Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets (Wiley Finance)
| Author | : | |
| Rating | : | 4.20 (972 Votes) |
| Asin | : | B0148KM73A |
| Format Type | : | |
| Number of Pages | : | 427 Pages |
| Publish Date | : | 2017-08-27 |
| Language | : | English |
DESCRIPTION:
A risk measurement and management framework that takes model risk seriouslyMost financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.. Recognize the assumptions embodied in classical statisticsQuantify model risk along multiple dimensions without backtesti
A pretty good text on Bayesian adaptive econometric time series analysis, little relevance to finance or risk management Aaron C. Brown This book is a pretty good textbook introduction to adaptive Bayesian time series analysis in econometrics. What's strange it that it claims to be a book on financial risk management. If that's your interest, you will be better served by Riccardo Rebonato's A Bayesian Approach to the Analysis of Financial Stress, The Perfect Hedger and the Fox, A Bayesian-Net Approach to Coherent Asset Allocation or Plight of the Fortune Tellers. Those books do not cover the
They don't have to. Yet many, if not most, financial services firms lack insight into the probabilistic structure of risk models and the corresponding risk of model failures. Bayesian probability methods are used throughout the book to: Understand the assumptions underlying classical time-series methods and the manner in which they restrict ongoing learning about market conditionsAccount for the possibility that different risk models may be useful under alternative market conditions, and that model parameters are known imperfectlyAllow risk models to adjust continuously to changing market conditions, incorporating varying degrees of memory and coherently revising model estimates from day to day in light of new informationDevelop and compare alternativ
He holds a BA in economics and mathematics from The Johns Hopkins University, an MA in history from The Johns Hopkins University, and an MBA in econometrics and statistics, analytic finance, and entrepreneurship from The University of Chicago Booth School of Business. He is also a CFA charterholder, a certified Financial Risk Manager, and a certified Energy Risk Professional.. MATT SEKERKE is an economic consultant based