Journal of Futures Markets最新文献

This study examines the impact of USDA reports on Chinese soybean complex futures using intraday price and volume data from January 2011 to April 2021. The results show that the strongest price and volume reactions occur immediately after the market opens. On average, price reactions dissipate within a few minutes, while increased trading volume can last up to an hour. Significant announcement effects are observed for all major USDA reports, but the World Agricultural Supply and Demand Estimates (WASDE) report has minimal impact when not released alongside other USDA reports. During the trade war period, USDA reports had reduced impact on soybean no. 1 and soybean oil futures but remained influential on soybean no. 2 and soybean meal futures. Moreover, there is no evidence that the informational value of the WASDE report on Chinese soybean complex futures declined following the introduction of the China Agricultural Supply and Demand Estimates (CASDE) report.

We propose a novel option pricing model that explicitly incorporates volatility-of-volatility (VOV) dynamics and its associated risk premium. Our framework integrates realized variance and realized quarticity to capture latent VOV dynamics, addressing key challenges in cryptocurrency option pricing. Using Fourier inversion methods, we derive a closed-form pricing formula for European-style options. Empirical analysis with high-frequency cryptocurrency option data shows that our model improves pricing accuracy, reducing implied volatility errors by 8.55% compared to benchmark models. The model outperforms benchmarks across all moneyness levels, remains robust for both short- and long-maturity contracts, and maintains accuracy under high volatility. This study contributes to the literature by introducing a tractable and empirically validated approach to cryptocurrency option pricing through explicit VOV modeling.

This paper uses high-frequency data of agricultural futures trading prices in the Chicago Mercantile Exchange, totaling about 1.2 million pieces of data, to conduct a study of climate risk and realized volatility based on machine learning methods. We carry out the following studies: first, we find that the nonlinear machine learning method has better applicability to the extended model of heterogeneous autoregressive model with the realized volatility model. Second, by analyzing the importance of the factors in the forecasting model, we find that compared with the global climate risk factor and most of the macroeconomic fundamentals, the climate risk factor of the main agricultural production region constructed in this paper contributes more to the forecasting of realized volatility. Third, based on the Shapley Additive exPlanations value analysis of the predictors, this paper examines the directional impact of climate risk factors and macroeconomic fundamentals on agricultural commodity volatility.

Using trade information over the 1993–2023 period from essentially the universe of futures exchanges spanning Asia, Europe, Latin America, and North America, we identify and examine periods of clustering or correlated occurrence of unexpected spikes in futures contract turnover, which we refer to as “turnover waves.” We investigate their frequency, distributional characteristics, and determinants along with the extent of their common occurrence both within and across various asset classes and geographic regions. We find that turnover waves are most frequently observed and are of the longest duration in Asia and in the energy asset class. We further find evidence of the common occurrence of waves across asset classes, notably within Asia, Europe, and Latin America. Across geographic regions, we find that concurrence is highest in the interest rate asset class. Finally, the initiation of waves is associated with increases in market policy uncertainty and asset class-level volatility.

Over decades, accurate computation of the Black–Scholes implied volatility (IV) is crucial yet still challenging for quantitative finance researchers and practitioners. In this paper, we propose a novel and robust algorithm to compute model-free bounds of IV based on the Dirac delta family method. Numerical experiments demonstrate that these bounds are tighter than representative ones in the literature. Further combined with the Householder method, our bounds can be applied universally to all parameter regimes with higher accuracy than the alternative methods in the literature. Our method is also extended to accurately calculate IV sensitivities and the equivalent local volatility function when the underlying asset follows a stochastic volatility model.

This paper examines the factor momentum in commodity futures markets. Based on the US and UK data from 1985 to 2022, we first show that a commodity factor's past returns positively predict its future returns. This predictability is at its strongest over the 1-month horizon, and could be explained by mispricing. The factor momentum suggests mean-variance inefficient commodity factors and negatively impacts the efficiency of pricing models. We then construct the time-series efficient factors, which exhibit higher Sharpe ratios and improve the performance of pricing models. These findings are robust across international commodity futures markets, but the transaction costs erode the economic gains of factor momentum and efficient factor strategies due to high portfolio turnover. Overall, our results point to the potential to time commodity factors and highlight the importance of conditional asset pricing in commodity futures markets.