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Bayesian Risk Management - A Guide to Model Risk and Sequential Learning in Financial Markets (Hardcover)
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Bayesian Risk Management - A Guide to Model Risk and Sequential Learning in Financial Markets (Hardcover)
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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.
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