- 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 develops a pricing model for asset-backed securities collateralised debt obligation tranches backed by mortgage collateral (including prime, sub-prime, and other property types) via the Monte Carlo method. It is organized as follows. Section 2 presents a brief historical overview of the sub-prime market. Section 3 provides the core technical description of the scenario generator. After the stochastic model is established in Section 4, the calibration procedure is presented. Section 5 and 6 discuss results and conclusions, respectively.
Julian Manzano holds a Ph.D. in Theoretical Physics from the Faculty of Physics, University of Barcelona, Spain, and a Master in Financial Derivatives from the Faculty of Statistics and Mathematics, Universitat Politècnica de Catalunya. After completing a postdoc at the Department of Physics and Measurement Technology, Linköping University, Sweden, in 2004 he joined HSBC Bank in New York working as a structured credit quant. In 2006 he joined Merrill Lynch in London and currently he is working for Bank of America Merrill Lynch on the area on algorithmic trading focusing on the FX market.
Vladimir Kamotski, gained an M.Sc. From St Petersburg State University (1999) and Ph.D. from Steklov Institute of Mathematics (2003). He was a postdoc at University of Bath (BICS) and worked on multi‐scale problems in PDEs. Since 2007 he has been quantitative analyst at Merrill Lynch, and is the author of five papers in major scientific journals.
Umberto Pesavento, a native of Padova, Italy, graduated from Princeton University with a BA in Physics. He received a Ph.D. in Physics from Cornell University where he also worked as a postdoctoral associate and lecturer in the Theoretical and Applied Mechanics Department conducting research in fluid dynamics and computational physics. Since 2008 he has been working at Merrill Lynch and Bank of America Merrill Lynch as a quant in the credit and interest rates groups.
Alexander Lipton is a Managing Director and Co-Head of the Global Quantitative Group at Bank of America Merrill Lynch, and Visiting Professor of Mathematics at Imperial College. Prior to his current role, he was Managing Director and Head of Capital Structure Quantitative Research at Citadel Investment Group in Chicago. He has also worked at Credit Suisse, Deutsche Bank, and Bankers Trust. Previously, he was a Full Professor of Mathematics at the University of Illinois, Chicago, and Consultant at Los Alamos National Laboratory. He received his undergraduate and graduate degrees from Moscow State University. Professor Lipton is author of two books and editor of three. He has published numerous research papers on hydrodynamics, magnetohydrodynamics, astrophysics, and financial engineering. He has delivered many invited lectures at leading universities and major conferences worldwide.
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