Journal of Futures Markets最新文献

Cross-asset order flow provides an incremental and novel nonlinear price discovery channel. Structural vector autoregressions of synchronized intraday message data reveal distinct patterns in the comovement of order flow and its influence on returns and volatility. While cross-market order flow usually reconciles prices through small-stakes arbitrage in periods of low volatility and comovement during medium volatility associated with information arrival, it can exacerbate price dislocation from fundamental values during extraordinary volatility. While applying market-wide circuit breakers (MWCB) mitigates the extreme negative spillovers by jointly halting markets, we identify room for further harmonization during the MWCB market reopening process.

Pay-for-success contracts are social and financial innovations in social policy and capital markets, respectively. This paper argues that they exhibit option-like payoffs and implements standard option-pricing arguments in assessing the value of investing in pay-for-success contracts. Sensitivities vis-à-vis contract specifications are reflected in the valuation formula and help reach investment and social policy decisions. These sensitivities are demonstrated via a numerical application that uses parameters drawn from the Massachusetts Juvenile Justice Pay for Success Initiative, the largest pay-for-success initiative in the United States at the time of its launch.

Based on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE-HAR-DL, which combines a supervised autoencoder (AE) with the deep learning-based HAR model framework to capture essential common factor information and uses the reconstruction error of the AE component as a regularizer to enhance the generalization ability of the testing subsample. The empirical findings strongly support the effectiveness of this model in accurately forecasting crude oil futures volatility in the post-COVID-19 era, compared to the HAR, HAR-PCA, and HAR-DL models. Moreover, a robustness check also demonstrates the positive contribution of common factors to the volatility prediction of other commodity futures. Notably, we establish that these common factors act as effective regularizers, mitigating prediction losses within the HAR model in extreme risk events such as the COVID-19 pandemic and the Russia–Ukraine conflict.

We construct a stochastic volatility model considering stochastic liquidity risks when valuing variance and volatility swaps with discrete sampling. We base our model on Heston stochastic volatility, which is adopted for the modeling of stock prices when the market is perfectly liquid. Stock dynamics are further revised by discounting their prices through the employment of mean reverting market liquidity. We convert the stock dynamics under the physical measure into the one under a risk-neutral measure via measure transform, with which the analytical valuation of variance and volatility swaps is realized. By taking the limit of sampling frequency, we further consider how both swaps with continuous sampling can be priced. Numerical implementation is finally carried out, with which the capability of the constructed model in capturing the influence of the two common types of financial risks can be clear.

The last decade has seen substantial research and debates on the procyclicality of Central Counterparty (CCP) margins, but these are limited to the sensitivity of margin models to market conditions. This paper establishes the effect of feedback loop between the sensitivity of margin models and trading behavior. It utilizes unique design and high-frequency data from Indian markets to conduct an event study surrounding the revisions in the margin models to enhance their anti-procyclicality (APC) characteristics. The analysis shows that current period volatility influences position exits in the subsequent period and vice-versa. The analysis further shows that for the same price movement, position exits are lowered under stronger APC margin. The effect is weaker for participants having variation losses, where limiting losses may be an additional incentive to exit positions in addition to rise in initial margins. The findings support the argument that better APC characteristics of CCP margins can attenuate volatility.