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Risks
Special Issues
AI-Driven Financial Econometrics and Risk Management
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AI-Driven Financial Econometrics and Risk Management

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5357

Editors

Dr. Difang Huang

E-Mail Website
Guest Editor
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Interests: risk management; behavioral finance; experimental economics; financial intermediation; human-algorithm interaction; decision science
Prof. Dr. Jue Wang

E-Mail Website
Guest Editor
1. AMSS Center for Forecasting Science, Chinese Academy of Sciences, Beijing, China
2. University of Chinese Academy of Sciences, Beijing, China
Interests: economic forecasting; machine learning; finance risk management; decision analysis; intelligent computing; operational optimization

Special Issue Information

Dear Colleagues,

Rapid advances in artificial intelligence, machine learning, and data science are reshaping the landscape of financial econometrics and modern risk management. Financial markets now generate unprecedented volumes of granular, high‑frequency, and unstructured data, while institutions face increasingly complex risks arising from geopolitical uncertainty, climate transitions, digital assets, algorithmic trading, and systemic interconnectedness. Traditional econometric models, though foundational, are often insufficient for fully capturing nonlinear dynamics, real‑time dependencies, and structural breaks that characterize today’s financial systems.

This Special Issue aims to bring together cutting‑edge theoretical, methodological, and empirical research at the intersection of AI technologies and financial risk analysis. We welcome contributions that develop novel econometric models enhanced by machine learning, explore data‑driven approaches to risk measurement, or apply AI tools to credit risk, market risk, systemic risk, insurance analytics, and portfolio optimization. Studies integrating the interpretability, robustness, fairness, and regulatory implications of AI-driven modeling are particularly encouraged. Both methodological innovations and high‑impact practical applications are welcome. By bridging financial econometrics with modern AI methodologies, this Special Issue seeks to advance the next generation of risk modeling tools and promote informed decision‑making in increasingly complex financial environments.

Dr. Difang Huang
Prof. Dr. Jue Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • financial econometrics
  • machine learning
  • artificial intelligence
  • risk management
  • systemic risk
  • volatility modeling
  • credit and market risk
  • high frequency data
  • model uncertainty and robustness
  • data analytics in finance

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Published Papers (2 papers)

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Research

24 pages, 1601 KB  
Article
Deep Reinforcement Learning for Cryptocurrency Portfolio Management: A Free-Energy Framework with Geometry-Based Transaction Costs and Efficiency Bounds
by Ntebogang Dinah Moroke
Risks 2026, 14(5), 103; https://doi.org/10.3390/risks14050103 - 2 May 2026
Cited by 3 | Viewed by 1487
Abstract
This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded [...] Read more.
This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration. Full article
(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)
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27 pages, 1070 KB  
Article
Human-AI Synergy in Statistical Arbitrage: Enhancing Robustness Across Volatile Financial Markets
by Binxu Lei
Risks 2026, 14(3), 63; https://doi.org/10.3390/risks14030063 - 12 Mar 2026
Viewed by 3274
Abstract
This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting [...] Read more.
This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting persistent challenges related to tail-risk exposure, regime instability, limited interpretability, and regulatory and governance constraints in practical applications. Building on this literature synthesis, the paper develops a conceptual AI-led, human-in-the-loop statistical arbitrage framework that integrates ML-generated signal modeling with structured human oversight—encompassing risk calibration, discretionary intervention, and interpretability review. This framework resonates with human-AI collaboration systems across other financial domains, collectively supporting the proposition that collaborative systems show potential to enhance resilience compared to purely AI-driven alternatives under specific market stress scenarios. It is positioned as a governance-oriented synthesis that qualitatively extends existing human-in-the-loop concepts by structurally embedding adaptive oversight within the statistical arbitrage decision architecture. Full article
(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)
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