- Oxford Handbooks in Finance
- Series Editor's Preface
- List of Figures
- List of Tables
- List of Contributors
- Non-Technical Introduction
- Technical Introduction
- Default Recovery Rates and Lgd in Credit Risk Modelling and Practice
- A Guide to Modelling credit term Structures
- Statistical data mining procedures in generalized cox regressions
- An Exposition Of CDS Market Models
- Single‐and Multi‐Name Credit Derivatives: Theory and Practice
- Marshall‐Olkin Copula‐Based Models
- Contagion Models in Credit Risk
- Markov Chain Models of Portfolio Credit Risk
- Counterparty Risk in Credit Derivative Contracts
- Credit Value Adjustment in the Extended Structural Default Model
- A New Philosophy of the Market
- An EVT Primer for Credit Risk
- Saddlepoint methods in portfolio theory
- Quantitative Aspects of the collapse of the parallel banking system
- Home Price Derivatives and Modelling
- A Valuation Model for ABS Cdos
- Name Index
- Subject Index
Abstract and Keywords
This article reviews a selection of methods and results for various applications of the theory of continuous time Markov chains to valuation of credit derivatives. Section 2 begins with a review of some basic notions and results from the theory of continuous-time Markov chains. Sections 3 to 5 are devoted to the study of a few specific Markovian models of portfolio credit risk.
Tomasz R. Bielecki is a Professor of Applied Mathematics at the Illinois Institute of Technology and the Director of the Master of Mathematical Finance programme at IIT. He has previously held academic positions in the Warsaw School of Economics, University of Kansas, University of Illinois at Chicago, Northeastern Illinois University, and visiting positions in the New York University and the Argonne National Laboratory. He is an author of numerous research papers in the areas of stochastic analysis, stochastic control, manufacturing systems, operations research and mathematical finance. He is a co‐author, with Marek Rutkowski, of the monograph ‘Credit Risk: Modeling, Valuation and Hedging’, which was published by Springer‐Verlag in 2002. He is also a co‐author, with Monique Jeanblanc and Marek Rutkowski, of the monograph ‘Credit Risk Modeling,’ which was published by Osaka University Press in 2009. He has been a recipient of various research grants and awards.
Stéphane Crépey obtained his Ph.D. degree in Applied Mathematics from École Polytechnique at INRIA Sophia Antipolis and the Caisse Autonome de Refinancement (group ‘Caisse des Dépôts’). He is now an Associate Professor at the Mathematics Department of Evry University. He is director of the Master programme M.Sc. Financial Engineering of Evry University. His current research interests are Financial Modelling, Credit Risk, Numerical Finance, as well as connected mathematical topics in the fields of Backward Stochastic Differential Equations and PDEs. Stéphane Crépey also had various consulting activities in the banking and financial engineering sector.
Alexander Herbertsson is at present employed as researcher at Centre for Finance and Department of Economics at the School of Business, Economics and Law, belonging to University of Gothenburg. He holds a Ph.D. in Economics (Quantitative Finance) from University of Gothenburg, and has a Licentiate degree in Industrial mathematics from Chalmers University of Technology and an M.Sc. In Engineering Physics from the same university. During 2008 he was a postdoc at the Department of Mathematics at the University of Leipzig, Germany. His main research field is default dependence modelling with a view towards pricing and hedging portfolio credit derivatives. Alexander has also done practical work in option pricing (implied volatility tree models) as a programmer and quantitative analyst in the Financial Engineering and Risk Management group at Handelsbanken Markets, Stockholm. He has taught maths courses at Chalmers University of Technology, in stochastic calculus for Ph.D. students at the Department of Economics, and also given courses in credit risk modelling as well as financial risk.
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