Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets (eng)

Sekerke, Matt

ISBN 10: 1118708601 / ISBN 13: 9781118708606

Published by Wiley, 2015

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Seller Inventory # 4ce3d711df3d5994a9bb62fbba7f8940

Bibliographic Details

• Title: Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets (eng)
• Publisher: Wiley
• Publication Date: 2015
• Language: English
• ISBN 10: 1118708601
• ISBN 13: 9781118708606
• Binding: Hardcover
• Condition: new
• Weight: 0.48 kilograms

About this title

Synopsis

A risk measurement and management framework that takes model risk seriously

Most 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. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. 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.

• Recognize the assumptions embodied in classical statistics
• Quantify model risk along multiple dimensions without backtesting
• Model time series without assuming stationarity
• Estimate state-space time series models online with simulation methods
• Uncover uncertainty in workhorse risk and asset-pricing models
• Embed Bayesian thinking about risk within a complex organization

Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.

About the Author

MATT SEKERKE is an economic consultant based in New York whose work focuses on the financial services industry and the application of advanced quantitative modeling techniques o financial data. 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.