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 cointegration 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 short-run 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 markets1 1Research 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 Information Efficiency reduces Kamara (1982) 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 Garbade and Silber (1983) Granger Casualty, Co-integration Oellermann et al Granger causality (1985) Stein (1987) Conditional Model Oellermann and Garbade Silber Farris (1989) framework Bessembinder and Co-integration Seguin (1993) Yang and Leatham Co-integration (1999) Singh (2000) Multiplicative dummy variable model Kanas (2000) Co-integration Yang et al (2001) Co-integration 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 Lead lag relationship between spot and futures prices Formulation of a pricing Opening Futures trading model based on prices improves risk sharing and interactions of informed reduces price volatility speculators and hedgers Impact of Futures Futures market discovers price trading on spot markets Open interest and spot Volatility of spot prices has market volatility positive relation with unexpected volume and negative relation with the expected open interest Price discovery function In a search for equilibrium for price, futures markets futures market implicitly gather more information than available in the cash markets alone Impact of futures Futures trading reduced price trading on seasonal price volatility fluctuations Spillover effects across Strength of the volatility countries spillovers increased after major events Effectiveness of Price Cash and Futures market co- discovery function of futures markets Thomas and Karande (2001) Price dynamics model suggested by Garbade and Silber (1983) Effectiveness of Price discovery function of futures markets 239 integration not affected by storage of traded assets, yet creating a bias in price estimates Reaction of markets to information altogether varies in price discovery process Assoe (2001) Moosa (2002) Naik and Jain (2002) Co-integration Linkage between various markets Garbade and Silber Effectiveness of Price Small and negative mean (1983) model spillover effects Futures role in Price Discovery is not substantial Co-integration discovery function of futures markets Price discovery and risk Significant Co-integration management relationship (a strong long run relation Futures markets impact cash Raju and Karande Granger causality, Volatility spill over and Co(2003) integration, GARCH Price discovery function market and brings volatility of futures markets Buguk et al EGARCH Volatility spillovers Strong price volatility (2003) spillovers Dasgupta (2004) Co-integration Kumar (2004) Johansen Cointegration Bhar and Hamori (2005) GARCH model 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 “Unnecessary hoarding increase the carrying cost leading to lower responsiveness of inventory to future prices” Futures market unable to fully incorporate information Higher order lagged returns affect trading volume relationship between volatility and trading volume Chopra and Co-integration, error Price discovery function Cash contracts and first Blesser in distant (2005) correction models cash markets futures contracts behavior shows inefficiency of market Ranjan (2005) Co-integration Trading impact of Futures trading ineffective in futures markets reducing seasonal price volatilities Yang et al (2005) Generalized forecast Lead-lag relationships Unidirectional impact on spot error variance between futures trading prices, “weak causal feedback decomposition and spot prices between open interest and cash Granger Causality price volatility” Slade and Thille Descriptive Levels and volatilities of Relationship between trading measures (2006) the spot prices volume and price instability is positive Sahi (2006) Granger Causality Trading impact of No change in volatility futures markets Qing-fu and JinJohansen CoPrice discovery function Significant bi-directional of qing(2006) integration, VECM, futures information flows Bivariate EGARCH Praveen and Granger Causality Price discovery between Unidirectional impact Sudhakar (2006) Commodity futures Bryant et al (2006) Kiran and Mukhopadhyay (2007) Piyamas and Pavabutr (2007) Nath and Co-integration market and stock market and Generalized theory of framework normal backwardation GARCH on intra-day Volatility spillover from the US to India VECM Hodrick-Prescott filter Lingareddy (2008) (1997) Rejected the hypothesis Established, Basic ARMA- GARCH specification outperforms MGARCH Price discovery function Partial performance of futures contracts Trading impact of Futures not increase cash futures price markets (India) volatility 240 Biswas (2009) Cointegration and Kumar (2009) Error correction dynamics Granger Causality, Forecast error variance decompositions, Impulse response Srinivasan (2009) VECM Price discovery function Co-integrated and long-run of futures contracts relationship established Linkages between Lagged unexpected volatility futures trading and volatility of causes spot price volatility for all spot market commodities Price discovery function Efficient in price discovery of futures contracts Maitra and Linear regression Forecast performance of Found some evidence Narayanan (2010) forecasting volatility Dey (2010) Causality, Impulse Futures and Spot price Causality is bi-directional and response convergence spot market for selected commodity flexible Kumar (2011b) DVECH-GARCH, Volatility and hedging Models effective in spot price BEKK-GARCH and behavior of selected discovery CCC-GARCH commodity futures indices Joseph and Sajan Mean, Deviations, Futures trading Markets offers profitable (2010) Correlations effectiveness investment opportunities Dash and Andrews Granger Causality Causality between spot Price discovery mechanism is and (2010) futures prices quite effective in general Chakravarty and Bivariate EGARCH, Volatility using intra day Evidence of intra day periodicity, Ghosh (2010) Parkinson(1980) interval data seasonality, and the net range inverse based estimator relation between time to maturity and realized volatility Debasish and GARCH, EGARCH Volatility and its spill Bi-directional spill over