Journal of Commodity Markets最新文献

This paper proposes a flexible framework for structuring and pricing parametric heat wave insurance. The framework is based on a general heat wave definition formulated in terms of an underlying temperature index. The definition can be varied in terms of the heat wave duration, intensity, measurement period and underlying index. This construction makes it straightforward to create contracts tailored to insure against heat events of many different types. A single stochastic model for the underlying index can be used to price all contracts. We consider contracts with payments that depend on the number of heat waves of a certain type occurring in the measurement period and derive the necessary pricing relations based on a general model structure encompassing several popular temperature models in the literature. An empirical case study is performed using data for Berlin where the daily maximum temperature is used as the underlying index. Model implied heat wave probabilities are consistent with historical patterns, point to high likelihoods for short duration heat events of different threshold temperatures and non-negligible risks for future heat waves of extreme temperatures and durations never before observed.

We find out-of-sample predictability of commodity futures excess returns using combination forecasts of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean–variance investor. Commodity return forecasts are closely linked to the real economy. Return predictability is countercyclical, and the combination forecasts of commodity returns have significant predictive power for future economic activity. Two-factor models featuring the market factor and the innovations in each of the combination forecasts explain a substantial proportion of the cross-sectional variation of both commodity and equity returns. The associated positive risk premiums are consistent with  Merton’s (1973) intertemporal capital asset pricing model (ICAPM), given how the combination forecasts predict an increase in future economic activity and a decline in stock market volatility in the time-series. Overall, combination forecasts act as state variables within the ICAPM, thus resurrecting a central role for macroeconomic risk in determining expected returns on commodities.

The United States and Brazil fiercely compete with each other in the Chinese soybean import market. Logistical functions and costs are volatile and risky and influence the export competition between the two countries. This study analyzes commodity trading strategies and the effect of logistical functions and costs in the United States and Brazil for shipments to China using an Optimized Monte Carlo Simulation model accounting for a large number of random and correlated variables. Base case results approximate the actual monthly data between 2013 and 2019. These results indicate that the United States captures a larger share of soybean export shipments between December and March while Brazil is dominant from April to November. Sensitivity analyses were performed on logistical variables in the United States (ocean shipping costs, U.S. secondary rail car market, and rail unload incentives) and Brazil (improving logistical infrastructure and wait times) to illustrate their impacts on optimal trading strategies.

In this paper, we revisit traditional gas pricing formulas and show the ever-changing relationships between natural gas and oil prices in Europe, the United States, and Japan between 2009 and 2021. The results suggest a stronger oil–gas link for all investigated markets after 2019, significantly impacted by fundamental supply and demand factors. However, the strength of the equilibria link differs across markets due to different price formation processes under the impact of the COVID-19 pandemic and the Ukraine war. For Japanese LNG prices, our results imply an enduring impact of oil-price indexation with a tight link to monthly crude prices. TTF and monthly oil prices enter a temporary equilibrium in times of high market volatility, whereby the long-term equilibrium dissipates. Despite the absence of oil indexation in the North American market, we find evidence of re-coupling of oil and gas prices given the demand shock of the COVID-19 pandemic. These findings are relevant to policy makers to assess market inefficiencies caused by the European gas crisis.

This paper contributes to the price discovery literature by establishing, for the first time, the role of commodity spot market auction data. Using the New Zealand whole milk powder market as an example, we show that auction-level data explain the price discovery dynamics above and beyond determinants previously identified as being relevant to spot and futures market price formation. In particular, the price discovery of the futures market rises with the volume of dairy products traded at the auction, signaling that the volume auctioned induces a change in the trading strategies of futures market participants. The whole milk powder discovery process is found to primarily take place in the spot market, which aligns well with the auction predating the introduction of the futures market, its higher volume, and lower trading costs.

This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.

We analyze various types of models for Value at Risk (VaR) forecasts for daily copper returns. The period of the analysis is from January 4, 2000 to January 14, 2021 including 5290 daily closing prices. The models considered are GARCH-type models, the Generalized Autoregressive Score model, the Dynamic Quantile Regression model, and the Conditional Autoregressive Value at Risk model specifications. The best model is selected using the Model Confidence Set approach. This approach provides a superior set of models by testing the null hypothesis of equal predictive ability. The findings suggest that the EGARCH model outperforms the rest of the models for the copper commodity under investigation.

Commodity style integration is appealing because by forming a unique long-short portfolio with exposure to K mildly correlated factors, a larger and more stable risk premium can be extracted than with any of the standalone styles. A key decision that a commodity style-integration investor faces at each rebalancing time is the relative weighting of the factors. We propose a Bayesian optimized style-integration (BOI) strategy with excellent out-of-sample performance. Focusing on the problem of a commodity investor that seeks exposure to the carry, hedging pressure, momentum, skewness, and basis-momentum factors, the evidence suggests that the BOI portfolio achieves better Sharpe ratios and certainty equivalent returns, among other performance metrics, than the $1/K$ style-weighted integrated portfolio, and a battery of sophisticated optimized integrations. The findings survive the consideration of longer estimation windows, various commodity score schemes, and alternative Bayesian priors.

Investigating the dependence and connectedness among global oil markets is of great significance for cross-market investors and regulators. However, most of the existing studies are confined to lower-order moments and the time domain. This paper is the first to examine the time-frequency dependence and connectedness among global oil markets from the higher-order moment perspective by applying the wavelet coherence method and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach. The empirical results demonstrate that higher-order moment dependence among oil markets is weaker than return and volatility dependence. In general, Dubai, Minas, and Tapis oil exhibit relatively higher wavelet coherence with Daqing oil at all moments. The lead-lag relationships are heterogeneous during most sample intervals. The total return and volatility connectedness indices are higher than the skewness and kurtosis. The return connectedness mainly occurs in the short term (1–5 days) whereas the volatility, skewness, and kurtosis connectedness occur in the long run (22-Inf days). West Texas Intermediate oil dominates the return, volatility, and skewness connectedness network while Dubai oil dominates the kurtosis connectedness network. Furthermore, the dynamic total, net, and net-pairwise connectedness indices are all time-varying and event-dependent with the higher-order moment connectedness illustrating more volatile features. Several practical implications are provided for various market agents.

This paper assesses the effects and transmission mechanisms of global liquidity and commodity market shocks in Mongolia, a commodity-exporting developing economy, using a structural vector autoregression (SVAR) model. Results show that boom and bust cycles in commodity and international financial markets lead to business and financial cycles in the economy as these shocks account for 30, 45, and 60 percent of domestic output, real exchange rate, and lending rate fluctuations, respectively. Commodity demand shocks have more persistent and robust effects on domestic cycles than commodity supply shocks. Trade and financial (resource export revenues, lending rate, and exchange rate) channels are essential in transmitting the shocks. Buoyant commodity demand and global liquidity shocks lead to a significant fall in the domestic lending rate, while positive commodity supply and global liquidity shocks appreciate the real exchange rate.