest and spot market volatility Opening Futures trading improves risk sharing and reduces price volatility Futures market discovers price Volatility of spot prices has positive relation with unexpected volume and negative relation with the expected open interest Co-integration Price discovery function for In a search for equilibrium price, futures markets futures market implicitly gather more information than available in the cash markets alone Multiplicative dummy Impact of futures trading Futures trading reduced price variable model on seasonal price volatility fluctuations Co-integration Spillover effects across Strength of the volatility countries spillovers increased after major events Co-integration Effectiveness of Price Cash and Futures market codiscovery function of integration not affected by storage futures markets of traded assets, yet creating a bias in price estimates Price dynamics model Effectiveness of Price Reaction of markets to suggested by Garbade discovery function of information altogether varies in and Silber (1983) futures markets price discovery process 239 Assoe (2001) Co-integration Moosa (2002) Garbade and Silber (1983) model Naik and Jain (2002) Co-integration Linkage between various markets Effectiveness of Price discovery function of futures markets Price discovery and risk management Small and negative mean spillover effects Futures role in Price Discovery is not substantial Commodity futures role in stabilization of spot prices, inventory and production decisions Information efficiency and Price discovery function of futures markets in India Dynamics of the relationship between volatility and trading volume Price discovery function in cash markets “Unnecessary hoarding increase the carrying cost leading to lower responsiveness of inventory to future prices” Futures market unable to fully incorporate information Buguk et al (2003) Significant Co-integration relationship (a strong long run relation Granger causality, Co- Volatility spill over and Futures markets impact cash integration, GARCH Price discovery function of market and brings volatility futures markets EGARCH Volatility spillovers Strong price volatility spillovers Dasgupta (2004) Co-integration Kumar (2004) Johansen Cointegration Bhar and Hamori (2005) GARCH model Chopra and Blesser (2005) Co-integration, error correction models Ranjan (2005) Co-integration Trading impact of futures markets Yang et al (2005) Generalized forecast error variance decomposition Granger Causality Descriptive measures Lead-lag relationships between futures trading and spot prices Sahi (2006) Granger Causality Qing-fu and Jinqing(2006) Johansen Cointegration, VECM, Bivariate EGARCH Granger Causality Trading impact of futures markets Price discovery function of Significant bi-directional futures information flows Raju and Karande (2003) Slade and Thille (2006) Praveen and Sudhakar (2006) Bryant et al (2006) Kiran and Mukhopadhyay (2007) Piyamas and Pavabutr (2007) Nath and Lingareddy (2008) Levels and volatilities of the spot prices Higher order lagged returns affect trading volume Cash contracts and first distant futures contracts behavior shows inefficiency of market Futures trading ineffective in reducing seasonal price volatilities Unidirectional impact on spot prices, “weak causal feedback between open interest and cash price volatility” Relationship between trading volume and price instability is positive No change in volatility Price discovery between Unidirectional impact Commodity futures market and stock market and Co-integration Generalized theory of Rejected the hypothesis framework normal backwardation GARCH on intra-day Volatility spillover from the Established, Basic ARMAUS to India GARCH specification outperforms MGARCH VECM Price discovery function of Partial performance futures contracts Hodrick-Prescott filter Trading impact of futures Futures not increase cash price (1997) markets (India) volatility 240 Biswas (2009) Kumar (2009) Srinivasan (2009) Cointegration and Error correction dynamics Granger Causality, Forecast error variance decompositions, Impulse response VECM Maitra and Narayanan (2010) Dey (2010) Linear regression forecasting Causality, Impulse response Kumar (2011b) DVECH-GARCH, BEKK-GARCH and CCC-GARCH Mean, Deviations, Correlations Granger Causality Price discovery function of Co-integrated and long-run futures contracts relationship established Linkages between futures trading and volatility of spot market Lagged unexpected volatility causes spot price volatility for all commodities Price discovery function of futures contracts Forecast performance of volatility Futures and Spot price convergence Efficient in price discovery Found some evidence Causality is bi-directional and spot market for selected commodity flexible Models effective in spot price discovery Volatility and hedging behavior of selected commodity futures indices Joseph and Sajan Futures trading Markets offers profitable (2010) effectiveness investment opportunities Dash and Andrews Causality between spot and Price discovery mechanism is (2010) futures prices quite effective in general Chakravarty and Bivariate EGARCH, Volatility using intra day Evidence of intra day periodicity, Ghosh (2010) Parkinson(1980) range interval data seasonality, and the net inverse based estimator relation between time to maturity and realized volatility Debasish and GARCH, EGARCH Volatility and its spill over Bi-directional spill over captured Kushankur (2011) CGARCH, MGARCH, effects under GARCH and unidirectional Diagonal VECH, BEKK