Journal of Futures Markets最新文献

This study explores the impact of the Shanghai crude oil futures (SC) market on corporate trade credit financing in China. Using data on listed firms from 2012 to 2023 and a difference-in-differences model, we find the launch of SC significantly increased trade credit financing for oil-dependent firms. Mechanism evidence suggests that SC is associated with improvements in credit risk and market information environment, which are economically consistent with suppliers' trade credit decisions. Cross-sectional analysis reveals stronger effects among firms with higher investment capacity, higher production costs, and lower transparency. Economic consequences analysis suggests the effect further stimulates corporate capital investment and technological innovation. The results demonstrate how innovations in energy finance may support the real economy and contribute to the stability of China's energy industries.

This paper presents a dynamic equilibrium model of US Treasury clearing that endogenizes explicit, VaR-based initial margins, EWMA volatility updates, variation-margin feedback, cross-margining, and crowding effects within CCP competition. We cast clearing as a three-stage stochastic game: CCPs commit to fees, margin multipliers, cross-margin discounts, and volatility-decay parameters; members allocate DV01 exposures in each period subject to funding-cost feedback from margin calls; and CCPs incur tail-risk, covariance, concentration, and infrastructure penalties over time. In the symmetric competitive equilibrium, stronger cross-margining discounts, dynamically calibrated skin-in-the-game buffers, concentration surcharges, and conservative margin-and-volatility regimes all reduce the joint default probability. We benchmark against a single-CCP monopoly and demonstrate that only under extreme crowding does a well-capitalized monopoly dominate. Our comparative-statics yield a comprehensive policy toolkit—transparent margin formulas, volatility-decay standards, feedback-cap mechanisms, cross-margin interoperability, and real-time crowding metrics—to guide resilient Treasury clearing.

This study introduces a novel network-based stochastic volatility model for futures markets, integrating graph theory with multivariate stochastic differential equations to capture cross-asset volatility spillovers. The model incorporates the graph Laplacian into volatility dynamics, overcoming a central limitation of traditional frameworks, such as the Heston model, which treat each asset's volatility as independent or fixed, and therefore cannot capture the topology-dependent transmission of volatility across linked markets. Theoretical properties, including positivity, stationarity, and affine pricing, are established, enabling efficient Fourier-based option pricing. Applications show that sparse, high-spillover network structures amplify volatility clustering, with kurtosis of 6–8 and elevated risk premia, whereas dense network structures stabilize volatility; we interpret these polar cases as stylized representations of emerging and developed markets, an interpretation we validate against real futures data in Appendix A. The model improves pricing accuracy by 8%–12% relative to Heston and reduces hedging variance by 10%–15%. Environmental, social, and governance integration via carbon futures nodes shows that sparse (emerging-market) networks experience 6%–8% higher energy-futures volatility following carbon-price shocks than dense (developed-market) networks (3%–4%), supporting sustainable finance and risk management applications.

This study presents an analytical framework for the valuation of American fractional floating-strike lookback options, taking into account both path dependency and the possibility of early exercise. The proposed pricing methodology is based on the Mellin transform technique, through which we derive and solve the associated partial differential equations governing the pricing functions. A key contribution of this work is the explicit characterization of the early-exercise premium via an integral equation, in contrast to existing methods that typically rely on implicit expressions obtained through inverse transformations. This explicit form provides a more transparent understanding of the structure and behavior of the early-exercise premium. Numerical experiments are conducted to confirm the accuracy and stability of the proposed approach in approximating the true option values.

This paper presents a dynamic equilibrium model of US Treasury clearing that endogenizes explicit, VaR-based initial margins, EWMA volatility updates, variation-margin feedback, cross-margining, and crowding effects within CCP competition. We cast clearing as a three-stage stochastic game: CCPs commit to fees, margin multipliers, cross-margin discounts, and volatility-decay parameters; members allocate DV01 exposures in each period subject to funding-cost feedback from margin calls; and CCPs incur tail-risk, covariance, concentration, and infrastructure penalties over time. In the symmetric competitive equilibrium, stronger cross-margining discounts, dynamically calibrated skin-in-the-game buffers, concentration surcharges, and conservative margin-and-volatility regimes all reduce the joint default probability. We benchmark against a single-CCP monopoly and demonstrate that only under extreme crowding does a well-capitalized monopoly dominate. Our comparative-statics yield a comprehensive policy toolkit—transparent margin formulas, volatility-decay standards, feedback-cap mechanisms, cross-margin interoperability, and real-time crowding metrics—to guide resilient Treasury clearing.

This study explores the impact of the Shanghai crude oil futures (SC) market on corporate trade credit financing in China. Using data on listed firms from 2012 to 2023 and a difference-in-differences model, we find the launch of SC significantly increased trade credit financing for oil-dependent firms. Mechanism evidence suggests that SC is associated with improvements in credit risk and market information environment, which are economically consistent with suppliers' trade credit decisions. Cross-sectional analysis reveals stronger effects among firms with higher investment capacity, higher production costs, and lower transparency. Economic consequences analysis suggests the effect further stimulates corporate capital investment and technological innovation. The results demonstrate how innovations in energy finance may support the real economy and contribute to the stability of China's energy industries.

We examine day-of-the-week effects in China's SSE 50 ETF options market and uncover three main patterns. Returns are weakest in midweek and strongest on Friday; Friday and Monday returns are not significantly different; and the trough occurs on Wednesday for calls but on Tuesday for puts. These results remain after controlling for standard risk factors, volatility clustering, and COVID-19-related structural breaks. While CVIX captures the broad Friday premium, it does not explain the internal asymmetry across option types. We show that trading value is the key liquidity channel behind the anomaly: call trading reflects speculative demand, whereas put trading is more closely tied to hedging demand, and these motives follow different weekly rhythms. VAR results further indicate that high Friday trading value creates an immediate price effect and a reversal on Monday, helping explain the missing weekend effect.

This study parameterizes extreme comovement between options market ambiguity (OMA) and implied volatility using the Symmetrized Joe–Clayton copula model and a recursive estimation approach. The empirical results demonstrate that acceleration in extreme comovement (AEC) contains incremental information about future market uncertainty beyond that captured by OMA and implied volatility. Both in-sample and out-of-sample predictive tests confirm that AEC anticipates impending market declines. Incorporation of this signal into investment strategies yields significantly better performance than the traditional buy-and-hold strategy and the strategies signaled by OMA and implied volatility, providing extra evidence that AEC is a leading indicator of market slumps.