  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.