spill over found under EGARCH Hussain (2011) Error Correction, Co- Price discovery function Futures market dominates price integration discovery Bhatt (2012) Lagged response Price discovery mechanism Model robust to capture trends model in commodities and assess the long-term trends in their prices Chauhan, Singh and Co-integration Spill over and Price Information flow from futures Arora (2013) discovery market to spot market Masood and Variance Ratios, Non- Growth of Commodity Volume and Value relationship is Chary(2016) parametric tests Futures Market linear We derive from the literature is that futures and spot markets absorb the same set of information in an identical period leading to discovery of price and spillover effects However, the conflict is the point of reaction i.e flowing from spot to futures or futures to spot We have further investigated these researches, particularity on Indian commodity markets Our examination indicates that the research inferences differ mainly because of the data used for analysis The data available on commodity exchanges – MCX, NCDEX, MCX, ICX differ significantly with respect to the time frequency, price-volume, and high low statistics On some exchanges the data is not continuous which raises questions on the applicability of an appropriate times series method We also raise questions on use of econometric techniques A vast number of studies have used the GARCH model during the periods when the market conditions were not relatively stable, even though the model captures time varying variances We find instances of fat tails in the price distribution of some commodities that limit the application of GARCH Use of Johansen Co-integration test is appropriate compared to Engle-Granger (EG) 241 method since EG captures only a single set of co-integrating relations However, Johansen Co-integration test requires a stationery filter, which many are violated for a significant number of commodities in India There is an ongoing debate on the use of VAR vs VECM VECM is able to explain the price discovery built in shortrun dynamics framework The inherent requirements of a particular model when applied to a given price series of commodity in different time periods may produce differing results Therefore, it becomes difficult to generalize the results of researches We also find that the commodities as per their nature be classified as – agro type (immediate consumption/intermediate production), asset type (gold/silver), industrial type (metals) However, there exists a relationship for the same commodity classified in two sets For example, silver is for industrial as well as household consumption Business cycles, festivals and hedging needs of economic participants would altogether affect the price series Consequentially, the results of price discovery and spillovers would be different in different circumstances These may possible explain the confusion between the researchers Spot and Future Markets Integration We further explore the relationship between futures and spot markets used to test the notion of efficiency under a distinct framework The selected list of researches on these aspects is given in Table Table – Researches on Spot and Future Markets Integration Author Ehrich (1969) Technique Co-integration Issue Examined Cash-futures price relationships Dusak (1973) Multivariate VECM, GARCH Co-integration Risk based returns Roll (1984) Bessler and Covey (1991) Chan (1992) Fortenbery and Zapata (1993) Co-integration Co-integration, lead lag regression Co-integration Baharumshah and Habibullah (1994) Co-integration Karbuz and Jumah (1995) Herrero and John (1997) Co-integration Fortenberry and Zapata (1997) Co-integration Co-integration, Granger Causality Fung and Patterson VAR (1998) Efficiency of Futures market Futures and cash market relationships for slaughter Cash-futures price relationships Interest rate in describing price discovery Implications Long-run relationships found, cash markets lead futures markets CAPM confirmed Futures market found to be efficient Found evidence Trading intensity affected lead lag relation-no convincing evidence Interest rate as an explanatory variable useful in explaining relationship Markets in Malaysia were highly integrated Long-run relationship among prices in six Malaysian markets Long-term relationship Prices of commodities tend to between spot and futures move together in the long run Stability of long-run coNo casualty found movement between commodity prices of world and UK retail Lead-lag relationship Partial evidence of integration between spot and futures in US Dynamic relationship Open interest and volume are not among trading volume, endogenously determined volatility and open interest 242 Silvapulle and Non-linear causality Moosa (2000) test Bivariate VAR Maynard et al (2001) Short-run analysis based on Two-sided distributed lag model McMillan and Speight (2002) Asche and Guttormsen (2002) Sahadevan (2002) Thenmozhi 2002) Ciner (2002) Chen and Firth (2005) Chan et al (2004) Yang et al (2005) Zapata et al (2005) Singh (2005) Mattos and Garcia (2004) Ahuja (2006) Reddy (2006) Price volume relationship Linear causality from volume to in futures market price Performance of Co-integration found in only one Minneapolis Grain variety(commodity class) Exchange futures contracts with thin volumes Sequential Information Relationship between From volumes to absolute returns, arrival Hypothesis and volume and absolute return no apparent causality, but some Distribution cases of causality flowing from Hypothesis , VAR return to volume seen Co-integration Lead lag relationships Futures lead cash spot prices and between spot and futures distant futures lead near futures Co-integration Relationship volatility, Lack of efficient and modern market depth, trading infrastructure facilities, existence volumes and absolute of the gray market and lack of returns participation in the futures markets Simultaneous equation Lead-lag relationship Futures react faster than the spot modeling, OLS, two between futures and spot market in disseminating stage least squares prices of stock indices information regression Linear Granger Relationship between Volume contains no information causality, VAR Tokyo commodity futures to forecast return price changes and volumes Co-integration Relationship between Existence of contemporaneous Chinese commodity futures correlation between trading and trading volume and returns volumes and absolute returns ruled out Co-integration Relationship between Relationship of volatility with Chinese commodity futures volume is positive and with open volume, daily volatility and interest is negative returns Generalized forecast Relationship between Weak causal feedback between error variance commodity futures volume volatility of cash prices and open decompositions and open interest interest Granger causality Co-integration Relationship between Causality in one direction from world cash prices for future price to cash but not vice exported sugar and New versa York sugar futures Mechanism of movement Long-run equilibrium Co-integration of spot and future prices mechanism exists Co-integration Relationship between Long-term equilibrium Brazilian agricultural relationships between spot and market future and spot future prices exists due to higher prices volume of trading Co-integration, Lead lag relationship Imperfections in Indian Casualty between spot and futures commodity markets trading Co-integration, Causality and speed of No single country is completely Granger causality, adjustment to deviations exogenous and many countries VECM in long run equilibrium GSP prices are interlinked to some extent 243 Hadsell (2006) Threshold ARCH Relationship between trading volume and price volatility Babula et al (2006) Co-integrated VAR Relationship between price based Johansen and volatility and trading Juselius methodology volume and Azizan, Ahmad and Bivariate ARMAFutures-cash market Shannon (2007) EGARCH model relationship (Malaysia) Traders react asymmetrically to new information Policy relevant elasticities important in US markets “Information transmission process at mean and volatility level between cash and futures” Lokare (2007) Co-integration, Cash-futures price Evidence of co-integration and Casualty relationships operational efficiency, though at a slower rate Iyer and Mehta Co-integration Integration between price Found evidence (2007) series Roy and Kumar Johansen CoLead-lag relationship Partial evidence (2007) integration, Garbade- between futures and spot Silber (1983) model prices and hedging effectiveness Kumar, Singh and Co-integration, Volatility, Risk premium Risk return relationship, Pandey (2008) Casualty and seasonality Seasonality in risk and return established Nath and Granger causality, Futures-Spot Integration Co-integration observed between Lingareddy (2008) Correlations, Basic China and U.S.A cotton futures regression with dummies, GARCH Roy (2008) Co-integration, Market integration with Spot markets in India are coCasualty global integrated with global Long and Lei (2008) Co-integration, Commodity prices Co-integrated Granger causality, relationships at exchanges Residue analysis, IRF, VECM Bekiros and Diks GARCH BEKK model Causal linkages between Evidence found (2008) spot and futures of Linear and nonlinear type Sandhya (2008) Co-integration Impact of futures trading Partial evidence on market integration Raveendran et al VECM Transmission of future Unidirectional flow from futures price to spot price to spot (2010) Bhardwaj and Co-integration, Integration of futures and Price discovery in each contract Vasisht (2009) Granger Causality the spot market is unidirectional Elumalai et al (2009) Johansen Cointegration Futures and spot price Evidence found analysis and VECM Ali (2009) Co-integration, Long-term equilibrium Relationship found Granger causality relationship between spot and futures Singh (2009) Co-integration Price linkages in Partial evidence competing domestic commodity futures markets Ghosh (2010) VAR Granger Role of commodity futures Little evidence that futures price Causality Unrelated with thin trading in prices serves as reference price regression determination in physical market 244 Iyer and Pillai (2010) TVAR Vasisht and Bhardwaj (2010) Ahmad (2010) Batten, Ciner, and Lucey (2010) Johansen Cointegration tests, Granger causality EGARCH GARCH Saravanan and VECM, GARCH Malabika (2010) model Xaviour and Mathew Co-integration (2010) Palanichamy et al Cointegration , Granger Causality (2010) tests Vasisht and Granger causality Bhardwaj (2010) Futures markets role in the Convergence rate of information price discovery process is slow specifically observed in non-expiration weeks Volatility of agricultural Co-integration evidence found prices but unidirectional causality from futures to spot markets Futures and cash market Persistence of Volatility higher relationship post migration marginally in automated trading of Malaysian crude palm system compared to relative to oil futures the open outcry system Monthly price volatilities “Precious metals are too