Applied Mathematics in Quantitative Finance and Risk Management: Theory, Evidence, and Applications

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 237

Special Issue Editor


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Guest Editor
School of Economics, Sichuan University, Chengdu, China
Interests: uncertainty; bond markets; sustainable finance; risk management; asset pricing; supply chain risk

Special Issue Information

Dear Colleagues,

Quantitative finance has become a core pillar of modern financial research and practice. It covers asset pricing, portfolio allocation, derivatives markets, trading mechanisms, and market microstructure, and—driven by advances in data science and computing technologies—continues to expand into broader settings for inference, forecasting, and decision making. Meanwhile, the financial system has become increasingly complex and interconnected. The interactions among leverage and liquidity cycles, technological innovation, climate transition, and geopolitical uncertainty make the formation of risk and return more dynamic and nonlinear, and raise higher demands for robust risk measurement, pricing, and management. Against this backdrop, there is a pressing need for rigorous quantitative research grounded in financial mathematics, statistics/econometrics, and computational methods to bridge theoretical innovation and real-world applications.

This Special Issue aims to advance frontier research at the intersection of financial mathematics, quantitative finance, and risk management, promoting deeper connections between methodological developments and practical applications. We welcome high-quality original research articles and authoritative survey papers. We particularly encourage submissions that propose new ideas and tools in theoretical modeling, empirical identification, and computational methods, while maintaining clear economic intuition and practical relevance. We expect this Special Issue to bring together the latest advances from both academia and industry, providing new evidence and insights to improve our understanding of market mechanisms, enhance investment and pricing models, and strengthen institutional risk management and the resilience of the financial system.

This Special Issue will accept high-quality papers containing original research results and survey articles of exceptional merit in the following fields (including but not limited to):

  • Financial mathematics and quantitative research methods;
  • Portfolio management and asset pricing;
  • Ambiguity and robust decision making;
  • Climate risk and sustainable investing;
  • Quantitative trading and market structure;
  • Derivatives markets and hedging strategies;
  • AI- and data-driven finance;
  • Digital finance and crypto-asset markets;
  • Supply-chain shocks and financial markets;
  • Liquidity risk and credit risk;
  • Geopolitical risk and global financial markets;
  • Cybersecurity and technology-related risks;
  • Systemic risk, contagion, and financial stability;
  • Stress testing, scenario analysis, and tail risk.

Dr. Jingzhou Yan
Guest Editor

Manuscript Submission Information

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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

  • quantitative finance
  • market microstructure
  • ambiguity aversion
  • credit risk
  • climate risk
  • sustainable investing
  • crypto-asset markets
  • geopolitical risk
  • cyber risk
  • supply chain shocks

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Published Papers (1 paper)

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Research

16 pages, 566 KB  
Article
Drift Estimation for Stochastic Partial Differential Equation Driven by Fractional Brownian Motion
by Hongsheng Qi, Lili Gao and Litan Yan
Mathematics 2026, 14(8), 1318; https://doi.org/10.3390/math14081318 - 15 Apr 2026
Abstract
This paper presents a systematic asymptotic analysis of the least squares estimator (LSE) for the drift parameter in a fractional stochastic heat equation driven by fractional Brownian motion. Fractional Brownian motion, capable of capturing stylized features in financial markets such as long memory, [...] Read more.
This paper presents a systematic asymptotic analysis of the least squares estimator (LSE) for the drift parameter in a fractional stochastic heat equation driven by fractional Brownian motion. Fractional Brownian motion, capable of capturing stylized features in financial markets such as long memory, has become an important modeling tool in financial econometrics and risk management. Based on continuous-time observations of the Fourier coefficients of the solution, we first establish the strong consistency and asymptotic normality of the estimator. We then construct an alternative estimator based on the LSE and analyze its asymptotic behavior. This study provides new asymptotic inference tools for stochastic systems with long-memory properties and extends the theoretical framework for parameter estimation in fractional stochastic partial differential equations. Full article
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