over captured Kushankur (2011) CGARCH, MGARCH, effects under GARCH and unidirectional Diagonal VECH, spill over found under EGARCH BEKK Hussain (2011) Error Correction, Co- Price discovery function Futures market dominates price integration discovery Bhatt (2012) Lagged response Price discovery Model robust to capture trends mechanism model in commodities and assess the long-term trends in their prices Chauhan, Singh Co-integration Spill over and Price Information flow from futures and Arora (2013) discovery market to spot market Masood and Variance Ratios, Growth of Commodity Volume and Value relationship Nonis 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 Iyer and Pillai (2010) Futures markets role in Convergence rate of the information price discovery process is slow specifically observed in non-expiration weeks Vasisht and Johansen Volatility of Co-integration evidence agricultural found Bhardwaj (2010) Cointegration prices but unidirectional tests, causality from Granger causality futures to spot markets Ahmad (2010) EGARCH Futures and cash marketPersistence of Volatility higher relationship post marginally in automated migration trading of Malaysian crude palm system compared to relative to oil futures the open outcry system Batten, Ciner, and GARCH Monthly price volatilities “Precious metals are too distinct Lucey (2010) for precious metals to be considered a single asset class, or represented by a single index” Saravanan and VECM, GARCH Role of futures on Partial evidence Malabika (2010) model underlying spot market Xaviour and Co-integration Arbitrage opportunity Arbitrage opportunities exist Mathew (2010) Palanichamy et Cointegration , Long-run equilibrium Evidence found al (2010) Granger Causality relationships tests Vasisht and Granger causality Bivariate relationshipEvidence found Bhardwaj (2010) between spot and future market Banerjee (2010) Futures Pricing Convenience yield Convergence found Model Hernandez and Torero (2010) TVAR Linear and nonlinear Relationship dynamics Futures markets generally (nonparametric) between spot and discover spot prices futures causality tests prices of agricultural commodities Jayagurunathan Johansen CoCo-integrations, price “Spot markets marginally et leads al (2010) integration, discovery process futures and spot prices Vector tend to Error Correction discover new information more Models rapidly than future prices” Chakravarty and Standard GARCH Effect of commodity Positive effect of future price Ghosh (2010) model futures trading on movements spot prices Mukerjee (2011) Multiple Impact of futures Significant discovery of regressions, trading price VAR, Granger on spot markets and risk management Causality, GARCH capabilities Srinivasan OLS ,VECM, MPerformance of “Dynamic M-GARCH model (2011) various GARCH with error hedge ratios hedging strategy performs best correction model Kumar and Pandey (2011a) Kumar and Pandey (2011b) Srinivasan (2012) in reducing the conditional variance of the hedged portfolio” World markets have a significant unidirectional impact on markets in India Johansen’s Cross market linkagesCointegration Nine commodities Granger futures causality Variance in India with those outside decomposition India GARCH (BEKK) GARCH, Granger Relationship between Overnight volatility impact the Causality, trading volume, trading volume, open variance volatility interest not decomposition, and open interest affected IRF Johansen Price discovery Evidence of Flow of process information Cointegration, and volatility from spot to futures VECM, spillovers Bivariate EGARCH 245 Haq and Rao(2014) Gupta, Chaudhary and Agarwal(2017) Johansen Co- Commodity market Only Long-run Efficiency integration and VECM VECM, efficiency observed Hedging Effectiveness Distant Futures have better hedge ratio V-MGARCH 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 demand-supply 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) Becker and Finnerty Technique GARCH GARCH (2000) Jensen, Mercer and Johnson (2002) Co-integration Pandey (2005) GARCH Issue Examined Rate of change of consumption and the real return to forward speculation Results Conditional covariance consistent with the predictions of the consumption-beta model Commodities can be used asPortfolio 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 Gorton and Horst (2006) Co-integration neighbors on Indian markets Correlation between traded Commodity futures correlate commodity futures and positively to inflation and other its assets classes changes 246 Aggarwal and Ladda Co-integration Inter linkages between (2007) model and OLS macroeconomics and agro model 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 increased after inflation trading Co-integration Role of US futures trading onNo conclusive evidence inflation in India found Granger Transaction tax effect of Relationship between causality commodity trading volumes transaction cost and volatility is negative Granger Role of futures trading on Granger causality causality establishes inflation for a major price surge of food items flowing from futures to spot Co-integration Cause of fluctuations in the From a macroeconomic prices of agricultural perspective , the futures price commodities in developing lack stability and efficiency countries and related macroeconomic implications Co-integration Evidence of speculation in Recent price surge in Indian commodity markets commodity markets has stipulated the intensity of various factors, which lead to the price volatility Co-integration Efficiency and Risk High settlement cost, poor management warehousing Bose (2009) Sahoo and Kumar (2009) Sen and Paul (2010) Nayak (2010) Varadi, (2012) Agarwal, Jain and Thomas (2014) Influences of macroeconomic 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 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, 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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 markets1 1Research... 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 short-run efficiency of markets. .. interactions of informed reduces price volatility speculators and hedgers Impact of Futures Futures market discovers price trading on spot markets Open interest and spot Volatility of spot prices