Agricultural Commodity Futures Research Articles

What explains long memory in futures price volatility?

Long memory in futures price volatility is a well-documented stylized fact with implications for market efficiency, risk management, forecasting and option pricing bias. The implications of long-memory differ, however, based on whether it is of a ‘fractional’ or of a ‘stochastic’ type. The aims of this article are to determine, in the case of agricultural commodity futures data, which type better describes price volatility and also to evaluate several competing explanations for findings of long memory. The evidence presented here finds little support for three out of four potential explanations, namely, excessive noise in the volatility measure, bias in the long-memory estimator and understated SEs of the long-memory parameter. For the data considered, price volatility appears to be most likely generated by a nonfractional long-memory process such as a stochastic break or stochastic unit root.

Efficiency in agricultural commodity futures markets in India

PurposeIn line with the ongoing global and domestic reforms in agriculture and allied sectors, the Indian Government is reducing its direct market intervention and encouraging private participation based on market forces. This has led to increased exposure of agricultural produce to price and other market risks, which consequently emphasize the importance of futures markets for price discovery and price risk management. The purpose of this paper is to analyze the efficiency of agricultural commodity markets by assessing the relationships between futures prices and spot market prices of major agricultural commodities in India.Design/methodology/approachThe efficiency of the futures market for 12 agricultural commodities, traded at one of the largest commodity exchanges of India, i.e. National Commodity & Derivatives Exchange Ltd, has been explored by using Johansen's cointegration analysis and Granger causality tests. Unit root test procedures such as Augmented Dickey‐Fuller and non‐parametric Phillips‐Perron were initially applied to examine whether futures and spot prices are stationary or not. The hypothesis, that futures prices are unbiased predictors of spot prices has been tested using econometric software package.FindingsResults show that cointegration exists significantly in futures and spot prices for all the selected agricultural commodities except for wheat and rice. This suggest that there is a long‐term relationship between futures and spot prices for most of the agricultural commodities like maize, chickpea, black lentil, pepper, castor seed, soybean and sugar. The causality test further distinguishes and categorizes the commodities based on direction of relationship between futures and spot prices. The analysis of short‐term relationship by causality test indicates that futures markets have stronger ability to predict subsequent spot prices for chickpea, castor seed, soybean and sugar as compared to maize, black lentil and pepper, where bi‐directional relationships exist in the short run.Practical implicationsThe results of this study are useful for various stakeholders active in agricultural commodities markets such as producers, traders, commission agents, commodity exchange participants, regulators and policy makers.Originality/valueThere are very few studies that have explored the efficiency of the commodity futures market in India in a detailed manner, especially at individual commodity level.

Revealing the impact of index traders on commodity futures markets

Commodity futures prices and volatility increased dramatically from 2006 to 2008, following a period during which index traders, a class of large investment funds, took on massive commodity futures positions. This article presents a method to reveal the extent to which index trader trading activity (volume) might have caused increases in futures price volatility. This approach is useful when position-level data are incomplete or confidential, as with index trader position data. The method is applied to leading agricultural commodity futures data. The impact of index traders is identified during their period of greatest activity, that is, 2005 to 2006, using aggregated volume data that are filtered using wavelet transforms. The filtering decision rule is guided by the Commodity Futures Trading Commission's (CFTC) finding that index traders do not engage in short-run trades. A joint model of futures (filtered) volume and (unfiltered) price volatility is estimated by 2SLS to account for the endogeneity of prices and volume. As a robustness check, both log-range and GARCH measures of volatility are used. The evidence provides no support for the claim that index traders have increased price volatility for storable commodities (grains/oilseeds) and only weak support in the case of nonstorable commodities (meats).

