Journal of Futures Markets最新文献

Despite the growing importance of integrating climate risks into financial decision-making, there has been limited research on how these risks affect stock–commodity correlations and the optimal hedging performance of commodities. Using four novel climate risk measures related to the US climate policy, international summits, global warming, and natural disasters, we explore the impact of climate risks on conditional correlations between commodity futures and equities. Our results reveal that higher transition risks (US climate policy and international summits) are associated with increased correlations, while higher physical risks (natural disasters and global warming) drive correlations lower in most cases. We also find that the interaction of climate risks with macro factors can exert significant influences on the time-varying correlations. During periods of extremely high climate risk, we generally observe higher hedging costs, reduced portfolio allocations to commodities, and lower hedging effectiveness compared to periods of extremely low climate risk.

The fluctuating crude oil price affects producers, consumers, investors, policy-making, and economic stability. This paper forecasts the spot price of West Texas Intermediate (WTI) crude oil using weekly data from 1991 to 2024, considering factors from the US crude oil market, financial markets, and economic policies. We present a new secondary decomposition-reconstruction-ensemble model based on variational mode decomposition (VMD). Triangulation topology aggregation optimizer (TTAO) algorithm is first utilized to optimize the VMD and BiLSTM for sequence decomposition and prediction. The proposed model reconstructs sequences based on the permutation entropy (PE) of subsequences after primary decomposition and conducts a secondary decomposition on the high-frequency reconstructed sequence. The model predicts subsequences and reconstructed sequences using TTAO-BiLSTM and integrates results via LSTM. Prediction errors decrease sequentially across univariate BiLSTM, multivariate BiLSTM, single decomposition-ensemble, single decomposition-reconstruction-ensemble, and the proposed secondary decomposition-reconstruction-ensemble models. TTAO outperforms adaptive moment estimation (Adam) in optimizing BiLSTM within all models.

Taiwan-listed companies are required to report unaudited net operating revenues monthly. This study examines the information content of trading in the short-sale-prohibited domestic warrant market before the interim accounting disclosures by adopting an implied volatility skew (IV skew) as a proxy for informed trading. We find a significantly negative relationship between the pre-announcement abnormal IV skew of warrants and cumulative abnormal stock return around monthly-revenue disclosures. The results of the placebo test further suggest that the return predictability of the IV skew is not prevalent in normal periods, but only the pre-announcement IV skew possesses predictive power toward future stock returns. Furthermore, the predictability of warrants' IV skew on monthly-revenue announcement return is stronger when the underlying stocks are priced high and weaker when some information about unpublished revenues has been reflected by pre-announcement stock returns. These findings suggest that informed trading is the driving force behind warrant market activities before monthly-revenue reporting.

This paper aims to improve Bitcoin price direction prediction using a CNN-LSTM model that incorporates various relevant indicators, such as stock market indices, commodity indices, and interest rates. Separate models are trained for predicting price up and down direction and combined to enhance prediction accuracy. We utilize binary classification models to independently analyze the impact of different features, verified through explainable artificial intelligence techniques. Additionally, an investment strategy based on our model is proposed and compared with traditional strategies, specifically focusing on maximum drawdown relative to the S&P500 buy-and-hold strategy. Results suggest that our strategy offers potential for stable investment in Bitcoin, showcasing its value as a financial asset. This study demonstrates the role of deep learning in Bitcoin price direction prediction and investment strategy development and contributes to future research on cryptocurrency forecasting and investment approaches.

Using a text-based measure of peer opinions constructed from cryptocurrency-related social media posts, we find that peer opinions contain valuable information about the prices of cryptocurrency options. Bitcoin options exhibit a volatility smile, which becomes steeper when peer opinions become bearish. The risk-neutral skewness of Bitcoin returns implied by options prices becomes more negative in times of bearish opinions. The predictability of peer opinions for Bitcoin option prices remains robust after controlling for momentum, volatility, demand pressures, news effects, and other sentiment measures, and exhibits no evidence of reversal over time. This effect is pronounced when Bitcoin attracts high investor attention, more diverse opinions about Bitcoin are expressed on social media, and Bitcoin options are more actively traded. We find similar results for Ethereum options.

This paper investigates the informativeness of option-implied volatility and Greeks in forecasting extreme stock returns. Using a large data set of U.S. stocks and options from 1996 to 2022 and employing Light Gradient-Boosting Machine as a machine learning algorithm, we show that option characteristics, particularly implied volatility and delta, are strong predictors of extreme returns. The long–short portfolio utilizing option variables significantly outperforms a benchmark using only stock characteristics, suggesting that options provide information beyond what can be inferred from stock characteristics. Put options are revealed to be more informative than call options, and crashes are easier to predict than jumps.