A threshold stochastic volatility model

Mike K. P. SO, Wai Keung LI, K. LAM

Research output: Contribution to journalArticlespeer-review

79 Citations (Scopus)


This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)473-500
JournalJournal of Forecasting
Issue number7
Publication statusPublished - Nov 2002


So, M. K. P., Li, W. K., & Lam, K. (2002). A threshold stochastic volatility model. Journal of Forecasting, 21(7), 473-500. doi: 10.1002/for.840


  • ARCH model
  • Gibbs sampling
  • Kalman filter
  • Monte Carlo Markov Chain
  • State space model


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