Density forecast evaluations via a simulation-based dynamic probability integral transformation

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Abstract

This paper presents simulation-based density forecast evaluation methods using particle filters. The simulation-based dynamic probability integral transformation or log-likelihood evaluation method is combined with the existing density forecast evaluation methods. This methodology is applicable to various density forecast models, such as log stochastic volatility models and affine jump diffusion (AJD) models, for which the probability integral transform or likelihood computation is difficult due to the presence of latent stochastic volatilities. This methodology is applied to the daily S&P 500 stock index. The empirical results show that the AJD models with jumps perform the best for out-of-sample evaluations.

Original languageEnglish
Pages (from-to)24-58
Number of pages35
JournalJournal of Financial Econometrics
Volume18
Issue number1
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press. All rights reserved.

Keywords

  • Affine jump diffusion models
  • Density forecasts
  • Particle filters
  • Simulation-based dynamic probability integral transform

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