Journal of Commodity Markets最新文献

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.

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.

This study investigates linkages and connectedness among geopolitical risks, systemic stress, and commodity futures (energy, precious metals, industrial metals, and agricultural commodities). We combine the 22-day rolling ex-post higher-order moments with a novel Quantile-VAR extended joint connectedness framework. Our findings highlight the significant impacts of geopolitical risks and systemic stress on equicorrelations and spillovers of the higher-order moment risks. The total spillovers of higher-order moments at the extreme upper (0.95) and lower (0.05) quantiles are notably higher than those at the median quantile. Geopolitical risks convey substantial net spillovers of higher-order moment risks to commodity futures, particularly in extreme market status. In normal market conditions, systemic financial stress also transmits notable spillovers to commodity futures. Moreover, the dynamic connectedness indices evolve across time and quantiles.

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.

This paper revisits the dynamics of pricing relationships between commodity and equity markets in a sample of commodity-exporting economies between 2000–2023. We confirm the correlation between these asset prices increases around episodes of financial distress. Prior research attributes this increase to the effects of contagion initiated by commodity price shocks. However, we find that after controlling for the effect of time varying risk aversion and investor sentiment, there is no evidence that the documented correlation increase originates from commodity market shocks. Indeed, we are unable to reject the hypothesis of no contagion. We maintain that controlling for the influence of time varying risk aversion and investor sentiment, together with other factors which potentially cause common variation across price movements in commodity and equity markets, is essential to accurately capturing the relationship between asset prices in these markets.

This research aims to investigate the propagation of extreme downside risk, commonly referred to as tail risk, within commodity markets using an innovative CAViaR-based connectivity model. We also evaluate the influence of various crises, including the global financial crisis, the shale oil revolution, the COVID-19 pandemic, and the Russia-Ukraine conflict, on the dynamic relationships among seventeen different commodity markets. Our findings reveal a diverse pattern of interconnections among these markets during distinct crisis periods. Surprisingly, we observe that the nature of these interconnections is remarkably similar during geopolitical and health crises. Notably, the spillover effects between different commodity categories are more pronounced during the COVID-19 pandemic and the Russia-Ukraine conflict than during the global financial crisis and the shale oil revolution. However, it is important to note that the total risk spillovers are more substantial during the global financial crisis. Furthermore, our analysis delves into the unique characteristics of each market, revealing that precious metals can function as a safe haven for both energy and industrial metals during times of economic turbulence.

Mixed and outdated natural gas price convergence results have caused confusion among analysts and strongly call for a comprehensive revision of the topic. The issue has been exacerbated with the recent rampant increase in LNG trade and the emergence of new gas spot trading hubs. Filling the gap, this study conducts growth convergence testing and clustering analysis on a panel comprised of four established gas price benchmarks and two emerging ones that expand up to the pre-Covid-19 period. The most significant finding is that no gas price convergence can be found outside Europe. This is despite the existence of episodes of partial convergence that are identified in the literature, and replicated and explained here. Importantly, the results strongly reject the postulate that increased LNG flows serve as a price-levelling arbitrage mechanism. Overall, these findings inform analysts, researchers and policymakers that they should be wary about taking convergence claims for granted.

In the climate-sensitive agricultural sector, product prices are particularly susceptible to climate risks. In this study, we constructed three novel climate risk perception indices—a climate policy uncertainty index, a climate physical risk index, and a climate concern index—using natural language processing and text mining to investigate their differential effects on bulk agricultural prices. The findings indicate that the responses of agricultural product prices to climate risk perceptions are more pronounced in the short term than in the long term. Moreover, the impacts of climate-related concerns and physical risks have stronger impacts than climate policy uncertainty. The dynamic analysis results also indicate that climate-related events have a great impact on investors and financial markets. Overall, the findings suggest that climate risk perceptions have become a significant factor in agricultural product price changes, which has important implications for policy regulators and market investors.