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• W3016590716 abstract "Economic Scenario Generators (ESGs) simulate economic and financial variables forward in time for risk management and asset allocation purposes. It is often not feasible to calibrate the dynamics of all variables within the ESG to historical data alone. Calibration to forward-information such as future scenarios and return expectations is needed for stress testing and portfolio optimization, but no generally accepted methodology is available. This paper introduces the Conditional Scenario Simulator, which is a framework for consistently calibrating simulations and projections of economic and financial variables both to historical data and forward-looking information. The framework can be viewed as a multi-period, multi-factor generalization of the Black-Litterman model, and can embed a wide array of financial and macroeconomic models. Two practical examples demonstrate this in a frequentist and Bayesian setting." @default.
• W3016590716 created "2020-04-24" @default.
• W3016590716 creator A5074806199 @default.
• W3016590716 date "2020-04-20" @default.
• W3016590716 modified "2023-09-27" @default.
• W3016590716 title "Consistent Calibration of Economic Scenario Generators: The Case for Conditional Simulation." @default.
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• W3016590716 hasPublicationYear "2020" @default.
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