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

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.