Journal of Commodity Markets最新文献

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.

The CFTC’s Commitments of Traders reports (DCOT and SCOT) are a key source of information about the open interest in commodity derivatives markets. While informative, these publications leave open four important questions. (1) Do traders that hold large positions every single day make up most of the total open interest? How big is that “market core”? (2) What is the relation between DCOT figures on swap dealer futures positions and CIT futures positions? (3) Are most futures traders long-only or short-only, or do they hold “mixed” positions? (4) Who makes up the fast-growing “Other Reportables” category that comprises all noncommercial market participants that are not managed money traders? We tackle those questions with regulatory data on futures positions in the four largest U.S. grain and oilseed markets in 2015–2018.

In this paper, we select 75 indicators to conduct a comprehensive analysis of the factors influencing the copper price along six dimensions: inventory, supply, demand, the macroeconomy, finance, and geopolitics. Facing the high-dimensionality problem, we use the least absolute shrinkage and selection operator (LASSO) regression model to select variables to measure the contribution of each category of factors. Furthermore, we identify the time-varying nature of the relationship among factors with rolling windows. Then, we decompose copper prices into different scales of fluctuations by means of empirical mode decomposition (EMD) and investigate the driving factors at each scale. The results show that financial and geopolitical factors have played an important role in copper pricing in recent years. The long-run fluctuation trend of copper prices is mainly determined by fundamental factors, while financial and geopolitical factors have a more direct impact on short-term fluctuations.

A number of studies have documented that there is a global market for salmon, in which wild and farmed salmon have a common price determination process. Recent studies report that Norwegian farmed salmon spot prices are also highly correlated with the Fish Pool salmon future contract price, indicating that the futures market can be an important risk management tool, as producers and buyers can hedge price risk. Here, we investigate whether the wild salmon prices can be cross-hedged against the Fish Pool salmon future contract, by testing whether the two prices are correlated.. If so, the Fish Pool future contracts can be used as a risk management tool for fishers and buyers of wild salmon. We find that this is the case, providing an additional link between wild and farmed salmon specifically, and between fisheries and aquaculture generally.

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.

We study the immediate impact of heightened geopolitical tensions caused by the Russia-Ukraine war, on volatility connectedness networks of 18 global stock markets and 5 major commodities. Our analyses reveal a shift in connectedness spillovers during the war: while crude oil (a net shock transmitter before the war) became a net shock receiver, shocks transmitted by crude oil net importers appear to primarily contribute to crude oil turning a net shock receiver, whereas for platinum and wheat, we observe that both net exporters and importers have received volatility shocks. We further dissect the impact of war on the direction of spillovers using panel censored regressions. Employing insights from the analyses, we design portfolios that weigh higher (lower) on stock indices with lower (higher) pairwise connectedness (PCI) to each commodity. We not only find these PCI-based portfolios to exhibit safe-haven properties under extreme geopolitical risk, but they also outperform an equally-weighted portfolio during a period of war. Finally, low-minus-high factors constructed on pairwise connectedness have significant explanatory power for portfolio returns, indicating connectedness as an additional factor for asset pricing models.

As climate change becomes an important global issue and the global energy transformation accelerates, the complex risk transmission among carbon, energy, and stock markets is a concern. However, the majority of the existing studies are restricted to the time domain. This paper explores the risk spillovers of carbon, energy, and sectoral stock markets based on the time-frequency spillover approaches. Furthermore, wavelet coherence is employed to analyze the time-frequency dependence between markets. The findings suggest that there is a strong connectedness among carbon, energy, and sectoral stock markets, with significant differences in risk spillover at different frequencies. The carbon and energy markets are the net recipients of risk spillovers, while the industrial goods and services and financial services sectors act as the dominant risk transmitters. The crisis events have intensified the risk spillover magnitude. These results provide suggestions for risk management and asset allocation.

This study examines dynamic connectedness linkages between precious metals, manufacturing metals, oil, natural gas, and Bitcoin. The Quantile-VAR methodology is utilised to identify causal spillovers from 2015 through 2022, where results demonstrate significantly stronger pairwise connectedness at extreme quantiles, where the gold–silver and copper–oil pairs exhibit the strongest linkages. Additionally, the overall dynamic connectedness is higher at the lowest and highest quantiles, particularly reinforced during inflationary periods. Copper is identified as the strongest generator of spillovers, followed by silver, nickel, and zinc. There are mixed findings when analysing gold and aluminium, whereas oil, natural gas, and Bitcoin are identified as net receivers. This study provides insight into commodities and cryptocurrency markets’ diversifying and hedging abilities during alternative economic and financial conditions.

This paper investigates the volatility connectedness between China’s carbon pilot markets. Using Diebold and Yilmaz (2014)’s approach based on the time-varying parameter vector autoregression model with a variety of parameter sets, we obtain the average across 40 results to capture the volatility connectedness between the markets. We further use the linear and nonlinear autoregressive distributed lag models to assess the role of external uncertainties in shaping volatility connectedness. Several findings emerge: (1) Guangdong (Chongqing) is the largest net transmitter (receiver) in terms of volatility connectedness; (2) Volatility connectedness shows a declining trend, with its cycle fluctuations caused by compliance-driven trading; (3) Volatility connectedness correlates negatively with external uncertainties. Both economic policy and climate policy indices have impacts on volatility connectedness. We recommend introducing market makers to enhance market liquidity and reduce risk spreading. We also highlight the need for further research to pinpoint idiosyncratic factors that affect different markets.

The provision of public information in commodity markets is justified in part by the idea that public information generates consensus among market participants about the fundamental value of the commodity and reduces price volatility. Significant reductions in options-implied volatility following report releases have been presented as evidence of this market-calming effect. We scrutinize this finding in more detail by comparing implied volatility to realized volatility measures from intraday price data. We show that while implied volatility does indeed fall after report releases, realized volatility does not decrease. We measure realized volatility using intraday data and find evidence of much higher volatility on report days only within minutes of the report release. This pattern is consistent with changes in implied volatility being driven by the resolution of uncertainty about the information contained in the report, rather than changes in volatility expectations that may reflect the consensus among traders about forthcoming price volatility.