Journal of Commodity Markets最新文献

Investors' focus on specific topics could translate into actual trading behavior, subsequently influencing market prices. Within the crude oil market, the issue of climate change risk arising from carbon emissions has garnered considerable attention recently, as investors' search behavior regarding this topic may impact crude oil prices. Based on the search information provided by Google, this paper employs quantile and quantile-on-quantile regression (QQR) methods to examine the relationship between investors' attention to climate change risk and crude oil futures price returns. The results reveal the following: (1) Simultaneous opposite correlations are observed, with a significantly positive relationship between attention and returns during high returns and a significantly negative relationship during periods of low returns. The correlation between the two exhibits considerable variation across different market performances. (2) A significant negative correlation exists mainly between attention to physical and opportunity risk and returns, while positive correlations exist mainly between attention to regulatory risk and returns. (3) Higher levels of climate change attention intensify these effects, as evidenced by an increase in the absolute value of the regression coefficients. The findings of this study can serve as a reference for investment institutions and policymakers in constructing investment portfolios and managing the impact of climate risk.

We document that the stock prices of food-sector firms react to USDA news. The economic and statistical significance of the effect depends on the commodity, type of scheduled USDA report, and direction and extent to which the USDA information surprises the market. Individual stock price responses to USDA news differ between firms on the input-side vs. firms on the output-side of agricultural (farm) production, based on which component of the firm's cash-flow expectations (costs or revenues) and which variable (commodity price or expected firm output) is impacted by the news. Planted Area surprises have the largest effect for both subsets of firms (ag-as-inputs and ag-as-output), followed by Grain Stocks news—with the effects having the expected sign. In contrast, WASDE surprises have very modest and mixed impacts on food-sector stock returns. Our findings establish that USDA announcements have an impact well beyond their recognized relevance to commodity markets.

The estimation of risk at extreme levels (such as 0.1%) can be crucial to capture the losses during market downturns, such as the global financial crisis and the COVID-19 market crash. For many existing models, it is challenging to estimate risk at extreme levels. In order to improve such estimation, we develop a framework to estimate Value-at-Risk and Expected Shortfall at an extreme level by extending the one-factor GAS model and the hybrid GAS/GARCH model to estimate Value-at-Risk and Expected Shortfall for two levels simultaneously, namely for an extreme level and for a more common level (such as 10%). Our simulation results indicate that the proposed models outperform the GAS model benchmarks in terms of in-sample and out-of-sample loss values, as well as backtest rejection rates. We apply the proposed models to oil futures (WTI, Brent, gas oil and heating oil) and compare them with a range of parametric, nonparametric, and semiparametric alternatives. The results show that our proposed models are generally superior to the alternatives.

This study explores the connectedness between major oil-producing and consuming countries' stock markets (United States, China, Russia, India) and different oil shocks categorized as demand, supply, and risk shocks, following Ready's (2018) framework. Employing a quantile-based connectedness approach and quantile cross-spectral dependence, our analysis spans from July 02, 2007 to May 31, 2023, encompassing diverse market conditions and events. These methodologies help identify interdependence patterns in extreme market scenarios at different time intervals. Key findings show variations in how these stock markets respond to oil shocks, depending on market conditions and quantiles. Demand-related shocks have the most significant spillover effects on the United States, Russia, and India, while risk-related shocks dominate as transmitters of shocks to the United States, China, and India in median quantiles. Market interconnectedness strengthens during extreme market conditions, reflecting historical events. Additionally, bearish markets offer diversification opportunities between these countries and crude oil. This study emphasizes the need for tailored investment strategies, monitoring global oil demand trends, dynamic portfolio management, crude oil inclusion in portfolios, and proactive responses to market players and geopolitical events. These insights benefit investors and policymakers seeking to optimize strategies in the interconnected global financial landscape.

The long and continuing coal-dominated energy structure in China makes it important to investigate the impact of coal price shocks on China's financial markets. This study identifies whether volatilities in coal market may propagate between sectoral equity markets through the heterogeneous connectedness between these markets, and even further contribute to larger scale overall instabilities. We first apply the cross-spectral quantile coherency (QC) to identify the time-frequency interconnectedness among returns of 28 sectors in China's equity market. A spatial autoregressive (SAR) framework based on the QC network is further utilized to identify the indirect effect propagating through the heterogeneous interconnectedness between 28 sectoral equity markets. The empirical results indicate significant risk contagion effects during market turmoil, while strong risk absorbing effects are confirmed for the tranquil case. The significantly varying sectoral interconnectedness along with the corresponding heterogeneous pattern of shock propagation under various market specifications may provide evidence for the spillover effects to be the key mechanism and the sectoral interconnectedness as an important channel for coal price shock propagation, which is essential to the effectiveness of portfolio diversification and financial stabilizing policy.

Traditionally, meat demand studies have estimated the demand for pork at the aggregate commodity level, but this study proposes wholesale pork demand estimation at the pork primal level. Flexibilities for the primal cuts as well as beef and chicken are estimated using an inverse almost ideal demand system (IAIDS). Own-quantity flexibilities for pork primal cuts are largely inflexible and statistically different from one another, suggesting heterogeneity exists in demand for pork at the primal level. Among the pork primal cuts, we find changes in quantity demanded result in the greatest percentage change in the price of loins and the smallest percentage change in the price of bellies. Ultimately, this study provides necessary information for the U.S. pork industry as recent policies, such as California's Proposition 12, are spurring changes in the pork production landscape. Estimated elasticities can be used in pork demand-building efforts both today and into the future.

We examine the type and quality of information OPEC needs to successfully stabilize the oil market. Our analysis considers the impact of observational errors regarding market shocks as well as erroneous judgments of demand and supply elasticities. Actual prices resulting from OPEC's historical efforts to dampen volatility are compared to counterfactual prices that would have prevailed had OPEC remained passive. Despite the potentially confounding effect of misinformation, the elevated counterfactuals indicate that OPEC has managed to substantially decrease price volatility. Indeed, during the 2017–2021 OPEC+ period we estimate price volatility would have been up to 100% greater than actual without the actions of OPEC and its allies.

The analysis of option pricing in derivative markets has commonly relied on the Black-Scholes model. This paper presents a conceptual and empirical analysis of option pricing with a focus on the validity of key assumptions embedded in the Black-Scholes model. Going beyond questioning the lognormality assumption, we investigate the role played by two assumptions made about the nature of price dynamics: quantile-specific departures from a unit root process, and the role of quantile-specific drift. Our analysis relies on a Quantile Autoregression (QAR) model that provides a flexible representation of the price distribution and its dynamics. Applied to the soybean futures market, we examine the validity of assumptions made in the Black-Scholes model along with their implications for option pricing. We document that price dynamics involve different responses in the tails of the distribution: overreaction and local instability in the upper tail, and underreaction in the lower tail. Investigating the implications of our QAR analysis for option pricing, we find that failing to capture local instability in the upper tail is more serious than failing to capture “fat tails” in the price distribution. We also find that the most serious problem with the Black-Scholes model arises in its representation of price dynamics in the lower tail.

This paper examines quantile coherency in bonds, commodities, currencies, and equities using a novel quantile coherency approach. While recent literature has explored single-frequency tail- and time-frequency dependence in asset returns, we provide fresh evidence on asset return dependence across quantiles (proxying business cycles or market conditions) at different frequencies (representing investment horizons). Considering sixty-seven individual asset return series in four asset classes, we observe that low frequency (yearly) dependence is stronger in the bond, foreign exchange, and equity markets. Specifically, we find strong dependence between the German and French bond markets, heating oil and crude oil, gold and silver, British Pound, and Euro, French and German and Canadian and US equities. As we report asset return interdependence in different business cycles and at different time horizons, these results have important implications for portfolio allocation and investment strategy formulation.

We study the out-of-sample predictability of monthly crude oil prices using forecast combinations constructed from several individual predictor forecasts. Our empirical results indicate that combination forecasts of monthly average oil prices are more accurate than the no-change forecast with statistically significant reductions in mean square forecast errors (MSFE) and significant directional accuracy at every horizon up to 24 months, consistent with earlier evidence that forecast combinations greatly enhance the forecastability of oil prices. In contrast, we find no significant MSFE reductions or directional accuracy for forecasts of end-of-month oil prices at almost all horizons. Furthermore, we document that end-of-month forecasts when used to guide investment and hedging decisions of investors, statistically, do not deliver superior economic value to investors. Overall, the implication of our results is that the statistical and economic significance of forecasts of oil prices is heavily influenced by the construction of the underlying oil price series and provide a cautionary note on which oil price series to use in forecasting.