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Mathematics
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Mathematical and Computational Methods in Financial and Risk Forecasting
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Mathematical and Computational Methods in Financial and Risk Forecasting

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 756

Special Issue Editors

Prof. Dr. Kuang-Hsun Shih

E-Mail Website
Guest Editor
Department of Business and Economic Law, CTBC Business School, Tainan 709, Taiwan
Interests: intelligent finance; banking operation and management; risk analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shu-Ping Lin

E-Mail Website
Guest Editor
Department of Banking and Finance, CTBC Business School, Tainan 709, Taiwan
Interests: research methods; data science and AI; service market and management; FINTECH; performance evaluation
Prof. Dr. Yi-Hsien Wang

E-Mail Website
Guest Editor
Doctorate Program in Intelligent Banking and Finance, CTBC Business School, Tainan 709, Taiwan
Interests: financial econometrics; investment management; decision science; risk analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, which focuses on the integration of advanced mathematical and computational techniques in financial innovation and risk management. The rapid evolution of blockchain technology and the cryptocurrency market has transformed financial asset management, introducing new complexities and challenges. Understanding the risk characteristics and opportunities arising from the convergence of traditional and digital finance is an essential area of academic and practical inquiries. This Special Issue addresses these developments and their implications for portfolio management, risk assessment, and regulatory compliance.

Moreover, it aims to advance our knowledge in applying mathematical and computational methods to financial and risk management challenges. It aligns with the journal’s focus on combining theoretical and practical insights to solve real-world problems. Topics of interest include innovative models and algorithms for portfolio optimization, derivative pricing, and financial econometrics. Contributions emphasizing interdisciplinary approaches that integrate finance, mathematics, computer science, and data analytics are particularly encouraged.

In this Special Issue, we welcome original research articles and reviews that explore, but are not limited to, the following themes:

  • Derivative pricing and trading strategies;
  • Portfolio optimization and ETF analysis;
  • Risk analytics and forecasting in finance;
  • Artificial intelligence and machine learning applications;
  • Computational methods in financial innovation;
  • Digital asset management.

We look forward to receiving your contributions.

Prof. Dr. Kuang-Hsun Shih
Prof. Dr. Shu-Ping Lin
Prof. Dr. Yi-Hsien 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • machine learning
  • cryptocurrency
  • financial risk forecasting
  • derivative pricing
  • portfolio optimization
  • computational finance
  • artificial intelligence
  • intelligent banking and finance
  • digital asset management

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Published Papers (1 paper)

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Research

28 pages, 5343 KiB  
Article
Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-Variance
by Yuping Song, Yuetong Zhang, Po Ning, Jiayi Peng, Chunyu Kao and Liang Hao
Mathematics 2025, 13(8), 1260; https://doi.org/10.3390/math13081260 - 11 Apr 2025
Viewed by 374
Abstract
Realized downward semi-variance (RDS) has been realized as a key indicator to measure the downside risk of asset prices, and the accurate prediction of RDS can effectively guide traders’ investment behavior and avoid the impact of market fluctuations caused by price declines. In [...] Read more.
Realized downward semi-variance (RDS) has been realized as a key indicator to measure the downside risk of asset prices, and the accurate prediction of RDS can effectively guide traders’ investment behavior and avoid the impact of market fluctuations caused by price declines. In this paper, the RDS rolling prediction performance of the traditional econometric model, machine learning model, and deep learning model is discussed in combination with various relevant influencing factors, and the sensitivity analysis is further carried out with the rolling window length, prediction length, and a variety of evaluation methods. In addition, due to the characteristics of RDS, such as aggregation and jumping, this paper further discusses the robustness of the model under the impact of external events, the influence of emotional factors on the prediction accuracy of the model, and the results and analysis of the hybrid model. The empirical results show that (1) when the rolling window is set to 20, the overall prediction effect of the model in this paper is the best. Taking the Transformer model under SSE as an example, compared with the prediction results under the rolling window length of 5, 10, and 30, the RMSE improvement ratio reaches 24.69%, 15.90%, and 43.60%, respectively. (2) The multivariable Transformer model shows a better forecasting effect. Compared with traditional econometric, machine learning, and deep learning models, the average increase percentage of RMSE, MAE, MAPE, SMAPE, MBE, and SD indicators is 52.23%, 20.03%, 62.33%, 60.33%, 37.57%, and 18.70%, respectively. (3) In multi-step prediction scenarios, the DM test statistic of the Transformer model is significantly positive, and the prediction accuracy of the Transformer model remains stable as the number of prediction steps increases. (4) Under the impact of external events of COVID-19, the Transformer model has stability, and the addition of emotional factors can effectively improve the prediction accuracy. In addition, the model’s prediction performance and generalization ability can be further improved by stacked prediction models. An in-depth study of RDS forecasting is of great value to capture the characteristics of downside risks, enrich the financial risk measurement system, and better evaluate potential losses. Full article
(This article belongs to the Special Issue Mathematical and Computational Methods in Financial and Risk Forecasting)
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