Intraday Periodicity and Long Memory Volatility in Hong Kong Stock Market

Abstract Full-Text HTML XML Download Download as PDF (Size:418KB) PP. 61-66
DOI: 10.4236/jss.2015.37011    2,906 Downloads   3,399 Views  

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.