Forecasting Volatility with Asymmetric Smooth Transition Dynamic Range Models

Posted: 11 May 2012

See all articles by Edward M.H. Lin

Edward M.H. Lin

Graduate Institute of Applied Statistics, Feng Chia University

Cathy W. S. Chen

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

Richard H. Gerlach

University of Sydney

Date Written: May 10, 2012

Abstract

We propose a nonlinear smooth transition conditional autoregressive range (CARR) model for capturing smooth volatility asymmetries in international financial stock markets, building on recent work on smooth transition conditional duration modelling. An adaptive Markov chain Monte Carlo scheme is developed for Bayesian estimation, volatility forecasting and model comparison for the proposed model. The model can capture sign or size asymmetry and heteroskedasticity, such as that which is commonly observed in financial markets. A mixture proposal distribution is developed in order to improve the acceptance rate and the mixing issues which are common in random walk Metropolis-Hastings methods. Further, the logistic transition function is employed and its main properties are considered and discussed in the context of the proposed model, which motivates a suitable, weakly informative prior which ensures a proper posterior distribution and identification of the estimators. The methods are illustrated using simulated data, and an empirical study also provides evidence in favour of the proposed model when forecasting the volatility in two financial stock markets. In addition, the deviance information criterion is employed to compare the proposed models with their limiting classes, the nonlinear threshold CARR models and the symmetric CARR model.

Keywords: smooth transition, volatility model, threshold variable, Bayesian inference, MCMC methods

Suggested Citation

Lin, Edward M.H. and Chen, Cathy W. S. and Gerlach, Richard H., Forecasting Volatility with Asymmetric Smooth Transition Dynamic Range Models (May 10, 2012). International Journal of Forecasting, Vol. 28, No. 2, 2012, Available at SSRN: https://ssrn.com/abstract=2055605

Edward M.H. Lin (Contact Author)

Graduate Institute of Applied Statistics, Feng Chia University ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan

Cathy W. S. Chen

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/

Richard H. Gerlach

University of Sydney ( email )

Room 483, Building H04
University of Sydney
Sydney, NSW 2006
Australia
+ 612 9351 3944 (Phone)
+ 612 9351 6409 (Fax)

HOME PAGE: http://www.econ.usyd.edu.au/staff/richardg

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