distinct for precious metals to be considered a single asset class, or represented by a single index” Role of futures on Partial evidence underlying spot market Arbitrage opportunity Arbitrage opportunities exist Long-run equilibrium relationships Evidence found Bivariate relationship between spot and future market Futures Pricing Model Convenience yield Evidence found Hernandez and Torero (2010) Linear and nonlinear (nonparametric) causality tests Futures markets generally discover spot prices Jayagurunathan et al (2010) Johansen Cointegration, Vector Error Correction Models Standard GARCH model Banerjee (2010) Chakravarty and Ghosh (2010) Mukerjee (2011) Relationship dynamics between spot and futures prices of agricultural commodities Co-integrations, price discovery process Effect of commodity futures trading on spot prices Multiple regressions, Impact of futures trading VAR, Granger on spot markets Causality, GARCH OLS ,VECM, MPerformance of various GARCH with error hedge ratios correction model Convergence found “Spot markets marginally leads futures and spot prices tend to discover new information more rapidly than future prices” Positive effect of future price movements Significant discovery of price and risk management capabilities Srinivasan (2011) “Dynamic M-GARCH model hedging strategy performs best in reducing the conditional variance of the hedged portfolio” Kumar and Pandey Johansen’s Cross market linkagesWorld markets have a significant (2011a) Cointegration Granger Nine commodities futures unidirectional impact on markets causality Variance in India with those outside in India decomposition India GARCH (BEKK) Kumar and Pandey GARCH, Granger Relationship between Overnight volatility impact the (2011b) Causality, variance trading volume, volatility trading volume, open interest not decomposition, IRF and open interest affected Srinivasan (2012) Johansen Price discovery process Evidence of Flow of information Cointegration, VECM, and volatility spillovers from spot to futures Bivariate EGARCH 245 Haq and Rao(2014) Gupta, Chaudhary and Agarwal(2017) Johansen CoCommodity market integration and VECM efficiency VECM, Hedging Effectiveness V-MGARCH Only Long-run Efficiency observed Distant Futures have better hedge ratio Going by the notion of efficiency, when the phenomenon of arbitrage and information flow distortion occurs, the market is said to be inefficient High volumes guide futures market of the commodities when large players can use muscle power to drive the Indian market by taking simultaneous position in futures and spot markets and playing an arbitrage The flow of information from futures to spot is obvious and is an auto process, yet we find instances of backwardation and contango violated over fairly large time periods We also blame the large Foreign Institutional Investors (FII) acting as fly by night operators both in the commodity and securities markets We generalize by saying that the Indian commodity market is inefficient in semi strong form but sometimes for selected data groups typically show efficiency in short runs Macro-Economic Impacts of Commodity Markets Researchers have extensively used a variety of integration and vector type models to investigate the impact of commodity trading on various macro economic measures particularly inflation In an Indian context, the most common measure of inflation is the change in the whole price index (WPI) Expectation hypothesis explains that prices in futures markets are unbiased expectations of series of future spot prices Therefore, demand-supply effect on prices of commodities in futures markets is obvious The series of futures prices convey trading information to the market players Given the inefficiency of information dissemination, the price arbitrage can have a devastating impact An important concern to the policy makers is the impact of the market behavior on the macro economic variables India, in the last decade has witnessed serious demandsupply gaps as well as price distortions in markets, which have raised important concerns like rising prices Inflation has become more important after the global financial crisis that made the markets more volatile Table shows the summary of the researches mainly carried out on Indian commodity markets Table - Researches on Macro-economic Impacts of Commodity Markets Author Jagannathan (1985) Technique GARCH Becker and Finnerty (2000) GARCH Jensen, Mercer and Johnson (2002) Co-integration Pandey (2005) GARCH Gorton and Horst (2006) Co-integration Issue Examined Results Rate of change of consumption Conditional covariance and the real return to forward consistent with the speculation predictions of the consumption-beta model Commodities can be used as Portfolio comprising of long inflation hedge commodity futures contracts improves risk and return performance when combined with bonds and stocks Benefits of diversification Commodity future derived from inclusion of sustainability enhances commodity futures to a portfolio performance traditional portfolio Price behavior of selected Partial Evidence commodities in Asian neighbors on Indian markets Correlation between traded Commodity futures correlate commodity futures and other positively to inflation and its assets classes changes 246 Aggarwal and Ladda (2007) Co-integration model and OLS model Inter linkages between macroeconomics and agro commodity prices Bhattacharya (2007) Casualty, Decsriptives Price arbitrage and market distortions Kumar, Singh and Pandey (2008) Sen (2008) OLS, VAR and Hedging effectiveness of