• Computing Optimal Recovery Pol...

Result: Computing Optimal Recovery Policies for Financial Markets

Title:
Computing Optimal Recovery Policies for Financial Markets
Authors:
BENTH, Fred E, DAHL, Geir, MANNINO, Carlo
Source:
Operations research. 60(6):1373-1388
Publisher Information:
Hanover, MD: Institute for Operations Research and the Management Sciences, 2012.
Publication Year:
2012
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Flots dans les réseaux. Problèmes combinatoires, Flows in networks. Combinatorial problems, Théorie du risque. Assurance, Risk theory. Actuarial science, Sélection et gestion de portefeuilles, Portfolio theory, Analyse risque, Risk analysis, Análisis riesgo, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Budget, Presupuesto, Champ aléatoire, Random field, Campo aleatorio, Crise économique, Economic crisis, Crisis económica, Crédit, Credit, Crédito, Emprunt, Loan, Préstamo, Entreprise, Firm, Empresa, Estimation paramètre, Parameter estimation, Estimación parámetro, Identification système, System identification, Identificación sistema, Marché financier, Financial market, Mercado financiero, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modélisation, Modeling, Modelización, Méthode combinatoire, Combinatorial method, Método combinatorio, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Politique financière, Financial policy, Política financiera, Politique optimale, Optimal policy, Política óptima, Processus Markov, Markov process, Proceso Markov, Programmation biniveau, Bilevel programming, Programación binivel, Programmation discrète, Discrete programming, Programación discreta, Prêt, Loans, Système incertain, Uncertain system, Sistema incierto, Secteur bancaire, Banking, Sector bancario, Markov random field, bilevel programming, discrete optimization, financial models
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics, Centre of Mathematics for Applications, University of Oslo, 0316 Oslo, Norway
Department of Mathematics and Department of Informatics, Centre of Mathematics for Applications, University of Oslo, 0316 Oslo, Norway
Department of Computer Science, University of Rome La Sapienza, Rome, Italy; and Centre of Mathematics for Applications, 0316 Oslo, Norway
ISSN:
0030-364X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26736257
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Operational research. Management
Accession Number:
edscal.26736257
Database:
PASCAL Archive

Further Information

The current financial crisis motivates the study of correlated defaults in financial systems. In this paper we focus on such a model, which is based on Markov random fields. This is a probabilistic model in which uncertainty in default probabilities incorporates experts' opinions on the default risk (based on various credit ratings). We consider a bilevel optimization model for finding an optimal recovery policy: which companies should be supported given a fixed budget. This is closely linked to the problem of finding a maximum likelihood estimator of the defaulting set of agents, and we show how to compute this solution efficiently using combinatorial methods. We also prove properties of such optimal solutions and give a practical procedure for estimation of model parameters. Computational examples are presented, and experiments indicate that our methods can find optimal recovery policies for up to about 100 companies. The overall approach is evaluated on a real-world problem concerning the major banks in Scandinavia and public loans. To our knowledge, this is a first attempt to apply combinatorial optimization techniques to this important and expanding area of default risk analysis.