Adaptive market efficiency of agricultural commodity futures contracts

In this paper we investigate the adaptive market efficiency of the agricultural commodity futures market, using a sample of eight futures contracts. Using a battery of nonlinear tests, we uncover the nonlinear serial dependence in the returns series. We run the Hinich portmanteau bicorrelation test to uncover the moments in which the nonlinear serial dependence, and therefore adaptive market efficiency, occurs for our sample.     

Market efficiency and natural selection in a commodity futures market

While the literature usually justifies informational efficiencyin the context of rationality, this article shows informationalefficiency by applying the evolutionary idea of natural selection.In a dynamic futures market, speculators are assumed to merelyact upon their predetermined trading types (buyer or seller),their predetermined fractions of wealth allocated for speculation,and their inherent abilities to predict the spot price, reflectedin their distributions of prediction errors with respect tothe spot price. This article shows that the proportion of timethat the futures price equals the spot price converges to onewith probability 1.     

Financialization and speculators risk premia in commodity futures markets

J.M. Keynes coined the term normal backwardation, a situation where a futures price for a particular expiry month is less than the expected spot price for that month. He argued hedgers pay speculators a risk premium, giving rise to normal backwardation. We study the behavior of commodity futures before and since financialization of the markets, which started about 20 years ago. We find the poor returns to managed futures in recent years are likely due to the impact of financialization and the associated outside money suppressing the futures risk premium.     

Volatility forecasting in the Chinese commodity futures market with intraday data

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.     

Does the stock market drive herd behavior in commodity futures markets?

This paper contributes to the debate on commodity financialization by extending tests of herd behavior to commodity futures markets. Utilizing a regime-switching model, we test the presence of herd behavior in a number of commodity sectors including energy, metals, grains and livestock during the low and high market volatility states. We find significant evidence of herd behavior in grains only during the high volatility state. We also find that large price movements in the energy and metal sectors significantly contribute to herd behavior in the market for grains. Finally, we find no significant effect of the stock market on herd behavior in the commodity futures market. Our findings in general do not support the much debated commodity financialization hypothesis.     

Trend following,risk parity and momentum in commodity futures

We show that combining momentum and trend following strategies for individual commodity futures can lead to portfolios which offer attractive risk adjusted returns which are superior to simple momentum strategies; when we expose these returns to a wide array of sources of systematic risk we find that robust alpha survives. Experimenting with risk parity portfolio weightings has limited impact on our results though in particular is beneficial to long–short strategies; the marginal impact of applying trend following methods far outweighs momentum and risk parity adjustments in terms of risk-adjusted returns and limiting downside risk. Overall this leads to an attractive strategy for investing in commodity futures and emphasises the importance of trend following as an investment strategy in the commodity futures context.     

Diversification benefits of commodity futures

The finance literature seems to be in support of the diversification benefits of adding commodity futures to an existing portfolio. Yet no empirical work has been performed to test whether the benefits are indeed statistically significant. This paper addresses several unresolved issues concerning the potential diversification benefits of commodities. First, we attempt to ascertain whether the alleged diversification benefits exist and are statistically significant. Second, to what extent are the diversification benefits unique to US investors? Would investors of a resource-based economy like Canada also benefit from adding commodities to their portfolios? Third, recent studies indicate that correlations among international equity returns are higher during bear markets than during bull markets. This type of regime-switching correlation behavior will mean lower diversification benefits from international investments when investors face a bearish environment at home. Do commodity futures display the same type of regime-switching behavior? To what extent do commodity futures offer real diversification benefits that are robust over time and across regimes? Finally, commodities may appear to be an asset for the more adventurous investors with higher risk tolerance. We want to know what type of investors should hold commodities. We demonstrate that the diversification benefit of commodities is a far more complex phenomenon than often understood in the finance literature.     

Tail risk of coal futures in China's market

This study evaluates the downside tail risk of coal futures contracts (coke, coking coal and thermal coal) traded in the Chinese market between 2011 and 2021, measured by value at risk (VaR). We examine the one-day-ahead VaR forecasting performance with a hybrid econometric and deep learning model (GARCH-LSTM), GARCH family models, extreme value theory models, quantile regression models and two naïve models (historical simulation and exponentially weighted moving average). We use four backtesting techniques and the model confidence set to identify the optimal models. The results suggest that the models focusing on tail risk or utilising long short-term memory generate more effective risk management.     

Regulatory reform and market efficiency: The case of Indian agricultural commodity futures markets

We present the results of two efficiency measures that include intraday return predictability measure based on order imbalance and measures of several variance ratio tests on intraday subsamples of nine major Indian agricultural commodity futures (castor seed, cotton oil cake, rape mustard seed, soybean, refined soya oil, crude palm oil, jeera, chana, and turmeric) quoted in the National Commodity and Derivatives Exchange (NCDEX). We perform the efficiency measures on five subsamples with holding periods of 5, 10, 15, 30, and 60 min over two sample periods following the announcement of the merger between the Forward Market Commission (FMC) and Securities Exchange Board of India (SEBI). We compare results of tests of weak-form market efficiency of futures markets between two periods (pre-merger period and post-merger period). Our results confirm that Indian agricultural commodity futures markets continue to remain inefficient in the short-term during both pre-merger and post-merger periods. Based on these findings, it is likely that profitable trading strategies in the short intraday intervals will be available for traders and market participants during post-merger period. Thus, regulators must focus more on policy initiative so as to enhance market quality in order to address such inefficiencies in Indian commodity futures markets.     

