Journal of Futures Markets最新文献

This study investigates the pricing of complex embedded options—quality, rolling timing, and month-end timing options—in China's Treasury bond futures market. We develop an innovative pricing model that integrates the generalized error distribution (GED) and Copula functions, specifically designed to capture the unique dependencies arising from institutional coordination. The framework models “valuation deviations”—discrepancies between actual closing prices and ChinaBond valuations—using GED marginals, while the Student's t-Copula explicitly captures symmetric heavy-tailed dependence patterns induced by coordinated institutional behavior. Empirical analysis demonstrates that the model achieves superior pricing accuracy compared to traditional approaches by effectively capturing policy-shaped joint distribution characteristics. Furthermore, we introduce a policy adjustment term to account for systematic mispricing during periods of strong policy guidance, further enhancing the model's robustness. This research provides a reliable valuation benchmark tailored to constrained market structures and advances pricing theory for derivatives markets influenced by institutional and policy factors.

High-frequency quantitative trading strategies have long been of significant interest in futures market. While advanced statistical arbitrage and deep learning enhance high-frequency data processing, they diminish opportunities for traditional methods and yield less interpretable, unstable strategies. Consequently, developing stable, interpretable quantitative strategies remains a priority in futures markets. In this study, we propose a novel pair trading strategy by leveraging the mathematical concept of path signature which serves as a feature representation of time series. Specifically, the path signature is decomposed into two new indicators: the path interactivity indicator segmented signature and the directional indicator covariation of increments, which serve as double filters in strategy design. Empirical experiments using minute-level futures data show our strategy significantly outperforms traditional pair trading, delivering higher returns, lower maximum drawdown, and higher Sharpe ratio. The proposed method enhances interpretability and robustness while maintaining strong returns, demonstrating the potential of path signatures in financial trading.

This study introduces a lower bound that integrates the market's simple return risk-neutral variance ($��\; ��\mathrm{SVI}�\; �X_t^2��$) and a combination of log-return moments ($��\; ��\mathrm{VI}�\; �X_t^2��$) to predict market log returns. A distinctive feature of the model is that the ratio of $��\; ��\mathrm{VI}�\; �X_t^2��$ to $��\; ��\mathrm{SVI}�\; �X_t^2��$ captures crash risk, and the lower bound of log returns depends on the joint effect of crash risk and risk-neutral variance. Our in-sample analysis shows that crash risk exhibits significant predictive power for market returns, and its marginal effect differs markedly between crisis and noncrisis periods. In out-of-sample testing, we argue that crash risk outperforms several benchmarks in return prediction, while the joint effects of crash risk and variance risk achieves higher accuracy in forecasting crash events. We further demonstrate that our model delivers superior performance when the risk aversion is high, particularly during periods of crises.

Language models have emerged as powerful tools for measuring textual sentiment. Do sentiment indices derived from language models and textual data truly capture richer informational content? Drawing on a theoretical model, this paper illustrates how market sentiment embedded in news-texts shapes investors' risk aversion and influences their inclination to speculation. This dynamic drives more frequent trading activities, ultimately exerting an impact on the options market. Using news data from The Wall Street Journal as the corpus, we employ language models to construct a market sentiment index. Our findings validate the predictions of the theoretical model: market sentiment exerts a significant direct effect on option prices and indirectly influences them through risk aversion as a mediating variable. Furthermore, empirical evidence reveals that uncertainty significantly moderates both the direct and mediated channels linking market sentiment to option prices. Capturing market sentiment precisely, language models are indispensable for asset pricing sentiment analysis.

We investigate the impact of heterogeneous investor attention on carbon prices. Although previous research has demonstrated the influence of investor attention on asset prices, how investors attribute attention toward the carbon-market, emission, and environment issues, and their consequences on carbon prices remain unknown. Leveraging data from the European Union (EU) and Chinese (Guangdong and Hubei pilots) carbon markets, our regression results reveal that investor attention toward the carbon-market and environment issues negatively affects carbon prices in the EU Emissions Trading System, whereas attention toward the carbon-market and emission issues exhibits positive impacts in the Guangdong pilot. Interestingly, these effects are reversed in the subsequent month, implying a lasting effect of investor attention. Additional analyses suggest that the different reactions in the EU and China can be attributed to investor types and green assets diversity. Finally, we demonstrate the economic usefulness of heterogeneous investor attention in improving carbon trading investors' trading strategies.

Implied covariance asymmetry is a market-wide measure defined as the average of the absolute difference between the downside and upside pairwise co-movements of individual stocks, estimated from options data. Its risk premium is linked to improved long-term economic conditions and significantly forecasts excess market returns from 1 month to 2 years. This predictive power persists at horizons beyond 6 months after controlling for popular financial and economic predictors in in-sample analyses. It also translates into superior out-of-sample forecasts and substantial economic gains for a mean-variance investor, particularly over medium and long horizons.