VECM, MGARCH futures VECM Role of futures trading on inflation Bose (2009) Co-integration Role of US futures trading on inflation in India Sahoo and Kumar (2009) Granger causality Transaction tax effect of commodity trading volumes Sen and Paul (2010) Granger causality Role of futures trading on inflation Nayak (2010) Co-integration Varadi, (2012) Co-integration Agarwal, Jain and Thomas (2014) Co-integration Cause of fluctuations in the prices of agricultural commodities in developing countries and related macroeconomic implications Evidence of speculation in Indian commodity markets Efficiency and Risk management Influences of macro-economic factors is somewhat minimal and bring obvious changes to yield and prices Across markets(mandis), prices vary primarily due to taxation, poor storage facilities and sub-optimal grading systems Futures are effective Inflation increased after trading No conclusive evidence found Relationship between transaction cost and volatility is negative Granger causality establishes for a major price surge of food items flowing from futures to spot From a macroeconomic perspective , the futures price lack stability and efficiency Recent price surge in commodity markets has stipulated the intensity of various factors, which lead to the price volatility High settlement cost, poor warehousing In developing countries like India, commodity markets haven direct implications on the functioning of the economy since a large chunk of the household demand is represented by commodity consumption The idea behind allowing the commodity futures to trade on the changes is to possible reduce the uncertainty of spot prices and an auto mechanism for the government authorities and regulators to fix the minimum support prices to farmers as applicable to many agro commodities The demand for gold in India is enormous resulting in large outflow of foreign exchange ranked next to the crude oil As such, the volatility jumps in pivot commodities is an obvious phenomenon This has attracted the attention of various researches as highlighted in Table We find a large use of the granger casualty and integration models to explain the macroeconomics particularly the inflation measured by Whole Sale Price Index (WPI) Most of the researches indicate that introduction of futures trading has produced inflationary effects We give two arguments to explain this phenomenon India has moved from a controlled economy to a somewhat open system As such, the short run period of few years cannot be said to be sufficient to justify the equilibrium restorations since the learning of market participants is long process Moreover, the commodity prices especially after the global crisis have been subject to high fluctuations globally which may not be captured by some Indian researches Indian markets suffer from a variety of issues as also highlighted by some researches These are restriction of commodities listed for derivative trading, liquidity problems, poor spot market mechanism mainly because 247 of inferior market knowledge, weak logistics infrastructure, tax arbitrages, weak financing, market imperfections like stamp duties, commodity exchanges acting as product bourses (Gupta and Ravi, 2013) etc Therefore, there is no option but to settle the contracts in cash leading to high fluctuation around settlement dates In June 2017, SEBI allowed the institutional investors - the hedge funds that are registered as category III Alternative Investment Funds (AIFs) to trade and invest in commodity derivative markets as “clients” (Singh, 2017) Interestingly, role of the government and regulators is questionable It has been a practice with the governments that whenever the volatility of any commodity increases on the exchange increases beyond a threshold level, the commodity is deleted from the trading list, just to fulfill the political agenda Instead, steps to be taken on market regulation and supply side corrections to address the ill effects Remarks Commodity exchanges in developing countries like India have a crucial role to play in the country’s economic development The importance of futures markets in restoring equilibrium and efficiency is unquestionable as evidenced form the research in various developed countries However, in India we find a host of market distortions that confuse the researchers to generalize their findings We find that controls exercised by regulators and various government agencies are dynamic, creating confusion and uncertainty in the minds of analysts, participants and researchers The market regulation, therefore, seems to be unclear possibly explains the contrasts seen in various research inferences The differing delivery and settlement mechanisms, government polices and lack of infrastructure make market inefficient which cannot solely be determined from the pure quantitative analysis like econometrics We conclude that, from a 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Interrelationship between futures and spot markets and (c) Macro economic impact of futures commodity markets We use the published researches on commodity markets with a focus on Indian markets 1 Research... between spot and futures prices Formulation of a pricing model based on prices interactions of informed speculators and hedgers Impact of Futures trading on spot markets Open interest and spot market... methods of testing efficiency of futures markets Studies conducted by Aulton, Ennew and Rayner (1997) and Kellard et al (1999) respectively show the partial integration and shortrun efficiency of markets