Heavy-Tailed-Distributed Threshold Stochastic Volatility Models in Financial Time Series

Australian & New Zealand Journal of Statistics, Vol. 50, pp. 1-23, 2008

30 Pages Posted: 27 May 2009 Last revised: 27 Aug 2009

See all articles by Cathy W. S. Chen

Cathy W. S. Chen

Feng Chia University - Department of Statistics; Graduate Institute of Statistics & Actuarial Science, Feng Chia University

Feng-Chi Liu

Feng Chia University - Department of Statistics

Mike K. P. So

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics & Operations Management

Date Written: March 1, 2008

Abstract

To capture mean and variance asymmetries and time-varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy-tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time-delay parameter. Self-exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach,we use Markov chainMonte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value-at-risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.

Keywords: Kalman filter, Markov chain Monte Carlo method, state space model, stochastic volatility models, threshold, value-at-risk

Suggested Citation

Chen, Cathy W. S. and Liu, Feng-Chi and So, Mike K.P., Heavy-Tailed-Distributed Threshold Stochastic Volatility Models in Financial Time Series (March 1, 2008). Australian & New Zealand Journal of Statistics, Vol. 50, pp. 1-23, 2008, Available at SSRN: https://ssrn.com/abstract=1410419

Cathy W. S. Chen (Contact Author)

Feng Chia University - Department of Statistics ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan
886 4 24517250 ext 4412 (Phone)
886 4 24517092 (Fax)

HOME PAGE: http://myweb.fcu.edu.tw/~chenws/

Graduate Institute of Statistics & Actuarial Science, Feng Chia University

100 Wenhwa Road
Talchung
Taiwan
886 4-24517250 ext 4412 (Phone)
886 4-2517092 (Fax)

HOME PAGE: http://myweb.fcu.edu.tw/~chenws/

Feng-Chi Liu

Feng Chia University - Department of Statistics ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan
+886-4-24517250 ext 4416 (Phone)

Mike K.P. So

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics & Operations Management ( email )

Clear Water Bay, Kowloon
Hong Kong

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
101
Abstract Views
744
Rank
476,311
PlumX Metrics