Value at risk estimation by threshold stochastic volatility model

Yi-Hou Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.

Original languageEnglish
Pages (from-to)4884-4900
Number of pages17
JournalApplied Economics
Issue number45
StatePublished - 26 Sep 2015


  • stochastic volatility
  • threshold model
  • value at risk


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