Discriminating modelling approaches for Point in Time Economic Scenario
Generation
- URL: http://arxiv.org/abs/2108.08818v1
- Date: Thu, 19 Aug 2021 17:36:53 GMT
- Title: Discriminating modelling approaches for Point in Time Economic Scenario
Generation
- Authors: Rui Wang
- Abstract summary: We introduce the notion of Point in Time Economic Scenario Generation (PiT ESG)
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 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.
- Score: 5.733401663293044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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