Journal of Commodity Markets最新文献

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.

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.