Capturing the risk premium of commodity futures: The role of hedging pressure

We construct long–short factor mimicking portfolios that capture the hedging pressure risk premium of commodity futures. We consider single sorts based on the open interests of hedgers or speculators, as well as double sorts based on both positions. The long–short hedging pressure portfolios are priced cross-sectionally and present Sharpe ratios that systematically exceed those of long-only benchmarks. Further tests show that the hedging pressure risk premiums rise with the volatility of commodity futures markets and that the predictive power of hedging pressure over cross-sectional commodity futures returns is different from the previously documented forecasting power of past returns and the slope of the term structure.     

Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets

The dynamic minimum variance hedge ratios (MVHRs) have been commonly estimated using the Bivariate GARCH model that overlooks the basis effect on the time-varying variance–covariance of spot and futures returns. This paper proposes an alternative specification of the BGARCH model in which the effect is incorporated for estimating MVHRs. Empirical investigation in commodity markets suggests that the basis effect is asymmetric, i.e., the positive basis has greater impact than the negative basis on the variance and covariance structure. Both in-sample and out-of-sample comparisons of the MVHR performance reveal that the model with the asymmetric effect provides greater risk reduction than the conventional models, illustrating importance of the asymmetric effect when modeling the joint dynamics of spot and futures returns and hence estimating hedging strategies.     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.     

Optimal commodity asset allocation with a coherent market risk modeling

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.     

日本商品期货市场近年衰落的原因和思考

进入21世纪以来,曾引领亚洲期货市场发展的日本商品期货市场明显衰落。在最初的要素条件、需求状况等外生比较优势逐步削弱的同时,规模经济、技术进步、产品差别等内生比较优势发展的滞后及市场分割、产业需求不足等固有矛盾的暴露,决定了日本商品期货市场发展的下行态势,日本监管当局规范和发展期货市场脱离实际的改革措施尤其加速了市场衰落的进程。本文从日本商品期货市场的现状入手,通过比较优势理论分析衰落的原因,并从大国商品期货市场的竞争和发展中探寻教训与经验,为我国商品期货市场发展提供借鉴。     

Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Commodity markets are a widely researched topic in the field of finance. In this paper, we investigate the co-movement of return and volatility measures in different commodity futures markets and how these measures are affected by liquidity risk. First, we find that commodity returns display co-movement and that liquidity risk plays a key role in shaping asset return patterns. Moreover, we show that the volatilities of commodity returns co-move, and we demonstrate the role of liquidity risk in this joint pattern. We also find that the commodity markets we investigated share a common volatility factor that determines their joint volatility co-movement. Because liquidity risk affects both commodity returns and volatility shocks, it might be interpreted as the common causal factor driving both measures simultaneously. Therefore, we affirm the view that liquidity shocks are firmly related to two residual risks originating from both market return and market volatility. Finally, we also show that liquidity spillovers can significantly drive cross-sectional correlation dynamics.     

An empirical study on the regulated Chinese agricultural commodity futures market based on skew Ornstein-Uhlenbeck model

This paper describes the regulated agricultural commodity futures market of China, focusing on six actively traded futures: corn, strong gluten wheat, No. 1 soybean, soymeal, cotton, and white sugar. A novel skew Ornstein-Uhlenbeck model is employed to characterize price dynamics with government controls. The empirical analysis reveals significant skew phenomena in these six futures and indicates that the price dynamics are influenced by state policy. The observed skew phenomena are most notable in grain futures, with relatively weaker, but statistically significant, evidence of skew phenomena in oilseed and soft futures markets. In addition, generalized quasi-likelihood ratio tests show that the skew Ornstein-Uhlenbeck model is superior to the Ornstein-Uhlenbeck model.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.     

Systemic risk of commodity markets: A dynamic factor copula approach

This study aims to measure the systemic risk of commodity markets and investigate its causal relationship with the macroeconomy. First, we propose a novel measure called the joint probability of abnormal changes (JPAC) to measure the systemic risk of commodity markets. Second, we introduce two new measures, the expected proportion of commodities in abnormal price change (EPAPC) and the contribution to systemic risk (CSR), to identify the systemic importance of each commodity. Third, we examine the relationship between JPAC and some key macroeconomic variables using mixed-frequency Granger causality tests. We conduct an empirical study using 24 commodity indices from 2015 to 2021. Our results reveal that: (1) JPAC can well capture the risk events in the real world; (2) the systemic risk of commodity markets has increased significantly since the start of the US-China trade war; (3) EPAPC and CSR indicate that energy commodities have higher systemic importance than others and are most likely to cause market fluctuations; and (4) some causal relationships between the systemic risk of commodity markets and the macroeconomy are identified. Overall, this study improves our understanding of systemic risk in commodity markets and provides important implications for policymakers in China and the US.     

The market impact of systemic risk capital surcharges

This paper tests the ‘Too-Big-to-Fail’ hypothesis that whether being designated as a global systemically important bank (G-SIB) has an impact on the credit default swap (CDS) price of the bank, thereby reducing its credit risk. We find surprising evidence that the CDS spreads of a bank increase (decrease) after the announcement of a higher (lower) capital surcharge. However, this effect is temporary, as the mean CDS spreads revert to preannouncement level, dropping sharply after the initial rise. These findings create a puzzle by implying that a higher capital surcharge requirement and more stringent regulation could outweigh the implicit subsidy advantages of being too-big-to-fail.