From the Back Cover
Building an accurate representation of firm-wide credit exposure, used for both trading and risk management, raises significant theoretical and technical challenges. This volume can be considered as a roadmap to finding practical solutions to the problem of modelling, pricing, and hedging counterparty credit exposure for large portfolios of both vanilla and exotic derivatives, usually traded by large Investment Banks. It is divided into four parts, (I) Methodology, (II) Architecture and Implementation, (III) Products, and (IV) Hedging and Managing Counterparty Risk. Starting from a generic modelling and valuation framework based on American Monte Carlo techniques, it presents a software architecture, which, with its modular design, allows the computation of credit exposure in a portfolio-aggregated and scenario-consistent way. An essential part of the design is the definition of a programming language, which allows trade representation based on dynamic modelling features. Several chapters are then devoted to the analysis of credit exposure across all asset classes, namely foreign exchange, interest rate, credit derivatives and equity. Finally it considers how to mitigate and hedge counterparty exposure. The crucial question of dynamic hedging is addressed by constructing a hybrid product, the Contingent-Credit Default Swap.
This volume addresses, from a quantitative perspective, recent developments related to counterparty credit exposure computation. Its unique characteristic is the combination of a rigorous but simple mathematical approach with a practical view of the financial problem at hand.
"...a fantastic book that covers all aspects of credit exposure modelling. Nowhere else can the interested reader find such a comprehensive collection of insights around this topic covering methodology, implementation, products and applications. A "must read" for practitioners and quants working in this space." Jörg Behrens, Fintegral Consulting, CH
"In the aftermath of the credit crunch, nobody will need convincing of the importance of managing counterparty risk. This unique book provides a consistent approach to the subject, taken all the way from underlying concepts to the nuts and bolts of computer architecture. It opens up many avenues for future research and throws down a challenge to the industry at large: any organization whose techniques are not at least as good as the ones described here had better shape up!" Mark Davis, Imperial College London, UK
"…impressive mathematical monograph … first unified and comprehensive approach to pricing and measuring counterparty credit exposures and, therefore, an essential must-have for all quantitatively oriented credit risk manager, academic researchers, and mathematics students alike … takes into account a unified approach for modelling the future economic scenarios across all asset classes under risk-neutral measure while generating a theoretic as well as technical framework for calculating credit and debit valuation adjustments … easily adapted to calculating the price of credit risk … flexible enough to price complex and hybrid financial derivatives in a completely scenario consistent way. These features make the book an absolutely outstanding and highly recommendable treatise…" Marcus R.W. Martin, Darmstadt University of Applied Sciences, D
About the Author
John Aquilina holds an M.Phil. in Statistical Science from the University of Cambridge and a Ph.D. in Mathematical Finance from the University of Bath. He has worked on modelling counterparty credit exposure at UBS since 2005.
Niels Charpillon holds a Diplôme d'Ingénieur from Ecole des Mines, an M.Sc. in Financial Mathematics from Warwick Business School, and a Licence in Economics from University of St. Etienne. He joined the counterparty exposure team at UBS in 2006.
Zlatko Filipovic started working for UBS in 2005 as a Quantitative Analyst in the counterparty exposure team. Before joining UBS, Zlatko had been working for Mako Global Derivatives, London, as a Financial Engineer. Zlatko obtained a Ph.D. in Quantitative Finance from Imperial College, London, after graduating from the Faculty of Mathematics, University of Belgrade.
Gordon Lee joined the counterparty exposure team at UBS in 2006. Prior to UBS, he was a Senior Associate in quantitative risk and performance analysis at Wilshire Associates. Gordon holds an M.A. in Mathematics from Churchill College, University of Cambridge.
Ion Manda holds a Diploma de Inginer in Software Engineering from the University of Bucharest and a M.Sc. in Financial Engineering from the University of London. He has been working in the credit exposure team at UBS since 2006. Ion is the creator of the Portfolio Aggregation Language (PAL) and the architect of the Risk Analytics.