Zhu, Junjun and Xie, Shiyu (2010): Bayesian Analysis of a Triple-Threshold GARCH Model with Application in Chinese Stock Market.
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We construct one triple-threshold GARCH model to analyze the asymmetric response of mean and conditional volatility. In parameter estimation, we apply Griddy-Gibbs sampling method, which require less work in selection of starting values and pre-run. As we apply this model in Chinese stock market, we find that 12-days-average return plays an important role in defining different regimes. While the down regime is characterized by negative 12-days-average return, the up regime has positive 12-days-average return. The conditional mean responds differently between down and up regime. In down regime, the return at date t is affected negatively by lag 2 negative return, while in up regime the return responds significantly to both positive and negative lag 1 past return. Moreover, our model shows that volatility reacts asymmetrically to positive and negative innovations, and this asymmetric reaction varies between down and up regimes. In down regime, volatility becomes more volatile when negative innovation impacts the market than when positive one does, while in up regime positive innovation leads to more volatile market than negative one.
|Item Type:||MPRA Paper|
|Original Title:||Bayesian Analysis of a Triple-Threshold GARCH Model with Application in Chinese Stock Market|
|Keywords:||Threshold; Griddy-Gibbs sampling; MCMC method; GARCH|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Junjun Zhu|
|Date Deposited:||20. Jan 2011 06:54|
|Last Modified:||15. Feb 2013 06:47|
Andrews, D.W.K. (1993), Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, Vol. 61, pp821-856.
Andrews, D.W.K. and Ploberger, W. (1994), Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative, Econometrica, Vol. 62, pp1383-1414.
Bauwens, L., Preminger, A. and Rombouts, J. (2010), Theory and Inference for a Markov-Switching GARCH Model, Econometrics Journal, Vol. 13, pp218 – 244.
Brooks, C. (2001) A Double-threshold GARCH Model for the French Franc/Deutschmark Exchange Rate, Journal of Forecasting, Vol. 20, pp135-143.
Chen, C.W.S. and Lee, J.C. (1995) Bayesian Inference of Threshold Autoregressive Models, Journal of Time Series Analysis, Vol. 16, pp483-492.
Chen, C.W.S., Chiang, T.C. and So, M.K.P. (2003) Asymmetrical Reaction to US Stock-return News: Evidence from Major Stock Markets Based on a Double-threshold Model, Journal of Economics and Business, Vol. 55, pp487-502．
Chen, C.W.S. and So, M.K.P, (2006) On a Threshold Heteroscedastic Model, International Journal of Forecasting, Vol. 22, pp73– 89.
Geweke, J. and Terui, N. (1993) Bayesian Threshold Autoregressive Models for nonlinear Time Series, Journal of Time Series Analysis, Vol. 14, pp441-454.
Glosten, L. R., Jaganathan, R, and Runkle, D. (1993) On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance, Vol. 48, pp1779-1801.
Goldman, E. and Agbeyegbe, T.D. (2007) Estimation of Threshold Time Series Models Using Efficient Jump MCMC, in Bayesian Statistics and Its Applications, edited by Upadhyway, S.K., Singh, U. and Dey, D.K., pp.241-253, Anamaya Publishers, New Delhi.
Hwang, S.Y. Baek, J.S. Park, J.A. Choi, M.S. (2010) Explosive Volatilities for Threshold-GARCH Processes Generated by Asymmetric Innovations, Statistics and Probability Letters, Vol. 80, pp26-33.
Li, W.K. and Lam, K.(1995) Modeling Asymmetry in Stock Returns by a Threshold ARCH Model, The Statistian, Vol. 44, pp333-341．
Li, C.W. and Li, W.K. (1996) On a Double-threshold Autoregressive Heteroscedastic Time Series Model, Journal of Applied Econometrics, Vol. 11, pp253-274．
Phann, G.A., Schotman, P.C. and Tschering, R. (1996) Non-linear Interest Rate Dynamics and Implications for the Term Structure, Journal of Econometrics, Vol. 74, pp149-176.
Tong, H. (1978) On a Threshold Model, in: C.H. Chen (Ed.), in Pattern Recognition and Signal Processing, Amsterdam: Sijhoff &Noordhoff.
Tong, H. (1983) Threshold Models in Non-linear Time Series Analysis, New York: Springer-Verlag.
Yang Y-L, Chang, C-L. (2008) A Double-threshold GARCH Model of Stock Market and Currency Shocks on Stock Returns, Mathematics and Computers in Simulation, Vol. 79, pp458–474.
Zakoian, J.M. (1994) Threshold Heteroskedastic Models, Journal of Economic Dynamics and Control, Vol. 18, pp931-955.
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