Journal of Futures Markets最新文献

We examine the role of risk management in the context of commodity factor premia. Stopping losses in individual commodities effectively improves the average returns of long-short commodity premia through persistent reduction in the frequency and severity of drawdowns. The magnitude of improvement is related to the quality of the signal, commodity return volatility, and autocorrelations, as well as transaction costs. The efficacy of a stop-loss strategy can be enhanced by dynamically calibrating loss thresholds in accordance with realized volatility, and it performs best in high conviction weighting schemes. Overall, we highlight the pivotal role of risk management beyond volatility targeting and risk-parity in harnessing commodity risk premia.

Since the S&P 100 Index underlies both American (OEX) and European (XEO) options, the value of the early exercise premium of American options can be directly observed. We find that the mid-quote of an XEO option can be higher than that of an otherwise identical OEX option, and liquidity can explain this overpricing phenomenon of European options. Our results show that illiquid options are significantly overpriced in the S&P 100 Index options market. This finding indicates that an illiquid option can be overvalued with a higher market offer price, which is the requirement of market makers for compensation for providing liquidity.

Traditional options pricing relies on underlying asset volatility and contract properties. However, asset volatility is affected by the “lead–lag effects,” known as the “momentum spillover effect.” To address this, we propose a proxy measuring correlated options' influence based on maturity date. Findings indicate that 1-day-lagged proxy indicators positively impact option returns. Furthermore, to capture the dynamic effects of correlated options, we introduce a deep graph neural network-based model (GNN-MS). Empirical results on Shanghai Stock Exchange 50 exchange-traded fund options reveal GNN-MS significantly outperforms classics, enhancing root-mean-square error by at least 8.81%. This study provides novel insights into option pricing considering momentum spillover effects.

This study develops a regime-switching Riemannian-geometric covariance framework for futures hedging. The covariance of conventional regime-switching BEKK (Baba, Engle, Kraft and Kroner) (RSBEKK) evolves on flat spaces that exclude a prior the possibility of inherent geometric covariance dynamic. A Riemannian-geometric regime-switching BEKK (RG-RSBEKK) is proposed such that the covariance moves along a trajectory on Riemannian manifolds. RG-RSBEKK is applied to China Securities Index 300 futures for hedging the stock sector exposures. Empirical results reveal that specifying covariance dynamic on curved spaces enhances hedging effectiveness based on the model confidence set with loss measures of variance, utility, value-at-risk, and Frobenius distance.

This study investigates whether and how air pollution can affect the crude oil futures market. The results indicate that, although air pollution does not significantly affect oil returns, it does have a significant negative impact on volatility and liquidity in the crude oil futures market in the presence of pit trading. Furthermore, air pollution near the New York Mercantile Exchange (NYMEX) negatively affects both volatility and liquidity, whereas the effect magnitude diminishes as the distance from the NYMEX increases. In general, this study reveals that air pollution affects investors in the crude oil futures market directly through its physical or cognitive impact.

We examine options trading in leveraged Exchange-Traded Funds (ETFs) and their impact on the performance of the underlying funds. Using implied volatility innovations in call and put options, we demonstrate that option signals from leveraged ETFs are robust predictors of the underlying ETFs' performance. While both levered and unlevered option signals forecast ETF returns, the levered signal is more pronounced in both magnitude and relevance. This predictivity power primarily stems from inverse leveraged ETFs and during economic downturns. Furthermore, we use the leveraged ETF option signals to develop a trading strategy that produces an average abnormal performance of 1.13% per month.

For a general stochastic volatility framework with correlation between the spot price and the instantaneous volatility, an analytical approximation for single barrier options with continuous monitoring is given. The approximation is expressed only in terms of market observable implied volatilities and prices. As such the approximation is independent of the specific form and number of parameters of the skew-generating stochastic volatility model.

The Secured Overnight Funding Rate (SOFR) has become the risk-free rate benchmark in US dollars, thus term structure models should reflect key features exhibited by SOFR and forward rates implied by SOFR futures. We construct a multifactor, stochastic volatility term structure model which incorporates these features. Calibrating to options on SOFR futures, we achieve a reasonable fit to the market across maturities and strikes in a single model. This also provides novel insights into SOFR term rate behavior (and implied volatilities) within their accrual periods, and a model mechanism by which interest rate mean reversion arises from monetary policy.

Market microstructure invariance (MMI) stipulates that trading costs of financial assets are driven by the volume and volatility of bets, but these variables are inherently difficult to identify. With futures transactions data, we estimate bet volume as the trading volume of brokerage firms that trade on behalf of their clients and bet volatility as the trade-related component of futures volatility. We find that the futures bid–ask spread lines up with bet volume and bet volatility as predicted by MMI, and that intermediation by high-frequency traders does not interfere with the MMI relation.