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

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This book provides elaborating tools to measure financial risk without assuming that time-invariant stochastic processes drive financial phenomena. Discarding time-invariance as a modeling assumption makes uncertainty about parameters, models, and forecasts accessible and irreducible in a way standard statistical risk measurements do not. The constructive alternative offered here under the slogan Bayesian Risk Management is an online sequential Bayesian modeling framework that acknowledges all of these sources of uncertainty, without giving up the structure afforded by parametric risk models and asset-pricing models. Part One of the book shows where Bayesian analysis opens up uncertainty about parameters and models in a static setting. Bayesian results are compared to standard statistical results to make plain the strong assumptions embodied in classical, ‘objective’ statistics. Part Two extends the Bayesian framework to sequential time series analysis. Part Three then applies the methods developed in the first two parts to the estimation of volatility and the estimation of a commodity forward curve under the risk-neutral measure subject to arbitrage restrictions. Part Four, synthesizes the results of the first three parts and begins the transition from a riskmeasurement framework based on Bayesian principles to a properly Bayesian riskmanagement.

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