Journal of Futures Markets最新文献

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.

This paper examines the co-jump transmission in 20 commodity futures returns in the United States using co-jump network models. Specifically, it reveals co-jumping behavior in both static and time-varying settings, considering the overall commodity markets and various commodity groups separately, which helps us understand the dynamic changes in co-jump dependencies at the overall and sector levels. The main results reveal that co-jump heterogeneity exists among commodities but is generally more apparent within each commodity group, and co-jumps vary over time. Gold exerts the strongest influence, with many commodity futures being influenced by the jumps behavior in gold returns. During the COVID-19 outbreak and the Russia–Ukraine war, the energy group ranks highest in terms of co-jump network centrality. Our empirical analysis highlights the portfolio performance and risk reduction, and an additional examination shows that centrality information from the co-jump network contains a highly and statistically forecasting power for US stock market volatility.

This article studies the pricing of VIX futures and options by directly modeling the dynamics of VIX, based on realized semivariances computed from high-frequency data of VIX. We derive the closed-form pricing formula for both the VIX futures and options. The empirical results show that the new model provides superior pricing performance compared with the model based on conventional unsigned realized variance and the classic Heston-Nandi GARCH model, both in sample and out of sample. Our study confirms that the decomposition of realized variance into upside and downside components helps to improve the pricing performance for VIX futures and options.