Journal of Commodity Markets最新文献

This study examines the relationship between the unexpected changes in earnings and the shipping market movements. The econometric method of variance decomposition proposed by Campbell (1991) is employed to empirically analyze the Panamax and Capesize markets. We find that a large proportion of unexpected earnings growth is related to news about returns that indicate subsequent price changes. The results provide important insights to practice for sustaining shipping businesses, which helps shipping companies make better investment and risk-management decisions. The contribution of this research is to deepen the understanding of the interaction between shocks to earnings growth, returns, and price–charter ratios in the present-value context.

This paper applies the Narrative-based Energy General Index (NEG) to forecast stock returns in the energy industry. The index is constructed using natural language processing (NLP) techniques applied to news topics from The Wall Street Journal. The results indicate that NEG outperforms in predicting future returns of the energy industry in both in-sample and out-of-sample, and the predictive power surpasses that of other macroeconomic variables. The asset allocation exercise demonstrates the substantial economic value of NEG. Furthermore, we document that NEG not only exhibits superior predictive power for energy sector returns but also provides valuable insights for the whole stock market.

There is extensive literature on problems involved in estimating implied parameters in the Merton Jump Diffusion model. Using simulated data, we use weighted non-linear least squares to estimate implied parameters in the four parameter jump diffusion model (JD) and in an eight parameter jump diffusion model with convenience yield (JDC). We find reliable and accurate implied parameter estimates for the JD model but biased and unreliable estimates for some parameters in the JDC model. However, for both models we estimate accurate option prices, usually within several basis points. We also use Bitcoin real data to estimate parameters and test the out-of-sample performance of the JDC model.

This paper investigates the asymmetric effect of G7 stock market volatility on predicting oil price volatility under different oil market conditions by using the quantile autoregression model. Both in- and out-of-sample results demonstrate the prediction superiority and effectiveness of the quantile autoregression model. The US and Canada's stock markets exhibit the strongest predictive ability across the entire distribution, while the UK demonstrates strong predictive power specifically during periods of high oil price volatility. Japan, Germany, France, and Italy as oil importers can predict low and median oil volatility. The strong predictability of G7 stock volatility may be attributable to their significant impact on the business cycle and investor sentiment. This asymmetric prediction ability arises not only from the average volatility shocks at various quantiles but also from the bad and good stock volatility at different quantiles. Further research suggests that bad stock volatility appears to be more predictable than good stock volatility, especially in high oil price fluctuations. Furthermore, the superiority and effectiveness of the quantile autoregression model in predicting oil volatility are proven to be applicable to emerging markets. This study may provide useful insights for policymakers, businesses, and investors to improve crude oil risk prediction and risk management under different market conditions.

In this paper, we examine the time-varying tail risks transmission among the agricultural, precious metals, and energy commodities markets, and explore how climate change concerns affect this connectedness. Using the Conditional Autoregressive Value-at-Risk (CAViaR) model and the time-varying parameter vector autoregressive (TVP-VAR) connectedness model, our empirical analysis reveals several key findings. First, our tail risk-based approach shows that tail risks transmission rises during crisis periods such as the GFC of 2007 and the Covid period of 2020. Second, climate risks, in particular climate transitions risks, play an important role in commodity tail risk connectedness. These findings are important for investors, practitioners, and policymakers. Our results are robust to a number of robustness tests.

Bitcoin is often labelled digital gold and many studies compare bitcoin and gold prices, returns and volatility. This paper digs deeper and compares the characteristics of bitcoin mining with gold mining to assess claims that bitcoin is digital gold. We identify 20 differences between gold and bitcoin mining. Gold miners locate where gold is present while bitcoin miners locate where cheap electricity is present. Gold mining has large barriers to entry relative to bitcoin mining making it relatively difficult to start and abandon a gold mine but much easier to start and abandon a bitcoin mine. This is reflected in a greater exposure of gold miners to gold prices and a smaller exposure of bitcoin miners to bitcoin prices. While the analysis demonstrates that bitcoin mining is less complex and less risky than gold mining, the similarities support the idea that bitcoin is digital gold.

Using proprietary data reported by swap dealers to the Commodity Futures Trading Commission, we first present new evidence on the size and composition of 13 over-the-counter agricultural swaps markets. We then utilize our novel dataset to show the existence of linkages with the equity markets. We use the spike in the Chicago Board Options Exchange Volatility Index in early 2020 to show that swaps trader positions were significantly impacted by the financial market volatility created by the COVID-19 pandemic. Following similar methods as Cheng et al. (2015), we find index swaps traders reduce their net long positions in response to tightening financial conditions, while commercial swaps traders absorb some of this risk by decreasing their net short positions. This internal swap market netting occurs in three of the four largest agricultural markets: corn, soft red winter wheat, and sugar. Concurrently, we observe a limited swap dealer hedging response in the futures market, especially when compared to other financial traders, consistent with swap market netting. Our results confirm that equity market shocks can affect financial traders in both commodity swaps and futures markets.

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.

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.

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.