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Risks
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Innovative Quantitative Methods for Financial Risk Management
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Innovative Quantitative Methods for Financial Risk Management

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

Deadline for manuscript submissions: 30 May 2026 | Viewed by 10873

Special Issue Editor

Prof. Dr. Pasquale Cirillo

E-Mail Website
Guest Editor
ZHAW School of Management and Law, Institute of Business Information Technology, 8400 Winterthur, Switzerland
Interests: risk modelling, in particular tail risk; quantitative risk management; financial mathematics; machine learning and data analytics; applied probability (change of measure, urn models, etc.)

Special Issue Information

Dear Colleagues,

Traditionally, financial risk management has been pivotal for safeguarding assets, ensuring stability and fostering growth in the volatile world of finance, with a tremendously positive impact on the real economy as well. The use of quantitative methods has proven essential to assess and hedge risks meaningfully. Research in stochastic calculus, the theory of risk measures, advanced simulations and numerical mathematics has provided invaluable results, which are now part of the panoply of tools we rely upon in dealing with financial risks and risks in general.

However, as the financial landscape evolves, so does the complexity of risks, necessitating newer and more reliable modeling approaches. This Special Issue seeks to bridge traditional methodologies with innovative approaches, inviting groundbreaking contributions that push the boundaries of quantitative risk management. In addition to new findings in traditional areas of quantitative risk management, we particularly welcome alternative approaches and research exploring the integration of machine learning algorithms. Decentralized finance and blockchains, as tools for risk mitigation and transparency, are also of interest for this Special Issue when studied from a quantitative point of view.

I look forward to receiving your contributions. Please feel free to reach out if you have article proposals.

Best regards,

Prof. Dr. Pasquale Cirillo
Guest Editor

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. 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

  • quantitative risk management
  • financial risk
  • financial markets
  • financial mathematics
  • decentralized finance
  • blockchain
  • artificial intelligence
  • machine learning
  • risk measures
  • stochastic processes

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Published Papers (3 papers)

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Research

24 pages, 2260 KB  
Article
Hidden Optionalities in American Options
by Noura El Hassan, Bacel Maddah and Nassim Nicholas Taleb
Risks 2026, 14(4), 89; https://doi.org/10.3390/risks14040089 - 14 Apr 2026
Viewed by 332
Abstract
We develop a practical framework for identifying and quantifying the hidden layers of risks and optionality embedded in American options by introducing stochasticity into one or more of their underlying determinants. The heuristic approach remedies the problems of conventional pricing systems, which treat [...] Read more.
We develop a practical framework for identifying and quantifying the hidden layers of risks and optionality embedded in American options by introducing stochasticity into one or more of their underlying determinants. The heuristic approach remedies the problems of conventional pricing systems, which treat some key inputs deterministically, hence systematically underestimate the flexibility and convexity inherent in early-exercise features. Full article
(This article belongs to the Special Issue Innovative Quantitative Methods for Financial Risk Management)
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24 pages, 625 KB  
Article
The Regress of Uncertainty and the Forecasting Paradox
by Nassim Nicholas Taleb and Pasquale Cirillo
Risks 2025, 13(12), 247; https://doi.org/10.3390/risks13120247 - 10 Dec 2025
Viewed by 4555
Abstract
We show that epistemic uncertainty–our iterated ignorance about our own ignorance–inevitably thickens statistical tails, even under perceived thin-tailed environments from past realizations. Any claim of precise risk carries a margin of error, and that margin itself is uncertain, in an infinite regress of [...] Read more.
We show that epistemic uncertainty–our iterated ignorance about our own ignorance–inevitably thickens statistical tails, even under perceived thin-tailed environments from past realizations. Any claim of precise risk carries a margin of error, and that margin itself is uncertain, in an infinite regress of doubt. This “errors-on-errors” mechanism rules out thin-tailed certainty: predictive laws must be heavier-tailed than their in-sample counterparts. The result is the Forecasting Paradox: the future is structurally more extreme than the past. This insight collapses branching scenarios into a single heavy-tailed forecast, with direct implications for risk management, scientific modeling, and AI safety. Full article
(This article belongs to the Special Issue Innovative Quantitative Methods for Financial Risk Management)
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20 pages, 1284 KB  
Article
Improving Credit Risk Assessment in Uncertain Times: Insights from IFRS 9
by Petr Jakubik and Saida Teleu
Risks 2025, 13(2), 38; https://doi.org/10.3390/risks13020038 - 19 Feb 2025
Cited by 8 | Viewed by 4837
Abstract
This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition [...] Read more.
This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions. Full article
(This article belongs to the Special Issue Innovative Quantitative Methods for Financial Risk Management)
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