Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets (Wiley Finance) - Tapa dura

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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.

De la contraportada

A Risk Measurement and Management Framework that Takes Model Risk Seriously

Why do risk models break down? The answer may lie in the way that statistical methods are conventionally used to draw inferences about market conditions and inform risk-taking behavior. Bayesian Risk Management enables a discussion on the way standard statistical methods overlook uncertainty in model specifications, model parameters, and model-driven forecasts. In a simple and direct way, Bayesian methods are used throughout the book to:

• Recognize the assumptions embodied in classical statistics
• Quantify model risk along multiple dimensions
• Model time series without assuming continuity between past and future
• Adjust time-series estimates to maintain forecast accuracy
• Uncover uncertainty in workhorse risk and asset-pricing models
• Achieve decentralized control of risk-taking in complex organizations

For firms in financial services and other industries operating in a dynamic environment of incomplete information, Bayesian Risk Management provides a thought-provoking challenge to the prevailing wisdom about the uses and limitations of statistical risk modeling.

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Most financial risk models assume that the future will look like the past. They don't have to. Bayesian Risk Management sketches a more flexible risk-modeling approach that more fully recognizes the irreducibility of our uncertainty about the future.

The risk that a firm's models may fail to capture shifts in market pricing, risk sensitivities, or the mix of the firm's risk exposures is a central operational risk for any financial services business. Yet many, if not most, financial services firms lack insight into the probabilistic structure of risk models and the corresponding risk of model failures. The thesis of Bayesian Risk Management is that most firms lack insight into model risk because of the way they practice statistical modeling. Because generally accepted statistical practice provides thin means for assessing model risk, alternative methods are needed to take model risk seriously. 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 conditions
• Account for the possibility that different risk models may be useful under alternative market conditions, and that model parameters are known imperfectly
• Allow 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 information
• Develop and compare alternative online models for single- and multiple-asset volatility
• Simulate the evolution of state variables and model parameters in dynamic asset pricing models to distinguish market and model risk

Ignoring the many dimensions of model risk means measuring too little risk and assuming too much of it. Bayesian Risk Management provides a coherent framework for discerning one's informational advantages and limitations in rapidly-evolving financial markets.