Discriminating modelling approaches for Point in Time Economic Scenario Generation

TL;DR

This paper introduces a unified framework for Point in Time Economic Scenario Generation, comparing various models including Generative Networks, and finds that CVAE offers robust and efficient performance for financial market forecasting.

Contribution

The paper formulates PiT ESG as a unified problem and evaluates multiple models, highlighting the superior performance of CVAE in this context.

Findings

Generative Networks outperform traditional models in PiT ESG tasks.

CVAE provides more robust and computationally efficient forecasts.

Models show improved out-of-sample forecasting accuracy.

Abstract

We introduce the notion of Point in Time Economic Scenario Generation (PiT ESG) with a clear mathematical problem formulation to unify and compare economic scenario generation approaches conditional on forward looking market data. Such PiT ESGs should provide quicker and more flexible reactions to sudden economic changes than traditional ESGs calibrated solely to long periods of historical data. We specifically take as economic variable the S&P500 Index with the VIX Index as forward looking market data to compare the nonparametric filtered historical simulation, GARCH model with joint likelihood estimation (parametric), Restricted Boltzmann Machine and the conditional Variational Autoencoder (Generative Networks) for their suitability as PiT ESG. Our evaluation consists of statistical tests for model fit and benchmarking the out of sample forecasting quality with a strategy backtest using model output as stop loss criterion. We find that both Generative Networks outperform the nonparametric and classic parametric model in our tests, but that the CVAE seems to be particularly well suited for our purposes: yielding more robust performance and being computationally lighter.