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We introduce the notion of Point in Time Economic Scenario Generation (PiTESG) with a clear mathematical problem formulation to unify and compareeconomic scenario generation approaches conditional on forward looking marketdata. Such PiT ESGs should provide quicker and more flexible reactions tosudden economic changes than traditional ESGs calibrated solely to long periodsof historical data. We specifically take as economic variable the S&P500 Indexwith the VIX Index as forward looking market data to compare the nonparametricfiltered historical simulation, GARCH model with joint likelihood estimation(parametric), Restricted Boltzmann Machine and the conditional VariationalAutoencoder (Generative Networks) for their suitability as PiT ESG. Ourevaluation consists of statistical tests for model fit and benchmarking the outof sample forecasting quality with a strategy backtest using model output asstop loss criterion. We find that both Generative Networks outperform thenonparametric and classic parametric model in our tests, but that the CVAEseems to be particularly well suited for our purposes: yielding more robustperformance and being computationally lighter.

Discriminating modelling approaches for Point in Time Economic Scenario Generation