Journal of Futures Markets最新文献

This study measures the asymptotic dependence between spot and futures losses and investigates its impact on hedging effectiveness using data from stock, currency, and commodity markets. The findings reveal that stock futures contracts show strong asymptotic dependence, while currency futures have weak asymptotic dependence and most commodity futures lack asymptotic dependence with the underlying spots. Further, stock futures have the highest hedging effectiveness, while commodity and currency futures show low hedging effectiveness for downside risk. Results also suggest that asymptotic dependence is critical for minimum-variance hedging. Asymptotic dependence increases with the hedging horizon, leading to a better hedging performance of the futures. It also appears that the hedging strategies sensitive to asymptotic dependence perform better than the competing models. The results for the entire period and the subsample periods offer similar conclusions.

Existing studies on commodity market risk spillovers recognize the pivotal role of geopolitical risk (GPR), but scarcely address how it drives tail risk spillover networks. This study adopts the Tail-Event driven NETwork methodology to explore high-dimensional Conditional Value at Risk (CoVaR) spillovers within energy and other strategic commodity markets. Our findings indicate that (1) In both lower and upper tail networks, metal and food commodities primarily act as net risk transmitters, whereas energy commodities are mainly net risk receivers. Additionally, these roles undergo short-term reversals during periods of heightened market uncertainty. (2) There exists an asymmetrical pattern of CoVaR co-movements in these commodity markets. The total connectedness (TC) in both the upper and lower tails demonstrates distinct responses to various extreme events. GPR tends to weaken the lower tail TC and strengthen the upper tail. (3) Incorporating GPR substantially improves the effectiveness of Minimum Connectedness Portfolio (MCoP) for these strategic commodities.

This study investigates the cross-border risk spillovers between the US soybean futures market and Chinese soybean-related futures markets. We first confirm the existence of strong tail dependence between US soybean futures and four Chinese soybean-related futures by conducting a novel quantile-Granger causality test. Second, tests under MVMQ-CAViaR further provide evidence of risk spillovers from CBOT soybean futures to the DCE No.1 soybean, No.2 soybean, soybean meal, and soybean oil futures in value-at-risk at different quantiles. Lastly, results from the quantile impulse-response function reveal the time-varying and asymmetric property of risk spillover effects. In addition, we compare the results from two subsample periods and identify different risk spillover effects across markets at different quantiles that may contribute to the investors' decision-making under extreme market conditions.

This study investigates the impact of climate policy uncertainty (CPU) on energy and metal commodity futures markets by employing quantile regression, which accounts for various (bearish, normal, and bullish) markets. Our results reveal that the impact of CPU shocks is heterogeneous and market condition-specific. Particularly, CPU exerts a significantly negative effect on all commodities, except natural gas, in a bearish market. Under a normal market, the impact of CPU on energy returns varies across commodities whereas for a bullish market, the CPU effect is mixed. The results also reveal natural gas to be a good hedge instrument for climate policy risk. We further conducted channel analysis using the theory of storage and hedging pressure hypothesis. The key finding reveals inventory level as the transmission channel of climate policy risk. Our findings have implications for the inventory management strategies of producers and suggest that regulators should consider market-based policies in their decarbonization efforts.

This paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

We apply a Time-Varying Parameter Vector Auto Regressive (TVP-VAR) connectedness approach on global assets to investigate time-varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID-19 and the Russia–Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID-19 and the Russia–Ukraine war and that cumulative returns of portfolios are higher during COVID-19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts.

To measure intraday volatility in international crude oil futures markets, we use the functional conditional variance to measure volatility and focus on volatility relationship analysis and prediction. This paper analyzes the simultaneous and predictive volatility relationships in crude oil futures markets. For covariate markets with significantly positive predictive volatility relationships, this paper empirically extends the fGARCH-X model so that it can introduce the volatility characteristics of covariate markets. The empirical application shows that using the fGARCH-X model can generally improve the predictive effects of functional volatility in crude oil futures markets. The robustness results indicate that the improvement in volatility prediction is significant. This study is beneficial for the stable development of international crude oil futures markets and is valuable for investors' investment decision-making.

We propose a model-guided option-implied ambiguity measure to capture uncertainty regarding the return distribution of a risky asset underlying a set of options, and investigate its predictive power on the asset return. A representative investor's ambiguous beliefs or prior distributions on the underlying asset returns are extracted from the market prices of options, the expected volatility of which is then defined as the option-implied ambiguity and is calculated in line with Brenner and Izhakian. Simulated paths of the calibrated models are utilized to compute all pertinent probability characteristics from a forward-looking perspective. The empirical results with SSE 50 ETF options indicate that the proposed option-implied ambiguity has strong predictive power for future returns of SSE 50 ETF. Out-of-sample tests also verify the significant predictive ability of the option-implied ambiguity to the equity returns.