Analyzing financial time series data using the GARCH model

일반 자기회귀 이분산 모형을 이용한 시계열 자료 분석

  • Kim, Sahm (Department of Statistics, Chung-Ang University) ;
  • Kim, Jin-A (Department of Statistics, Chung-Ang University)
  • Published : 2009.05.31


In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.


  1. Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307-327.
  2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1008.
  3. Glosten, L. R, Jegannathan, R. and Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess on stock. Journal of Finance, 48, 1779-1801.
  4. Kim, S. and Hwang, S. Y. (2005). Binary random power approach to modelling asymmetric conditional heteroscadasticity. Journal of the Korean Statistical Society, 1, 61-71.
  5. Kim, S. Y. and Chong, T. S. (2005). An estimating function approach for threshold-ARCH models. Journal of the Korean Data & Information Science Society, 16, 33-40.
  6. Kim, S. Y., Lee, S. D. and Jeong A. R. (2005). On asymmeticity for power transformaed TARGH model. Journal of the Korean Data & Information Science Society, 16, 271-281.
  7. Li, C. W. and Li, W. K. (1996). On a double-threshold autoregressive heteroscedastic time series model. Journal of Applied Economics, 11, 253-274.<253::AID-JAE393>3.0.CO;2-8
  8. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347-370.
  9. Zakoian, J. M. (1993). Threshold ARCH models and asymmetries in volatility. Joutnal of Applied. Econometrics, 8, 31-49.