Journal of Futures Markets最新文献

This study employs a time-varying parameter vector autoregression methodology with the Diebold and Yilmaz spillover index to scrutinize the temporal fluctuations in volatility spillovers between the Chinese coal and metal markets. The analysis is conducted from the dual perspectives of security indices and futures prices. The findings reveal a robust correlation between the coal and metal markets, with the coal market serving as a primary conduit for volatility spillover into the metal market. Furthermore, this study investigates the time-specific impacts of coal decommissioning policies, the COVID-19 pandemic, and the coal supply crisis on the coal–metal market volatility spillovers. The findings indicate that these three unique shocks significantly increase the overall risk spillover index between the coal and metal markets. Moreover, during these exceptional events, the extent or role of risk spillover in the coal–metal market undergoes varying degrees of change. On the basis of these findings, this article presents pertinent policy recommendations.

This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk-taking.

Hedging requires adequacy and timing. This paper finds that banks did not systematically ignore balance-sheet risks like Silicon Valley Bank (SVB), and instead exercised risk management by asymmetrically increasing hedging activity when security losses increase and scaling back hedging activity as security losses reverse. Banks also hedge against bank runs when risk increases due to a combination of security losses and funding risks from unsecured deposits. Findings suggest SVB's mistakes are idiosyncratic. Results suggest that nonstress test banks target balance-sheet risks when hedging, stabilizing themselves from interest rate shocks transmitted through fixed-income securities. Scrutiny of rules-based outliers like SVB is preferable to increased regulatory burden for all nonstress test banks.

A number of papers have dealt with commodity financialization finding strong evidence for its existence and its effect on commodity prices and volatility. We chose convenience yield (CY) to study the effect of commodity financialization based on the theory of storage and on the argument that CY resembles a call option. Using quarterly data in the period 1995–2018, on soybeans stocks, cash and futures prices, a dynamic Autoregressive Distributed Lag with Exogenous model is estimated to measure the effects of independent variables from both the financial and commodity markets on CY. The evidence reveals that financial markets volatility along macroeconomic global variables affect soybeans CY giving support to the existence of commodity financialization. Besides, we find a statistically significant and negative relation between volatility index and CY. Support for this evidence rests on the theory of storage, Real Option Analysis, and behavioral finance.

This paper examines the short-term market reaction of four agricultural commodities to the Russian–Ukraine war and various stages of the Black Sea Grain Initiative Agreement. Using an event study, the results show a positive abnormal return for the agricultural grain markets with the outbreak of the war and the nonrenewal of the Black Sea Grain Agreement. These two events by causing supply-side constraints, led to an increase in the price of grains. The results also show negative and statistically significant abnormal returns around the signing of the Black Sea Grain Agreement, its implementation through the departure of the first ship loaded with Ukrainian grain after the beginning of the war and the successive extensions of the agreement. These disruptions not only affect Ukraine and Russia but also have critical implications for world food security. Policy implications of our findings are provided.

Since 2013, China's futures exchanges have implemented night trading for agricultural futures to reduce the overnight risk and price jump of futures products by extending trading hours. This study uses difference-in-differences (DID) to examine the impacts of night trading on daytime price volatility in corn and corn starch futures markets. On the basis of tick-by-tick data for these futures, we find that night trading has significantly reduced daytime volatility and contributed to price volatility stability in the corresponding futures market. Moreover, we make DID estimations for separate daytime sessions and find that the reduction of the daytime volatility takes place mainly during the first trading session. Robustness and placebo tests further support our main conclusions. Our results provide valuable guidance for futures exchanges and regulators seeking to formulate night trading policies for futures and options.

Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in-sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out-of-sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long-term forecasting and does not derive from other carbon-related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate.