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Financial Markets: Risk Forecasting, Dynamic Models and Data Analysis
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Financial Markets: Risk Forecasting, Dynamic Models and Data Analysis

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2838

Special Issue Editors

Prof. Dr. Kuan-Min Wang

E-Mail Website
Guest Editor
Department of Finance, College of Business and Management, Overseas Chinese University, Taichung 40721, Taiwan
Interests: international finance; financial markets; econometrics for finance
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan-Ming Lee

E-Mail Website
Guest Editor
Department of Finance, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
Interests: international economics; econometrics of finance; stock market and economic growth issues

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore innovative approaches and recent advancements in understanding and managing risks within modern financial markets. With the increasing complexity and interconnectedness of global markets, traditional models often fall short in capturing dynamic behaviors and systemic risks. This Special Issue welcomes original research and comprehensive reviews focusing on the development and application of quantitative models, forecasting techniques, and data-driven strategies for risk assessment and financial decision-making. Topics of interest include, but are not limited to, stochastic modeling, machine learning in finance, volatility forecasting, systemic risk analysis, and market microstructure. We particularly encourage interdisciplinary contributions that integrate finance, statistics, and data science to offer novel insights into financial risk and market dynamics.

Prof. Dr. Kuan-Min Wang
Dr. Yuan-Ming Lee
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Financial Studies 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 risk forecasting
  • dynamic financial models
  • volatility analysis
  • systemic risk
  • quantitative finance
  • machine learning in finance
  • high-frequency data
  • time series analysis
  • financial data analytics
  • AI in finance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

34 pages, 5123 KB  
Article
Comparative Analysis of Tail Risk in Emerging and Developed Equity Markets: An Extreme Value Theory Perspective
by Sthembiso Dlamini and Sandile Charles Shongwe
Int. J. Financial Stud. 2026, 14(1), 11; https://doi.org/10.3390/ijfs14010011 - 6 Jan 2026
Viewed by 738
Abstract
This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study [...] Read more.
This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study aims to recommend the most suitable probability distribution between the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) and to assess the associated tail risk using the value-at-risk and expected shortfall. To address volatility clustering, four generalised autoregressive conditional heteroscedasticity (GARCH) models (standard GARCH, exponential GARCH, threshold-GARCH and APARCH (asymmetric power ARCH)) are first applied to returns before implementing the peaks-over-threshold and block maxima methods on standardised residuals. For each equity index, the probability models were ranked based on goodness-of-fit and accuracy using a combination of graphical and numerical methods as well as the comparison of empirical and theoretical risk measures. Beyond its technical contributions, this study has broader implications for building sustainable and resilient financial systems. The results indicate that, for the GEVD, the maxima and minima returns of block size 21 yield the best fit for all indices. For GPD, Hill’s plot is the preferred threshold selection method across all indices due to higher exceedances. A final comparison between GEVD and GPD is conducted to estimate tail risk for each index, and it is observed that GPD consistently outperforms GEVD regardless of market classification. Full article
(This article belongs to the Special Issue Financial Markets: Risk Forecasting, Dynamic Models and Data Analysis)
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35 pages, 4264 KB  
Article
Smart Tangency Portfolio: Deep Reinforcement Learning for Dynamic Rebalancing and Risk–Return Trade-Off
by Jiayang Yu and Kuo-Chu Chang
Int. J. Financial Stud. 2025, 13(4), 227; https://doi.org/10.3390/ijfs13040227 - 2 Dec 2025
Viewed by 1576
Abstract
This paper proposes a dynamic portfolio allocation framework that integrates deep reinforcement learning (DRL) with classical portfolio optimization to enhance rebalancing strategies and risk–return management. Within a unified reinforcement-learning environment for portfolio reallocation, we train actor–critic agents (Proximal Policy Optimization (PPO) and Advantage [...] Read more.
This paper proposes a dynamic portfolio allocation framework that integrates deep reinforcement learning (DRL) with classical portfolio optimization to enhance rebalancing strategies and risk–return management. Within a unified reinforcement-learning environment for portfolio reallocation, we train actor–critic agents (Proximal Policy Optimization (PPO) and Advantage Actor–Critic (A2C)). These agents learn to select both the risk-aversion level—positioning the portfolio along the efficient frontier defined by expected return and a chosen risk measure (variance, Semivariance, or CVaR)—and the rebalancing horizon. An ensemble procedure, which selects the most effective agent–utility combination based on the Sharpe ratio, provides additional robustness. Unlike approaches that directly estimate portfolio weights, our framework retains the optimization structure while delegating the choice of risk level and rebalancing interval to the AI agent, thereby improving stability and incorporating a market-timing component. Empirical analysis on daily data for 12 U.S. sector ETFs (2003–2023) and 28 Dow Jones Industrial Average components (2005–2023) demonstrates that DRL-guided strategies consistently outperform static tangency portfolios and market benchmarks in annualized return, volatility, and Sharpe ratio. These findings underscore the potential of DRL-driven rebalancing for adaptive portfolio management. Full article
(This article belongs to the Special Issue Financial Markets: Risk Forecasting, Dynamic Models and Data Analysis)
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