An empirical Bayesian forecast in the threshold stochastic volatility models

Tsai Hung Fan, Yi Fu Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In the area of finance, the stochastic volatility (SV) model is a useful tool for modelling stock market returns. However, there is evidence that asymmetric behaviour of stock returns exists. A threshold SV (THSV) model is provided to capture this behaviour. In this study, we introduce a robust model created through empirical Bayesian analysis to deal with the uncertainty between the SV and THSV models. A Markov chain Monte Carlo algorithm is applied to empirically select the hyperparameters of the prior distribution. Furthermore, the value at risk from the resulting predictive distribution is also given. Simulation studies show that the proposed empirical Bayes model not only clarifies the acceptability of prediction but also reduces the risk of model uncertainty.

Original languageEnglish
Pages (from-to)486-500
Number of pages15
JournalJournal of Statistical Computation and Simulation
Volume83
Issue number3
DOIs
StatePublished - Mar 2013

Keywords

  • empirical Bayes
  • MCMC
  • stochastic volatility model
  • threshold
  • VaR

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