JSS  Vol.3 No.7 , July 2015
Intraday Periodicity and Long Memory Volatility in Hong Kong Stock Market
ABSTRACT

This paper characterizes the volatility in Hong Kong Stock Market based on a 2-year sample of 5-min Heng Seng Index. By using the method of Flexible Fourier Form Filtering, we have successful removed the periodicity and have built a model of ARMA (1,1)-FIAPARCH (2, 0.300165,1). Further, the intraday volatility exists with long memory and asymmetry; the negative shock from the market will give rise to a higher volatility than the positive ones.


Cite this paper
Dai, W. , Xie, D. and Sun, B. (2015) Intraday Periodicity and Long Memory Volatility in Hong Kong Stock Market. Open Journal of Social Sciences, 3, 61-66. doi: 10.4236/jss.2015.37011.
References
[1]   Bollerslev, T., Chou, R.Y. and Kroner, K.F. (1992) ARCH Modeling in I’inance. Journal of Econometrics, 52, 5-59. http://dx.doi.org/10.1016/0304-4076(92)90064-X

[2]   Ghysels, E., Harvey, A. and Renault, E. (1995) Stochastic Volatility. CIRANO.

[3]   Karpoff, J.M. (1987) The Relation between Price Changes and Trading Volume: A Survey. Journal of Financial and Quantitative Analysis, 22, 109-126.

[4]   Lamoureux, C.G. and Lastrapes, W.D. (1990) Persistence in Variance, Structural Change, and the GARCH Model. Journal of Business & Economic Statistics, 8, 225-234.

[5]   Majand, M. and Yung, K. (1991) A GARCH Examination of the Relationship between Volume and Price Variability in Futures Markets. Journal of Futures Markets, 11, 613-621. http://dx.doi.org/10.1002/fut.3990110509

[6]   Sharma, J.L., Mougoue, M. and Kamath, R. (1996) Heteroscedasticity in Stock Market Indicator Return Data: Volume versus GARCH Effects. Applied Financial Economics, 6, 337-342.

[7]   Hansen, P.R. and Lunde, A. (2006) Realized Variance and Market Microstructure Noise. Journal of Business & Economic Statistics, 24, 127-161. http://dx.doi.org/10.1198/073500106000000071

[8]   Andersen, T.G., Bollerslev, T., Diebold, F.X., et al. (2001) The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 61, 43-76. http://dx.doi.org/10.1016/S0304-405X(01)00055-1

[9]   Koopman, S.J., Jungbacker, B. and Hol, E. (2005) Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements. Journal of Empirical Finance, 12, 445-475.

[10]   孙便霞, 西村友作 (2012) 沪深300股指期货的日内动态特征分析. 上海金融, 12, 80-83.

[11]   Andersen, T.G., Bollerslev, T. and Cai, J. (2000) Intraday and Interday Volatility in the Japanese Stock Market. Journal of International Financial Markets, Institutions and Money, 10, 107-130. http://dx.doi.org/10.1016/S1042-4431(99)00029-3

[12]   Andersen, T.G. and Bollerslev, T. (1998) Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review, 885-905.

[13]   Tse, Y.K. (1998) The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate. Journal of Applied Econometrics, 13, 49-55. http://dx.doi.org/10.1002/(SICI)1099-1255(199801/02)13:1<49::AID-JAE459>3.0.CO;2-O

[14]   Ding, Z. and Granger, C.W.J. (1996) Modeling Volatility Persistence of Speculative Returns: A New Approach. Journal of Econometrics, 73, 185-215. http://dx.doi.org/10.1016/0304-4076(95)01737-2

 
 
Top