Journal of Futures Markets最新文献

In this paper, we investigate the tail dependence and risk spillovers between International Energy Exchange (INE) crude oil futures and global crude oil benchmarks (WTI and Brent), as well as its underlying spot markets, by integrating the ARMA–GARCH-skewed-$��\; ��t��$ model with the Copula-CoVaR framework. Using high-frequency data with synchronized trading windows, we find consistently strong tail dependence across all sessions, supporting the role of INE as an emerging Asian benchmark. Risk spillovers are asymmetric, with downside risk dominating. INE functions as an information sender during daytime trading, characterized by notable volatility transmission, whereas nighttime spillover is more stable and symmetric. Moreover, INE is more sensitive to extreme events such as COVID-19 pandemic and the Russia–Ukraine conflict during its domestic trading hours. Our findings offer practical implications for market regulation, emphasizing the need to improve nighttime liquidity and enhance systemic risk monitoring under time-varying uncertainty.

We leverage the capacity of large language models such as Generative Pre-trained Transformer (GPT) in constructing factor models for Chinese futures markets. We successfully obtained 40 factors to design single-factor and multi-factor portfolios through long-short and long-only strategies, conducting backtests during the in-sample and out-of-sample periods. Comprehensive empirical analysis reveals that GPT-generated factors deliver remarkable Sharpe ratios and annualized returns while maintaining acceptable maximum drawdowns. Notably, the GPT-based factor models also achieve significant alphas over the IPCA benchmark. Moreover, these factors demonstrate significant performance across extensive robustness tests, particularly excelling after the cutoff date of GPT's training data.

This paper empirically demonstrates that the SSE 50 ETF option market has the informational advantage compared to the underlying market, and evaluates the relative importance of option characteristics in price discovery using interpretable machine learning methods. Estimating the Information Leadership Share using 1-s resolution price data as a measure of price discovery indicates that price discovery occurs in the SSE 50 ETF option market more, less in the underlying market. The feature importance analysis reveals that trading cost is the primary factor contributing to the informational advantage of option markets, followed by leverage, market maker risk, and speculation, while liquidity and open interest have less impact. Extensive robustness tests are also conducted to assess the stability of the feature importance.

Using a novel news-based climate policy uncertainty (GCPU) index, we empirically investigate its impact on commodity market volatility risk. Our findings reveal the implicit cost of policy chaos, showing that GCPU significantly amplifies commodity futures volatility, especially following major climate policy events. Channel analyses indicate that GCPU affects volatility through mechanisms such as inventory scarcity, speculative activity, and shifts in investor attention. Furthermore, employing the network connectedness framework, we trace the dynamic risk spillovers of GCPU. We find that while systemic spillovers moderate over time, pronounced heterogeneity remains across sectors and contracts: agriculture and metals display persistently higher exposure, whereas the muted aggregate effect for energy is due to offsetting dynamics at the futures level. Taken together, these results reconcile regression evidence with spillover analysis and offer important implications for risk management.

We introduce an equilibrium model for Bitcoin options that endogenizes stochastic volatility (SV), correlated jumps, and liquidity risk. Investors with constant relative risk aversion utility over consumption and real-money balances face an exponential penalty for illiquidity, yielding a pricing kernel with jump premia linked to a mean-reverting liquidity index. Under the risk-neutral measure, we obtain closed-form adjustments to drifts and Poisson intensities, leading to a semianalytic fourfold sum of Black–Scholes prices at scenario-specific variances. We derive an affine characteristic function for the logarithm of the real price and implement a fast Fourier-transform inversion for efficient valuation. Comparative statics show that higher liquidity aversion steepens short-term skews and raises deep out-of-the-money premia. Two-stage calibration to Bitcoin option surfaces and high-frequency liquidity measures demonstrates that the model captures observed volatility smiles and term structures more effectively than classical SV and jump-diffusion models.

This study proposes a deep learning framework, MIDAS–TGCN, to model the volatility spillover networks in futures markets and examines their impact on risk premiums. Traditional approaches typically rely on variance decomposition methods or VAR models, facing limitations in capturing high-dimensional nonlinear dependencies and integrating macroeconomic factors. Our framework combines mixed-data sampling (MIDAS) with Temporal Graph Convolutional Networks (TGCNs) to process high-frequency market data and low-frequency macroeconomic indicators through dual pathways, generating distinct short-term (market-driven) and long-term (macro-driven) volatility networks. The volatility network spillover effects derived through our proposed modeling framework not only capture structural responses to systemic events but also demonstrate enhanced robustness with reduced sensitivity to tail events compared with conventional approaches. Importantly, network spillover dynamics constructed via MIDAS–TGCN methodology exhibit significant explanatory power in decoding term structure risk premia in futures markets, which can be seen that the volatility network spillovers have asset pricing effects. This empirical validation aligns with emerging literature on high-frequency risk transmission while extending the analytical frontier through temporal graph convolutional architectures.

To delve deeper into the risk spillover in the commodity and stock markets, this study proposes an interconnected multilayer extreme risk spillover network using the TVP-VAR-DY model, block aggregation technique, and network measures. Specifically, we focus on the interconnectedness among commodity futures, commodity spot markets (iron ore, coal, and crude oil), and stock markets in the US and China. The results confirm that: (i) commodity futures markets are the largest transmitters to other markets at the aggregate level, (ii) energy markets are viewed as a bridge for extreme cross-market risk spillovers in commodity and stock markets, and (iii) risk contagion between markets is stronger in extreme cases compared to the volatility-based one, while unexpected events, including trade wars and COVID-19, exacerbate the risk transmission strength. Our study provides a new perspective on extreme risk contagion in the commodity and stock markets and contributes to risk management in financial markets.

This article examines VIX option pricing using a direct modeling approach that emphasizes the dynamics of the VIX by incorporating identified jumps. Using high-frequency intraday VIX data, we isolate jumps from continuous movements, and integrate realized jump variation and bipower variation, respectively, into the dynamics of conditional variance and jump intensity. We subsequently derive a closed-form pricing formula for VIX options and conduct a comprehensive evaluation of the model's pricing accuracy. Empirical results indicate that the jump-based model consistently outperforms models based on conventional realized variance and the classic Heston-Nandi GARCH framework, both in-sample and out-of-sample. Our findings demonstrate that decomposing VIX variation into jump and continuous components significantly improves VIX option pricing performance.

This paper explores information dissemination under macroeconomic conditions and market performance across various commodity markets. It includes 11 markets, including tangible and virtual commodities, of which the tangible markets are distinguished into futures markets of four different commodity sectors (energy commodities, agricultural products, livestock, and precious metals), while the virtual markets are represented by cryptocurrency. The results show that when the economy is in a period of high volatility, that is, when commodity markets tend to experience a sharp rise (bubble) or a sharp fall (crash), the information dissemination level between various commodities increases. This paper also uses Social Network Analysis to analyze information dissemination channels under different circumstances and finds that regardless of economic conditions, the commodity market that dominates the dissemination function differs whether prices are rising or falling. Changes in the effect of Bitcoin's information dissemination could act as a signal of a turning point in these markets.