Journal of Futures Markets最新文献

We investigate the dynamic price discovery in the regular bitcoin (BTC) and microbitcoin (MBT) futures at the Chicago Mercantile Exchange. The only difference between the two bitcoin futures is the contract size, with MBT representing 1/50th of BTC. In contrast to recent findings in the literature, we find that BTC dominates MBT futures in price discovery, which can be attributed to the relative liquidity and investor structure in the BTC and MBT futures. In addition, crypto hacking activities can affect price discovery in bitcoin futures as we find higher hack stolen funds reduce (enhance) the price discovery in BTC (MBT) futures. These findings provide practical implications for bitcoin investors and regulators.

We study the “real world” calibration of a partially specified stochastic volatility model, where the analytic expressions of the asset price drift rate and of the stochastic variance drift are not specified. The model is calibrated matching the observed asset log returns and the priors assigned by the investor. No option price data are used in the calibration. The priors chosen for the asset price drift rate and for the stochastic variance drift are those suggested by the Heston model. For this reason, the model presented can be considered as an “enhanced” Heston model. The calibration problem is formulated as a stochastic optimal control problem and solved using the dynamic programming principle. The model presented and the Heston model are calibrated using synthetic and Standard & Poor 500 (S&P500) data. The calibrated models are used to produce 6, 12, and 24 months in the future synthetic and S&P500 forecasts.

Brazil's significant commodity production is internationally recognized, yet the absence of a mature futures market exposes it to price risks and international shocks. This study explores the dynamic connectedness between commodity futures and the Brazilian cash markets, using a time-varying parameter vector autoregressive model. We also assess COVID-19's impact on this connectedness. We find a significant influence of oil prices on Brazilian ethanol prices, and particularly emphasize the Heating Oil spillover effect on ethanol in the post-COVID-19 era. We also note the ascension of Brazilian soybean spot markets' international significance since 2017, amplifying their role in global grain price discovery. Finally, by computing hedge ratios and effectiveness between commodity futures contracts and Brazilian spot prices, our study reveals soybean cash price as the most effective hedge. These insights deepen comprehension of connectedness within Brazilian commodity markets, thereby guiding investors and policymakers in strategic energy policy decisions.

In this paper, a novel measurement of overconfidence over the market is developed based on the size of ambiguity (the confidence of investors in information). The proposed measure of market-wide overconfidence is consistent with the predictions motivated by prior literature. It has a significant negative association with the next-month market excess return. Associations between the overconfidence measure and riskier portfolio returns behave stronger and last longer, implying a risk-taking proclivity of overconfident investors.

Given the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well-known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID-19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out-of-sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV-related portfolios.

This study investigates the time-varying connectedness between subsectoral clean-energy stocks and fossil fuel energy commodities (crude oil, natural gas, and coal) over the period of December 2013–January 2023 employing the Diebold and Yilmaz approach and the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model. According to the findings, oil transmits the highest volatility spillover shocks to biofuels, and the least to the fuel cell industry. Both natural gas and coal transmit the highest volatility spillover shocks to energy storage, and the least to geothermal and green information technology, respectively. The study also finds strong and time-varying volatility connectedness among clean-energy assets and fossil fuels, significantly affected by global extreme events, such as the COVID-19 pandemic and the Russia–Ukraine conflict. Additionally, the study provides time-varying and mean optimal hedge ratios with optimal portfolio weights for investors. The empirical results are robust, and important portfolio and policy implications based on empirical findings are provided.

This study employs the quantile-on-quantile method, casualty-in-quantiles method, and rolling window regression to investigate the impact of international crude oil future prices on the stock prices of both traditional and new energy sectors in China. The empirical results reveal that the effect of oil future prices on the energy stock market in China varies across quantiles and is easily affected by extreme events. Specifically, the impact of oil future prices on the new energy stock market is significant and volatile, while it is less volatile and displays a negative correlation with the traditional energy market. Furthermore, a concentrated positive correlation is observed in the middle and low quantile stages of the energy stock market. A significant Granger causality exists between oil future prices and the energy stock market in different quantiles. Those findings can provide useful guidance for policymakers, investors, and consumers.