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Computation
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Quantitative Finance and Risk Management Research: 2nd Edition
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Quantitative Finance and Risk Management Research: 2nd Edition

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Social Science".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 4375

Special Issue Editors

Dr. Athanasios G. Tsagkanos

E-Mail Website
Guest Editor
Department of Business Administration, University of Patras, Patras, Greece
Interests: financial management; quantitative methods; applied economics
Special Issues, Collections and Topics in MDPI journals
Dr. Vasilios I. Sogiakas

E-Mail Website
Guest Editor
Department of Regional Development, Ionian University, 49100 Corfu, Greece
Interests: quantitative finance; risk management; asset pricing models; derivatives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the Journal of Computation (ISSN 2079-3197) is devoted to quantitative finance and risk management research, reflecting the crucial necessity to incorporate advanced quantitative and computational techniques in finance and risk management.

Our Special Issue welcomes papers dealing with original and innovative contributions in the following areas:

  • Asset pricing;
  • EMH and adaptive market hypothesis;
  • Financial markets;
  • Financial econometrics;
  • Risk management;
  • Financial regulation;
  • Artificial intelligence machine learning in financial trading;
  • Volatility modelling and risk management;
  • Nonlinear and stochastic optimization in finance;
  • Behavior finance;
  • Corporate finance;
  • Derivative pricing and hedging;
  • Portfolio management;
  • Financial market regulation;
  • Spillover effects;
  • Price discovery and informational efficiency;
  • Asset pricing and macroeconomic fundamentals;
  • Financial market structure and microstructure;
  • Mutual funds and hedge funds;
  • Big data analysis.

Dr. Athanasios G. Tsagkanos
Dr. Vasilios I. Sogiakas
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. Computation 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 finance
  • risk management research
  • credit risk
  • financial crisis

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Related Special Issue

Published Papers (3 papers)

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Research

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9 pages, 915 KiB  
Article
Tree-Based Methods of Volatility Prediction for the S&P 500 Index
by Marin Lolic
Computation 2025, 13(4), 84; https://doi.org/10.3390/computation13040084 - 24 Mar 2025
Viewed by 363
Abstract
Predicting asset return volatility is one of the central problems in quantitative finance. These predictions are used for portfolio construction, calculation of value at risk (VaR), and pricing of derivatives such as options. Classical methods of volatility prediction utilize historical returns data and [...] Read more.
Predicting asset return volatility is one of the central problems in quantitative finance. These predictions are used for portfolio construction, calculation of value at risk (VaR), and pricing of derivatives such as options. Classical methods of volatility prediction utilize historical returns data and include the exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroskedasticity (GARCH). These approaches have shown significantly higher rates of predictive accuracy than corresponding methods of return forecasting, but they still have vast room for improvement. In this paper, we propose and test several methods of volatility forecasting on the S&P 500 Index using tree ensembles from machine learning, namely random forest and gradient boosting. We show that these methods generally outperform the classical approaches across a variety of metrics on out-of-sample data. Finally, we use the unique properties of tree-based ensembles to assess what data can be particularly useful in predicting asset return volatility. Full article
(This article belongs to the Special Issue Quantitative Finance and Risk Management Research: 2nd Edition)
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22 pages, 3100 KiB  
Article
Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches
by Sonia Benghiat and Salim Lahmiri
Computation 2025, 13(3), 76; https://doi.org/10.3390/computation13030076 - 14 Mar 2025
Viewed by 489
Abstract
As climate change has become of eminent importance in the last two decades, so has interest in industry-wide carbon emissions and policies promoting a low-carbon economy. Investors and policymakers could improve their decision-making by producing accurate forecasts of relevant green finance market indices: [...] Read more.
As climate change has become of eminent importance in the last two decades, so has interest in industry-wide carbon emissions and policies promoting a low-carbon economy. Investors and policymakers could improve their decision-making by producing accurate forecasts of relevant green finance market indices: carbon efficiency, clean energy, and sustainability. The purpose of this paper is to compare the performance of single-step univariate forecasts produced by a set of selected statistical and regression-tree-based predictive models, using large datasets of over 2500 daily records of green market indices gathered in a ten-year timespan. The statistical models include simple exponential smoothing, Holt’s method, the ETS version of the exponential model, linear regression, weighted moving average, and autoregressive moving average (ARMA). In addition, the decision tree-based machine learning (ML) methods include the standard regression trees and two ensemble methods, namely the random forests and extreme gradient boosting (XGBoost). The forecasting results show that (i) exponential smoothing models achieve the best performance, and (ii) ensemble methods, namely XGBoost and random forests, perform better than the standard regression trees. The findings of this study will be valuable to both policymakers and investors. Policymakers can leverage these predictive models to design balanced policy interventions that support environmentally sustainable businesses while fostering continued economic growth. In parallel, investors and traders will benefit from an ease of adaptability to rapid market changes thanks to the computationally cost-effective model attributes found in this study to generate profits. Full article
(This article belongs to the Special Issue Quantitative Finance and Risk Management Research: 2nd Edition)
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Review

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18 pages, 2101 KiB  
Review
Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
by Diandra Chika Fransisca, Sukono, Diah Chaerani and Nurfadhlina Abdul Halim
Computation 2024, 12(8), 166; https://doi.org/10.3390/computation12080166 - 18 Aug 2024
Cited by 1 | Viewed by 3089
Abstract
Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims [...] Read more.
Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time. Full article
(This article belongs to the Special Issue Quantitative Finance and Risk Management Research: 2nd Edition)
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