Hands-On Value-at-Risk and Expected Shortfall, 1st ed. 2018 A Practical Primer Management for Professionals Series

This book describes a maximally simple market risk model that is still practical and main risk measures like the value-at-risk and the expected shortfall. It outlines the model's (i) underlying math, (ii) daily operation, and (iii) implementation, while stripping away statistical overhead to keep the concepts accessible. The author selects and weighs the various model features, motivating the choices under real-world constraints, and addresses the evermore important handling of regulatory requirements. The book targets not only practitioners new to the field but also experienced market risk operators by suggesting useful data analysis procedures and implementation details. It furthermore addresses market risk consumers such as managers, traders, and compliance officers by making the model behavior intuitively transparent.

A very useful guide to the theoretical and practical aspects of implementing and operating a risk-monitoring system for a mid-size financial institution. It sets a common body of knowledge to facilitate communication between risk managers, computer and investment specialists by bridging their diverse backgrounds.

Giovanni Barone-Adesi ? Professor, Universitá della Svizzera italiana

 

This unassuming and insightful book starts from the basics and plainly brings the reader up to speed on both theory and implementation.

Shane Hegarty ? Director Trade Floor Risk Management, Scotiabank

 

Visit the book?s website at www.value-at-risk.com.

1 Introduction.- 2 Motivation.- Part I MEASURES.- 3 Basic Terms and Notation.- 4 Historical Value-at-Risk.- 5 Sensitivities.- 6 Stress Tests.- 7 Analytical Value-at-Risk.- 8 Expected Shortfall.- 9 Model Choices.- 10 A Monte Carlo Modi cation.- 11 Support Measures.- Part II OPERATIONS.- 12 Properties of VaR.- 13 Properties of ES.- 14 VaR Noise.- 15 Backtesting.- 16 Distribution Test.- 17 Nine to Five.- Part III SETUP.- 18 Context.- 19 Scope and Workflow.- 20 Implementation.- PART IV WRAP-UP.- 21 Conclusion.- 22 Acknowledgments.- APPENDIX.

Avoids unnecessary formalism to allow for an intuitive understanding of the behavior of risk models and measures for an audience with varying quantitative backgrounds Selects and weighs different models under real-world constraints to illustrate the simplest - yet still practical - model Provides the required regulatory context to validate risk models and to ease communication between different stakeholders