Journal of Futures Markets最新文献

This paper studies the pricing of vulnerable options with systematic and idiosyncratic factors incorporated. Variance gamma processes are employed to model price jumps caused by the arrivals of systematic and idiosyncratic relevant information. A parsimonious pricing measure is developed and Laplace transforms of option price and Greek letters are given. Numerical results are obtained by a two-sided Euler inversion method in an efficient and accuracy way. It shows that in contrast to idiosyncratic factors, the effect of systematic factors on vulnerable options is strongly affected by the skewness and leptokurtosis features of systematic variance gamma processes.

This study explores VIX forecasting by proposing a novel model to characterize the volatility of volatility based on high-frequency VIX. Specifically, the decomposed jumps, the short- and long-term volatility of VIX realized volatility obtained through wavelet analysis are considered by integrating the HAR-DJI-GARCH with GARCH-MIDAS model. Empirical results show superior performance over competing models, with enhanced predictive accuracy under four non-parametric jumps. The model's effectiveness is further validated by adjusting prediction windows, wavelet levels, examining VIX term structure, varying the significance level of jump test, and through the assessment of its economic significance.

Motivated by the ongoing debate on whether optimal or naive diversification should be preferred when distributing wealth across investment alternatives, this article investigates how the choice of covariance estimator affects mean-variance portfolio selection. In an environment tailored to ideal tradability, we construct optimal commodity futures portfolios based on 12 promising covariance matrix estimators and compare their out-of-sample investment performance to a simple, equally weighted investment strategy by means of bootstrap testing. We find that neither the naive allocation approach nor the advanced covariance estimators can outperform the traditional sample covariance matrix. Because this empirical result is robust to modifications of the research design (including alternative investigation periods, data frequencies, estimation window sizes, holding period lengths, weight constraint specifications, and transaction cost levels), it opposes the recurrent suggestion of the equity-oriented literature that the sample covariance matrix should not be used for the purpose of portfolio optimization.

This study explores whether frequent trading is profitable to investors in an emerging stock index futures market. Our analyses, based on long-term data from 2010 to 2023, indicate that the effect of trading frequency differs across investor types and market conditions. Only some domestic institutions gain additional profits from more frequent trading, and such a tendency is apparent when the futures price falls and when the futures market volatility is low. Foreign investors experience losses as they trade more when the market is bearish and are frequently net long. The performance of domestic individuals does not depend on their trading frequency in general; however, they lose more from trading when the market is bearish and when the market is less volatile.

We offer analytic formulae for valuing finite maturity profit caps and floors that are contingent on continuous flows without the need for subtracting the risk-neutral expectation of the forward starting perpetual solution from the corresponding perpetual solution. The related price caps, floors, and collars are easily obtained from any analytic representation of profit caps and floors using some arbitrage-free relations. Finally, we offer two novel methods for calculating the optimal triggers of investment projects in the presence of price floors and collars regimes in a way that is much simpler than the ones currently used.

With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures in the United States and China from July 9, 2014, to December 31, 2022. We obtain several findings. First, CBOT corn, soybean, and wheat are identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Second, sudden events or increased economic uncertainty can enlarge the overall risk spillovers. Third, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillovers. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk-spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree.