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Do traders in futures markets make use of all relevant information and is this reflected in prices? This collection of original essays by a team of international economists considers these and other questions central to futures markets

Do traders in futures markets make use of all relevant information and is this reflected in prices? This collection of original essays by a team of international economists considers these and other questions central to futures markets.

Commodity price forecasting is challenging full of volatility, uncertainty and complexity. In this paper, a novel modeling framework is proposed to predict the market price of commodity futures. Three types of commodity are selected as representatives: corn from agricultural products, gold from industrial metal and crude oil from energy. We decomposed the original series into independent components at various scales using singular spectrum analysis (SSA). A SSA-causality test is introduced to investigate the mutual influence between commodity futures prices. Additionally, using the SSA-smoothing scheme, we construct combined neural network models including back propagation, radial basis function and wavelet neural network to predict the commodity price. The experimental results illustrate that neural network models with the SSA outperform the benchmarks in terms of distinct measures.

The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challenging due to the complex, nonlinear characteristics of agricultural markets and the diverse range of influencing factors. To overcome these challenges, this study develops a novel price forecasting framework that combines advanced time series decomposition, swarm intelligence optimization, and deep learning techniques. The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. These components are then fed into a CNN-augmented BiLSTM model, enhanced with an attention mechanism to extract both temporal dependencies and intricate data relationships. To fine-tune the model's hyperparameters, this study introduces a multiple strategies dung beetle optimisation algorithm (MSDBO), which integrates four strategic modifications to improve the balance between global search, local exploration, and convergence efficiency. Using historical data from corn and wheat markets as case studies, the experimental findings demonstrate that the proposed SVMD-MSDBO-CNN-BiLSTM-A model significantly outperforms nine baseline approaches. Specifically, it reduces the Mean Absolute Percentage Error (MAPE) by 25.78% and 37.57%, respectively, and enhances directional accuracy (Dstat) by 1.15% and 14.53% compared to the top single models.

Mean reversion, stochastic volatility, convenience yield and presence of jump clustering are well documented salient features of commodity markets, where Asian options are very popular. We propose a model which takes into account all these stylized features. We first state our model under the historical measure, then, after introducing a structure preserving change of measure, we provide a risk-neutral version of the same model and we show how to price geometric and arithmetic Asian options. To this end, we derive semi-closed formulas for the geometric Asian options price and develop a computationally efficient simulation scheme for the price process, allowing to price the arithmetic counterparts using control variate technique. Finally, we propose a simple econometric experiment to document presence of jump clusters in commodity prices and evaluate the performances of the proposed simulation scheme on some parameter sets calibrated on real data.

The run-up in oil prices since 2004 coincided with growing investment in commodity markets and increased price co-movement among different commodities. We assess whether speculation in the oil market played a role in driving this salient empirical pattern. We identify oil shocks from a large dataset using a dynamic factor model. This method is motivated by the fact that a small-scale vector autoregression is not informationally sufficient to identify the shocks. The main results are as follows. (i) While global demand shocks account for the largest share of oil price fluctuations, speculative shocks are the second most important driver. (ii) The increase in oil prices over the last decade is mainly driven by the strength of global demand. However, speculation played a significant role in the oil price increase between 2004 and 2008 and its subsequent collapse. (iii) The co-movement between oil prices and the prices of other commodities is mainly explained by global demand shocks. Our results support the view that the recent oil price increase is mainly driven by the strength of global demand but that the financialization process of commodity markets also played a role.

The recent growth in economic and financial markets has brought the focus on energy derivatives as an alternative investment class for investors, financial analysts, and portfolio managers. The financial modeling and risk management of portfolios using the energy derivatives instrument is a requirement and challenge for researchers in the field. The energy and other commodity futures force the expert investors to investigate the broader investment spectrum and consequently diversify their portfolios using the futures instruments. Going beyond the conventional portfolios and developing out-of-the-box strategies that comply with the changing financial and economic advancements are the keys to long-term sustainability in the financial world. This study investigates the impact of diversification with five energy futures from January 2011 to July 2020 on three traditional commodity futures portfolios. The results show that diversification increased the returns while simultaneously reducing the portfolio volatility in all portfolios. The diversified portfolios provided higher returns than the traditional portfolios for the same level of risk. This study also revealed that the results might improve when a short position in the futures contracts is allowed. Moreover, we conclude that adding multiple energy futures in a portfolio provides enhanced diversification results, whereas the WTI crude oil futures fail to diversify any portfolio considered in the study.

This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Agricultural price volatility poses critical challenges for food security, economic stability, and the livelihoods of millions, particularly in developing countries like India. Accurately forecasting these price fluctuations is vital for effective policymaking and strategic decision-making in agricultural markets. This study investigates the potential of deep learning models, specifically LSTM, and their integration with GARCH for forecasting agricultural commodity price volatility. Using extensive historical price data for 23 commodities across 165 markets in India from February 2010 to June 2024, the proposed hybrid model demonstrates significantly enhanced accuracy and robustness compared to standalone econometric or deep learning models. The results suggest that this hybrid approach effectively addresses price instability, offering improved predictive capabilities. These findings provide valuable implications for policymakers and stakeholders, emphasizing the adoption of advanced machine learning techniques for better market risk management and policy interventions tailored to agricultural price dynamics.

This study investigates the impact of economic policy uncertainty (EPU) on the volatility of European Union (EU) carbon futures prices and whether it has predictive power for the volatility of carbon futures prices. The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance (EUA) futures. We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models. Our empirical results show that the GARCH-MIDAS models, which exhibit superior out-of-sample predictive ability, outperform GARCH-type models. The results also indicate that EPU has noticeable effect on the volatility of EUA futures. Specifically, the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index. Robustness checks further confirm that the EPU index (especially the EPU index of the EU) has strong predictive power for EUA futures prices. Additionally, using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index, investors can construct their portfolios to realize economic returns.

This paper provides evidence that the two leading principal components in a panel of 23 commodity convenience yields have statistically and quantitatively important predictive power for inflation even after controlling for unemployment gap and oil prices. The results hold up in out-of-sample forecasts, across forecast horizons, and across G7 countries. The convenience yields also explain commodity prices and can be seen as informational variables about future economic conditions as conveyed by the futures markets. A bootstrap procedure for conducting inference when the principal components are used as regressors is also proposed.