Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.0
1.5
Journals
Risks
Special Issues
Computational Methods and Models in the Financial Risk Management Process
share announcement

Computational Methods and Models in the Financial Risk Management Process

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3449

Special Issue Editor

Dr. Marina Resta

E-Mail Website
Guest Editor
Department of Economics, University of Genoa, 16126 Genoa, Italy
Interests: machine learning; portfolio optimization; Bayesian networks; network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity of the financial context poses challenging issues that are particularly well suited to be managed using machine learning algorithms.

This Special Issue focuses on state-of-the-art applications in financial markets, with particular attention to all aspects of the risk management process. We therefore welcome and encourage high-quality contributions focused on these areas, including (but not limited to) the following:

  • Asset pricing;
  • Big data analytics;
  • Financial data mining;
  • Commodity markets;
  • Term structure models;
  • Trading systems;
  • Hedging strategies;
  • Actuarial mathematics;
  • Deep learning and artificial neural networks;
  • Fuzzy sets, rough sets, and granular computing;
  • Hybrid systems;
  • Support vector machines.

Dr. Marina Resta
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 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. 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

  • machine learning
  • risk management
  • data analytics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 753 KB  
Article
Estimating Policy Impact in a Difference-in-Differences Hazard Model: A Simulation Study
by David A. Hsieh
Risks 2025, 13(10), 200; https://doi.org/10.3390/risks13100200 - 13 Oct 2025
Abstract
This article estimates the impact of a policy change on an event probability in a difference-in-differences hazard model using four estimators. We examine the error distributions of the estimators via a simulation experiment with twelve different scenarios. In four simulation scenarios when all [...] Read more.
This article estimates the impact of a policy change on an event probability in a difference-in-differences hazard model using four estimators. We examine the error distributions of the estimators via a simulation experiment with twelve different scenarios. In four simulation scenarios when all relevant variables are known, three of the four methods yield accurate estimates of the policy impact. In eight simulation scenarios when an individual characteristic is unobservable to the researcher, only one method (nonparametric maximum likelihood) achieves accurate estimates of the policy change. The other three methods (standard Cox, three-step Cox, and linear probability) are severely biased. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
►▼ Show Figures

Figure 1

19 pages, 395 KB  
Article
Robust Tail Risk Estimation in Cryptocurrency Markets: Addressing GARCH Misspecification with Block Bootstrapping
by Christos Christodoulou-Volos
Risks 2025, 13(9), 166; https://doi.org/10.3390/risks13090166 - 29 Aug 2025
Viewed by 666
Abstract
This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in [...] Read more.
This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare the performance of classical and block bootstrap procedures in residual risk. Diagnostic tests indicate that standardized residuals are dependent, contrary to the independent and identically distributed (i.i.d.) assumption of conventional FHS. Comparing the block and ordinary bootstrapping approaches, we find that block bootstrap produces wider, more conservative confidence intervals, particularly in extreme tails (e.g., 0.1% and 99.9% percentiles). The findings suggest that block bootstrapping can be employed as a correction instrument in risk modeling where the standard volatility filters do not work. The article highlights the necessity to account for remaining dependencies and offers practical recommendations for more robust tail risk estimation during volatile markets. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
12 pages, 2274 KB  
Article
A New Approach on Country Risk Monitoring
by Christos E. Kountzakis and Christos Floros
Risks 2025, 13(5), 80; https://doi.org/10.3390/risks13050080 - 22 Apr 2025
Viewed by 718
Abstract
Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product [...] Read more.
Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in terms of a spot value. Another index is the spot value of the percentage of the annual interest rate payments of the state to the owners of sovereign debt. These indexes provide an inefficient evaluation of the national debt and moreover they are sensitive in their calculative aspect. Hence, we propose another index of national debt evaluation, which is more realistic, since public debt is a part of the balance sheet of the state itself. Moreover, this index may be translated into growth variables of the national economy. Since Gross National Product relies on consumption of the Economy, more consumption implies an ’illusion’ about sovereign debt. On the other hand, this index has limits to its credibility because it depends on the size of the annual investments. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
18 pages, 586 KB  
Article
A Bivariate Model for Correlated and Mixed Outcomes: A Case Study on the Simultaneous Prediction of Credit Risk and Profitability of Peer-to-Peer (P2P) Loans
by Yan Wang, Xuelei Sherry Ni, Huan Ni and Sanad Biswas
Risks 2025, 13(2), 33; https://doi.org/10.3390/risks13020033 - 12 Feb 2025
Viewed by 1197
Abstract
In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a [...] Read more.
In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a single scoring approach may bias the loan evaluation conclusion. In this paper, we propose a bivariate model based on the integration of two scoring approaches. We first formulate the loan evaluation task as a multi-target problem, in which loan_status (i.e., default or not default) is used as the discrete outcome for the credit risk measure while the annualized rate of return (ARR) is used as the continuous outcome for the profitability measure. Then to solve the multi-target problem, we design a novel loss function based on the assumption that the discrete outcome follows a Bernoulli distribution, and the continuous outcome is normally distributed conditional on the discrete output. The effectiveness of the proposed model is examined using the real-world P2P data from the Lending Club. Results indicate that our approach outperforms the sole scoring methods by identifying loans with higher profit and lower default risk. Therefore, the proposed method can serve as an alternative for loan evaluation. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop