Volatility Switching in Shanghai Stock Exchange: Does regulation help reduce volatility?

Zhang, Dayong and Dickinson, David and Barassi, Marco (2008): Volatility Switching in Shanghai Stock Exchange: Does regulation help reduce volatility?

Abstract

This paper investigates volatility switching in the Shanghai Stock Exchange (SSE hereafter,) using several recently developed techniques. They can be categorized into CUSUM type tests and Markov-Switching ARCH models. By detecting and dating switches with these models, we are able to show the volatility dynamics in SSE. Investigating the events in SSE around the switching date suggests that regulation improvements significantly reduce the volatility of the underlying market. Furthermore, the empirical results show that outliers can have significant impact on the conclusion and thus should properly be removed.

• Item Type: MPRA Paper
• Language: English
• Keywords: Volatility switching; CUSUM test; Markov-Switching ARCH; Shanghai Stock Exchange; Outlier.
• Subjects: G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; G - Financial Economics > G1 - General Financial Markets > G18 - Government Policy and Regulation

References

Andreou, E. and Ghysels, E. (2002) Detecting multiple breaks in financial market volatility dynamics, Journal of Applied Econometrics 17, 579-600.

Bauwens, Luc, Preminger, Arie and Rombouts, J. V.K. (2007) Theory and inference for a Markov Switching GARCH model, CORE Discussion paper 55.

Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327.

Cai, J. (1994) A Markov model of switching-regime ARCH, Journal of Business and Economic Statistics 12, 309-316.

Chaudhuri, K. and Klaassen, F. (2001) Have East Asian stock markets calmed down? Evidence from a regime-switching model, mimeo, University of Amsterdam .

Chen, C. and Liu, L.-M. (1993) Joint estimation of model parameters and outlier effects in time series, Journal of the American Statistical Association 88, 284-297.

Doornik, Jurgen A. and Ooms M. (2005) Outlier Detection in GARCH Models, Economic papers 2005-W24, Economics Group, Nuffield College, University of Oxford.

Dueker, J. (1997) Markov switching in GARCH processes and mean reverting stock market volatility, Journal of Business and Economic statistics 15, 26-34.

Franses, P. H. and Van Dijk, D. (1999) Outlier Detection in the GARCH(1,1) model, Working paper 9926/a, Erasmus University of Rotterdam Econometric Institute.

den Haan, W. J. and Levin, A. (1997) Handbook of Statistics, Elsevier, A practioner's Guide to Robust Covariance Matrix Estimation, pp. 291-341.

Hamilton, J. and Susmel, R. (1994) Autoregressive conditional heteroskedasticity and changes in regime, Journal of Econometrics 64, 307-333.

Inclan, C. and Tiao, G. C. (1994) Use of cumulative sums of squares for retrospective detection of changes of variance, Journal of American Statistical Association 89, 913-923.

Kim, S., Cho, S. and Lee, S. (2000) On the CUSUM test for parameter changes in GARCH(1,1) models, Communications in Statistics: Theory and Methods 29, 445-462.

Kokoszka, P. and Leipus, R. (1998) Change-point in the mean of dependent observations, Statistics and Probability Letters 40, 385-393.

Kokoszka, P. and Leipus, R. (2000) Change-point estimation in ARCH models, Bernoulli 6, 1-28.

Lamoureux, C. and Lastrpes, W. (1990) Heteroskedasticity in stock return data: Volume versus GARCH effects, Journal of Finance 45, 221-229.

Lee, S., Tokutsu, Y. and Maekawa, K. (2003) The residual CUSUM test for the constancy of parameters in GARCH (1,1) models, Working papers at Hiroshima University .