Hands-On Value-at-Risk and Expected Shortfall

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

Autorentext
Martin Auer is senior consultant in the areas of quantitative finance, market and credit risk, and IT, working for the likes of Raiffeisen Bank International, Kommunalkredit, and T-Systems in Vienna, and for Bank of America in New York. He holds degrees in computer science, mathematics, and mathematics of finance from Vienna University of Technology, University of Vienna, and Columbia University.

Inhalt
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.