A Jump Diffusion Model for Agricultural Commodities with Bayesian Analysis

Price jumps, stochastic volatility, seasonality and stochastic cost of carry have been considered separately, but not collectively, for pricing agricultural commodity futures and options. Focusing on corn, soybeans and wheat, we propose a comprehensive model that incorporates all four features. We employ a special Markov Chain Monte Carlo (MCMC) algorithm, which is new in the agricultural commodity derivatives pricing literature, to estimate the proposed stochastic volatility (SV) and stochastic volatility with jumps (SVJ) models. Along with various model diagnostic tests, the in-sample and out-of-sample pricing and hedging results generally, with few exceptions, lend support for the SVJ model.

Multifractal analysis of China's agricultural commodity futures markets

Abstract We investigate the multifractal characteristics of the volatility time series from China's agricultural commodity futures markets, using Multifractal Detrended Fluctuation Analysis and multifractal spectrum analysis. We find that prominent multifractal features exit in China's major agricultural commodity futures markets, including the Hard Winter Wheat (HW) futures, the Strong Gluten Wheat (SG) futures, Soy Bean (SB) futures and corn futures. Furthermore, the multifractality strength and multifractal spectrum width of HW futures are both bigger than that of SG, SB and corn futures, implying that the market risk for HW futures might be the strongest among all the four futures contracts. Finally, comparing empirical results of shuffling and surrogate data, we also find that nonlinear temporal correlations instead of non-Gaussian distribution constitute the major contributions in the formation of multifractal features in these four agricultural commodity futures markets.

Nonlinear bivariate dependency of price–volume relationships in agricultural commodity futures markets: A perspective from Multifractal Detrended Cross-Correlation Analysis

Nonlinear dependency between characteristic financial and commodity market quantities (variables) is crucially important, especially between trading volume and market price. Studies on nonlinear dependency between price and volume can provide practical insights into market trading characteristics, as well as the theoretical understanding of market dynamics. Actually, nonlinear dependency and its underlying dynamical mechanisms between price and volume can help researchers and technical analysts in understanding the market dynamics by integrating the market variables, instead of investigating them in the current literature. Therefore, for investigating nonlinear dependency of price–volume relationships in agricultural commodity futures markets in China and the US, we perform a new statistical test to detect cross-correlations and apply a new methodology called Multifractal Detrended Cross-Correlation Analysis (MF-DCCA), which is an efficient algorithm to analyze two spatially or temporally correlated time series. We discuss theoretically the relationship between the bivariate cross-correlation exponent and the generalized Hurst exponents for time series of respective variables. We also perform an empirical study and find that there exists a power-law cross-correlation between them, and that multifractal features are significant in all the analyzed agricultural commodity futures markets.

Nonparametric estimation of market risk: an application to agricultural commodity futures

Purpose – While the extant literature is replete with theoretical and empirical studies of value at risk (VaR) methods, only a few papers have applied the concept of VaR to quantify market risk in the context of agricultural finance. Furthermore, papers that have done so have largely relied on parametric methods to recover estimates of the VaR. The purpose of this paper is to assess extreme market risk on investment in three actively traded agricultural commodity futures.Design/methodology/approach – A nonparametric Kernel method was implemented which accommodates fat tails and asymmetry of the portfolio return density as well as serial correlation of the data, to estimate market risk for investments in three actively traded agricultural futures contracts: corn, soybeans, and wheat. As a futures contract is a zero‐sum game, the VaR for both short and long sides of the market was computed.Findings – It was found that wheat futures are riskier than either corn or soybeans futures over both periods considere...

The Theory of Storage and Price Dynamics of Agricultural Commodity Futures: the Case of Corn and Wheat

Using a restricted version of the BEKK model it is tested an implication of the theory of storage that supply-and-demand fundamentals affect the price dynamics of agricultural commodities. The commodities under analysis are corn and wheat. An interest-storage-adjusted-spread was used as a proxy variable for supply-and-demand fundamentals to test the aforementioned implication for both commodities. It is also tested the Samuelson hypothesis that spot prices have higher volatility than futures prices. It is found that the interest-storage-adjusted-spread has had a statistically significant positive influence on the spot and futures returns for both commodities. Likewise, the results also show that spot price returns have higher volatility compared to futures price returns which is consistent with the Samuelson hypothesis. The results of the aforementioned tests are consistent with both theories and with the existing literature related to commodity futures. JEL Classification: C22, G10, Q14.

Multifractal spectrum analysis of nonlinear dynamical mechanisms in China’s agricultural futures markets

Based on Partition Function and Multifractal Spectrum Analysis, we investigated the nonlinear dynamical mechanisms in China’s agricultural futures markets, namely, Dalian Commodity Exchange (DCE for short) and Zhengzhou Commodity Exchange (ZCE for short), where nearly all agricultural futures contracts are traded in the two markets. Firstly, we found nontrivial multifractal spectra, which are the empirical evidence of the existence of multifractal features, in 4 representative futures markets in China, that is, Hard Winter wheat (HW for short) and Strong Gluten wheat (SG for short) futures markets from ZCE and Soy Meal (SM for short) futures and Soy Bean No.1 (SB for short) futures markets from DCE. Secondly, by shuffling the original time series, we destroyed the underlying nonlinear temporal correlation; thus, we identified that long-range correlation mechanism constitutes major contributions in the formation in the multifractals of the markets. Thirdly, by tracking the evolution of left- and right-half spectra, we found that there exist critical points, between which there are different behaviors, in the left-half spectra for large price fluctuations; but for the right-hand spectra for small price fluctuations, the width of those increases slowly as the delay t increases in the long run. Finally, the dynamics of large fluctuations is significantly different from that of the small ones, which implies that there exist different underlying mechanisms in the formation of multifractality in the markets. Our main contributions focus on that we not only provided empirical evidence of the existence of multifractal features in China agricultural commodity futures markets; but also we pioneered in investigating the sources of the multifractality in China’s agricultural futures markets in current literature; furthermore, we investigated the nonlinear dynamical mechanisms based on spectrum analysis, which offers us insights into the underlying dynamical mechanisms in China’s agricultural futures markets.

Fractional Integration in Commodity Futures Returns

AbstractWe reexamine commodity futures returns for evidence of fractional integration utilizing two estimators based on wavelets. We summarize basic wavelet methods for signal processing and decompose commodity futures returns by wavelet scale. We find the evidence for long memory is not conclusive based on visual inspection of the wavelet decomposition, but formal statistical tests suggest evidence of long memory, in the form of antipersistence, in about half of agricultural commodity futures. We find little evidence of long memory in metal futures. Our results are useful in interpreting previous disparate findings based on frequency domain estimators.

Long-range dependence in the volatility of commodity futures prices: Wavelet-based evidence

Commodity futures have long been used to facilitate risk management and inventory stabilization. The study of commodity futures prices has attracted much attention in the literature because they are highly volatile and because commodities represent a large proportion of the export value in many developing countries. Previous research has found apparently contradictory findings about the presence of long memory or more generally, long-range dependence. This note investigates the nature of long-range dependence in the volatility of 14 energy and agricultural commodity futures price series using the improved Hurst coefficient ( H ) estimator of Abry, Teyssière and Veitch. This estimator is motivated by the ability of wavelets to detect self-similarity and also enables a test for the stability of H . The results show evidence of long-range dependence for all 14 commodities and of a non-stationary H for 9 of 14 commodities.

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGACH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.

