Modeling, Measuring and Hedging Operational Risk. The Wiley Finance Series

  • ID: 2215689
  • Book
  • 346 Pages
  • John Wiley and Sons Ltd
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"Dr Marcelo Cruz is rightfully acknowledged as a world expert in the quantification of operational risk. He has set out to produce a book that is comprehensive yet also comprehensible to non–mathematicians – and is to be congratulated for succeeding in this aim. This book should be regarded as essential reading for all professional risk managers, irrespective of their particular lens of perception." Brendan Young, Chairman, Operational Risk Research Forum

"As a technically trained analyst, Marcelo Cruz summarizes a wide range of mathematical techniques. As an experienced capital markets trader and risk manager, he provides real world examples of their relevance for operational risk. This will be a common reference work in the field for years to come." David M. Rowe, Ph.D., Group Executive Vice President for Risk Management Sun Gard Trading and Risk Systems

Operational risk is an important, yet little explored, area within risk management. The need to model and measure the risks arising from operational errors and to allocate capital against them will be soon become a regulatory requirement for financial institutions. In this book, Marcelo Cruz provides a quantitative look at the subject, presenting several mathematical models that can be used and adapted to measure, manage and hedge operational risk.

Based on the author′s extensive experience, the book maps out state–of–the–art mathematical and statistical techniques that can be used to model operational risk. In addition, the book describes a variety of appropriate models that can be applied to specific structures or areas, including operational risk database modeling, stochastic models, statistical distributions for frequency and severity, extreme value theory, operational VaR models, artificial intelligence models, dynamic multifactor models, Bayesian analysis, Monte Carlo simulation, stress test/ scenario analysis, real options, state–space models and the Kalman filter, Markovian stochastic models and others. These models have been tested with real data in real operational events. Based on this experience, numerous examples are sited throughout.

Modeling, Measuring and Hedging Operational Risk provides a complete quantitative reference for all those involved in modeling and managing operational risk as well as for those involved with developing hedging products for operational risk within insurance companies and derivatives houses.
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Introduction

PART I: DATABASE MODELING

Database Modeling

PART II: STOCHASTIC MODELING

Severity Distributions

Extreme Value Theory

Frequency Distributions

The Operational Risk VaR

Stochastic Processes in Operational Risk

PART III: CAUSAL MODELS

Causal Models : Applying Econometrics and Time Series Statistics to Operational Risk

Non–Linear Models in Operational Risk

Bayesian Techniques in Operational Risk

PART IV: OPERATIONAL RISK MANAGEMENT

Operational Risk Reporting, Control and Management

– Stress Tests and Scenario Analysis in Operational Risk

PART V: HEDGING OPERATIONAL RISK

Operational Risk Derivatives

Developing an OR Hedging Program

PART VI: OPERATIONAL RISK REGULATORY CAPITAL

Operational Risk Regulatory Capital

PART VII: MEASURING "OTHER RISKS" –

FOREGONE REVENUE MEASUREMENT MODELS

An Enterprise–Wide Model for Measuring Reputational Risk

– Measuring Concentration (or Key Personnel) Risk

– Using Real Options in Modeling and Measuring Operational and ′Other′ Risks

APPENDIX –

Valuing Networks –

Understanding the basics of e–ventures valuation
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Marcelo G. Cruz
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