Computational Methods for Risk Management in Economics and Finance
By Marina Resta
Contents
About the Special Issue Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Preface to ”Computational Methods for Risk Management in Economics and Finance” . . . . ix
Peter Martey Addo, Dominique Guegan and Bertrand Hassani
Credit Risk Analysis Using Machine and Deep Learning Models †
Reprinted from: Risks 2018, 6, 38, doi:10.3390/risks6020038 . . . . . . . . . . . . . . . . . . . . . . 1
Andreas M¨ uhlbacher and Thomas Guhr
Credit Risk Meets Random Matrices: Coping withNon-Stationary Asset Correlations
Reprinted from: Risks 2018, 6, 42, doi:10.3390/risks6020042 . . . . . . . . . . . . . . . . . . . . . . 21
Douw Gerbrand Breed, Tanja Verster, Willem D. Schutte and Naeem Siddiqi
Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio
Reprinted from: Risks 2019, 7, 123, doi:10.3390/risks7040123 . . . . . . . . . . . . . . . . . . . . . 47
Stanislaus Maier-Paape and Qiji Jim Zhu
A General Framework for Portfolio Theory—Part I: Theory and Various Models
Reprinted from: Risks 2018, 6, 53, doi:10.3390/risks6020053 . . . . . . . . . . . . . . . . . . . . . . 63
Stanislaus Maier-Paape and Qiji Jim Zhu
A General Framework for Portfolio Theory. Part II: Drawdown Risk Measures
Reprinted from: Risks 2018, 6, 76, doi:10.3390/risks6030076 . . . . . . . . . . . . . . . . . . . . . . 99
Marco Neffelli
Target Matrix Estimators in Risk-Based Portfolios
Reprinted from: Risks 2018, 6, 125, doi:10.3390/risks6040125 . . . . . . . . . . . . . . . . . . . . . 131
Takaaki Koike and Marius Hofert
Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations
Reprinted from: Risks 2020, 8, 6, doi:10.3390/risks8010006 . . . . . . . . . . . . . . . . . . . . . . . 151
Wided Khiari and Salim Ben Sassi
On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the _Covar Measures
Reprinted from: Risks 2019, 7, 122, doi:10.3390/risks7040122 . . . . . . . . . . . . . . . . . . . . . 185
Rasika Yatigammana, Shelton Peiris, Richard Gerlach and David Edmund Allen
Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants
Reprinted from: Risks 2018, 6, 52, doi:10.3390/risks6020052 . . . . . . . . . . . . . . . . . . . . . . 201
Preface to ”Computational Methods for Risk Management in Economics and Finance”
The aim of the Special Issue is to highlight the relevance of computational methods in economic and financial modeling as an alternative to conventional analytical and numerical paradigms, bringing together both theoretical and application-oriented contributions. We received a large number of submissions, and ultimately published the nine high quality contributions that compose this Issue. The papers address a variety of important issues, mainly focusing on credit risk and risk measures.
The research stream of credit risk is debated in three papers. The paper of Peter Martey Addo, Dominique Guegan, Bertrand Hassani (Addot et al., 2018) addresses questions related to the intensive use of deep learning systems in enterprises to predict loan default probability. Andreas M‥ uhlbacher and Thomas Guhr (M‥ uhlbacher and Guhr, 2018) examine the issue of modeling credit risk for correlated assets. Employing a new interpretation of the Wishart model for random correlation matrices, they model non-stationary effects and show how their approach can grasp different market situations.
Douw Gerbrand Breed, Tanja Verster, Willem D. Schutte, and Naeem Siddiqi (Breed et al., 2019) propose a method based on weighted logistic regression to model loss given default for IFRS 9 purposes.
The Special Issue also has a number of contributions dealing with portfolio theory and risk measures.
The first contribution of Stanislaus Maier-Paape and Qiji Jim Zhu (Maier-Paape and Zhu, 2018a) debates the mathematical representation of the trade-off between utility and risk, thus introducing a general framework for portfolio theory that allows a unification of several important existing portfolio theories. The second contribution (Maier-Paape and Zhu, 2018b) provides several examples of convex risk measures necessary for the application of the above-discussed general framework, with a special focus on risk measures related to the “current” drawdown of the portfolio equity. The focus is maintained on risk-based portfolios in the paper of Marco Neffelli (Neffelli, 2018), who compares various estimators for the sample covariance matrix. Using extensive Monte Carlo simulations, the author offers a comparative study of these estimators, testing their ability to reproduce the true portfolio weights.
Takaaki Koike and Marius Hofert (Koike and Hofert, 2020) use Markov chain Monte Carlo (MCMC) methods to estimate systemic risk measures and risk allocations. The efficiency of the MCMC estimators is demonstrated in a series of numerical experiments. The remaining contributions address practical issues: Wided Khiari and Salim Ben Sassi (Khiari and Ben Sassi, 2019) assess the systemic risk of Tunisian listed banks by way of CoVaR; finally, Rasika Yatigammana, Shelton Peiris, Richard Gerlach, and David Edmund Allen (Yatigammana et al, 2018) analyze the direction of price movements under an ordered probit framework, and provide empirical evidence based on stocks listed on the Australian Securities Exchange (ASX).
All papers appearing in this Special Issue went through a refereeing process subject to the usual high standards of Risks. I would like to thank all the authors for their contribution and all the referees for their thorough and timely reviews. I would also like to express my gratitude to the editor of Risks, to the Assistant Editors, and to MDPI for their support in the editorial process. I hope that this Special Issue will help to stimulate the debate on using computational methods in economic and financial modeling from both theoretical and applied perspectives.
Marina Resta
Special Issue Editor