Journal of Futures Markets最新文献

{"title":"Does Cutting Carbon Emissions Reduce Tail Risk Spillovers? A Quantile LSTM-KAN-CoVaR Approach","authors":"Ziwei Wang,&nbsp;Yibo Liu,&nbsp;Peng Lu","doi":"10.1002/fut.70063","DOIUrl":"https://doi.org/10.1002/fut.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper evaluates the association between carbon emissions and tail-risk spillovers in European futures markets. We propose an innovative quantile LSTM-KAN model to capture the time-varying, nonlinear dynamics of tail-risk spillover networks. Using data from 29 EU futures markets, we find that tail-risk spillovers increase significantly during key events, including the 2016 Brexit referendum and the 2020 COVID-19 pandemic. Oil, natural gas, and EU allowance futures play central roles as recipients of tail risk, whereas bond and low-carbon futures exert tail-risk spillovers on other markets. In addition, we analyze the impact of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 \u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math> emissions on tail-risk spillovers. Higher <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 \u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math> emissions significantly increase the tail-risk spillovers received by EU allowance futures and low-carbon equity futures. In low-volatility periods, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 \u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math> emissions increase the spillovers transmitted from oil and gas sector futures to other markets. In high-volatility periods, they intensify the tail-risk spillovers received by crude oil futures.</p>\u0000 </div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"381-412"},"PeriodicalIF":2.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Journal of Futures Markets: Volume 45, Number 12, December 2025","authors":"","doi":"10.1002/fut.22528","DOIUrl":"https://doi.org/10.1002/fut.22528","url":null,"abstract":"","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"45 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fut.22528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Grain Futures Prices Through Uncertainty Indices and Mixed-Frequency Fusion: An Interpretable Deep Learning Framework","authors":"Weixin Sun,&nbsp;Minghao Li,&nbsp;Li Zhang,&nbsp;Yong Wang","doi":"10.1002/fut.70060","DOIUrl":"https://doi.org/10.1002/fut.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>This study innovatively develops an interpretable mixed-frequency feature interaction deep learning network (IMF-FIDNet) to improve high-frequency grain futures price prediction via effective multi-frequency data integration, with a focus on ensuring robustness amid market uncertainty. By refining advanced mixed-frequency processing methods, proposing a new deep learning model, and integrating multiple modules, IMF-FIDNet enhances feature interaction modeling between low-frequency uncertainty indicators and high-frequency grain prices. Experiments show it outperforms traditional models in accuracy and robustness, and effectively supports investment decisions; further, its interpretability quantifies uncertainty indices' contributions, confirming macro-indicators' role in high-frequency price forecasting.</p>\u0000 </div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"353-380"},"PeriodicalIF":2.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Debt With Intensity-Based Models","authors":"João Miguel Reis,&nbsp;José Carlos Dias","doi":"10.1002/fut.70057","DOIUrl":"https://doi.org/10.1002/fut.70057","url":null,"abstract":"<p>This article proposes a dynamic debt model where the face value of debt can change. In particular, our dynamic debt setting allows debt changes ruled by intensity processes that are linked to the firm value through the correlation between the stochastic processes. Analytical solutions are obtained, and we extend the proposed dynamic debt model to the case of subordinated debt. While empirical behaviors are emulated, the impacts of dynamic debt over the credit spreads are explored. In this model, the possibility of debt increases magnifies credit spreads and the reverse occurs for the possibility of debt decreases.</p>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"334-352"},"PeriodicalIF":2.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fut.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speed of Adjustment in Digital Assets in a Decentralized Financial World","authors":"Jeremy Eng-Tuck Cheah,&nbsp;Thong Dao,&nbsp;Hung Do,&nbsp;Tapas Mishra","doi":"10.1002/fut.70055","DOIUrl":"https://doi.org/10.1002/fut.70055","url":null,"abstract":"<p>This paper investigates the stability and co-movement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to long-run equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a time-varying series of SA, allowing examination of its evolution and cross-asset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct risk-return trade-offs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forward-looking indicator of stability in digital asset markets.</p>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"320-333"},"PeriodicalIF":2.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fut.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extreme Comovement and Risk Spillovers in Crude Oil Prices: A Tale of Two Events","authors":"Haoyu Shi,&nbsp;Yuansheng Wang,&nbsp;Xu Zheng","doi":"10.1002/fut.70059","DOIUrl":"https://doi.org/10.1002/fut.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>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-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math> 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.</p></div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"283-319"},"PeriodicalIF":2.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models and Futures Price Factors in China","authors":"Yuhan Cheng,&nbsp;Yanchu Liu,&nbsp;Heyang Zhou","doi":"10.1002/fut.70061","DOIUrl":"https://doi.org/10.1002/fut.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"262-282"},"PeriodicalIF":2.3,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of Price Discovery in Option Markets: An Interpretable Machine Learning Perspective","authors":"Jufang Liang,&nbsp;Dan Yang,&nbsp;Qian Han","doi":"10.1002/fut.70052","DOIUrl":"https://doi.org/10.1002/fut.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p></div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 2","pages":"237-261"},"PeriodicalIF":2.3,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Chaos of Climate Ambitions: Climate Policy Uncertainty and the Volatility Risk in Commodity Markets","authors":"Shuhui Zhu,&nbsp;Fenglin Wu,&nbsp;Yufan Wan,&nbsp;Yanshuang Li","doi":"10.1002/fut.70056","DOIUrl":"https://doi.org/10.1002/fut.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 1","pages":"197-220"},"PeriodicalIF":2.3,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equilibrium Pricing of Bitcoin Options With Stochastic Volatility, Jumps, and Liquidity Risk","authors":"Jingrui Li","doi":"10.1002/fut.70058","DOIUrl":"https://doi.org/10.1002/fut.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":15863,"journal":{"name":"Journal of Futures Markets","volume":"46 1","pages":"221-234"},"PeriodicalIF":2.3,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}