Long memory in commodity futures volatility: A wavelet perspective

AbstractThe authors reexamine the volatility of agricultural commodity futures for evidence of fractional integration, providing new empirical results and extending the extant literature in important dimensions. First, they utilize two relatively new estimators based on wavelets, which are generally superior to, for example, the popular estimator by J. Geweke and S. Porter‐Hudak (GPH; 1983) and exact maximum likelihood estimators (MLEs) on the basis of mean squared error (MSE). Second, they provide simulations to contrast their point estimates with those obtained by a fractionally integrated GARCH (generalized autoregressive conditional heteroscedasticity) model. Third, they conduct a wavelet coef.cient decomposition of futures volatility. They .nd that futures volatilities display the self‐similarity property consistent with long memory and that futures volatilities exhibit persistent long memory with .nite unconditional variance. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:411–437, 2007

Component structures of agricultural commodity futures traded on the Tokyo Grain Exchange

In this paper, we propose a discrete version of the short-term and long-term component model of the agricultural futures prices. The maximum likelihood estimate of each parameter is obtained using an adaptive filtering algorithm. The diagnostics statistically support the specification of the model. The short-term components exhibit no causal relationship with economic fundamentals such as inflation rate and economic growth rate. These components, therefore, seem to be driven mainly by fads rather than market fundamentals. On the other hand, the long-term components show conitegrating relationship with only one cointegrating vector among the three futures contracts examined.

Testing Futures Returns Predictability: Implications for Hedgers

The predictability of futures returns is investigated using a semiparametric approach where it is assumed that the expected returns depend non parametrically on a combination of predictors. We first collapse the forecasting variables into a single index variable where the weights are identified up to scale, using the average derivative estimator proposed by Stoker (1986). We then use the Nadaraya-Watson kernel estimator to calculate (and visually depict) the relation between the estimated index and the expected futures returns. An application to four agricultural commodity futures illustrates the technique. The results indicate that for each of the commodities considered, the estimated index contains statistically significant information regarding the expected futures returns. Economic implications for a non-infinitely risk averse hedger are also discussed.

Linkages among agricultural commodity futures prices: some further evidence from Tokyo

Booth and Ciner (2001) find that the prices of commodity futures traded on the Tokyo Grain Exchange (TGE) do not move together in the long run. This study analyses whether their empirical results remain true for a more recent period. The empirical results suggest that the cointegrating relation exists among commodity futures contracts from 2000 to 2003, but not earlier during the 1990s. This indicates that the price mechanism works better and the long-run relationships among prices become more apparent as a market develops.

Efficiency tests of agricultural commodity futures markets in China

The efficiency of the Chinese wheat and soybean futures markets is studied. Formal statistical tests were conducted based on Johansen's cointegration approach for three different cash markets and six different futures forecasting horizons ranging from 1 week to 4 months. The results suggest a long-term equilibrium relationship between the futures price and cash price for soybeans and weak short-term efficiency in the soybean futures market. The futures market for wheat is inefficient, which may be caused by over-speculation and government intervention.

A selected review of agricultural commodity futures and options markets

This paper provides a selected review of the research literature on commodity futures and options markets, focusing primarily on empirical studies. The topics featured include the development of intertemporal price relationships, hedging and basis relationships, price behaviour, and discussion of the markets' institutional issues. In each case the recent contributions are recognised. Using this base of information as background, we focus on identifying and motivating future research challenges for agricultural commodity futures and options markets. Copyright 2004, Oxford University Press.

The cross section of expected futures returns and the Keynesian hypothesis

This article identifies some shortcomings in the tests of the Keynesian hypothesis implemented so far. The previous studies either assume integration between futures and equity markets or rely on a methodology that might produce incorrect inferences regarding the presence of a futures risk premium. This article investigates the normal backwardation theory using a methodology exempt from these problems. While short and long hedgers in agricultural commodity futures markets transfer their risk to one another at no cost, the Keynesian hypothesis is found to have some merits in describing the way financial and metal futures prices are set.

Asset storability and hedging effectiveness in commodity futures markets

This paper examines risk minimization hedging effectiveness for major storable and nonstorable agricultural commodity futures markets. Based on the error correction model - bivariate GARCH frameworks, some evidence is found that the hedging effectiveness is stronger for storable commodities than nonstorable commodities under consideration. The finding illustrates an important difference between storable and nonstorable commodities with regard to their hedging function.