Semi-Markov Migration Models for Credit Risk (Stochastic Models for Insurance Set)

Credit risk is one of the most important contemporary problems for banks and insurance companies.  Indeed, for banks, more than forty percent of the equities are necessary to cover this risk. Though this problem is studied by large rating agencies with substantial economic, social and financial tools, building stochastic models is nevertheless necessary to complete this descriptive orientation.
This book presents a complete presentation of such a category of models using homogeneous and non-homogeneous semi-Markov processes developed by the authors in several recent papers. This approach provides a good method of evaluating the default risk and the classical VaR indicators used for Solvency II and Basel III governance rules.

This book is the first to present a complete semi-Markov treatment of credit risk while also insisting on the practical use of the models presented here, including numerical aspects, so that this book is not only useful for scientific research but also to managers working in this field for banks, insurance companies, pension funds and other financial institutions.

Guglielmo D'Amico is Associate Professor of Applied Mathematics at "G. D'Annunzio" University of Chieti-Pescara in Italy. He has published 69 papers in peer-reviewed international journals.

Giuseppe Di Biase is Associate Professor of Applied Mathematics for Economics and Finance in the Department of Pharmacy at the "G. D'Annunzio" University of Chieti-Pescara in Italy.

Jacques Janssen is Honorary Professor at the Solvay Brussels School of Economics and Management in Brussels, Belgium. He is founding editor of the Wiley journal Applied Stochastic Models for Business and Industry, and manages book series and open journals for ISTE.

Raimondo Manca is Full Professor of Mathematics for Economics, Finance and Insurance at University of Rome "La Sapienza" in Italy. He has written 220 papers and 11 scientific books and is associate editor of Methodology and Computing in Applied Probability.