Regime-dependent Characteristics of KOSPI Return

Kim, Woohwan;Bang, Seungbeom

  • 투고 : 2014.08.14
  • 심사 : 2014.11.11
  • 발행 : 2014.11.30


Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from $4^{th}$ January 2000 to $30^{th}$ June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).


Finite mixture distribution;regime switching GJR-GARCH model;financial crisis;KOSPI


  1. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B, 39, 1-38.
  2. Ardia, D. and Mullen, K. (2010). DEoptim: Differential evolution optimization in R. R package version 2.0-4, URL
  3. Bauwens, L., Preminger, A. and Rombouts, J. V. K. (2012). Theory and inference for a Markov switching GARCH model, Econometrics Journal, 13, 218-244.
  4. Behr, A. and Potter, U. (2009). Alternatives to the normal model of stock returns: Gaussian mixture generalised logF and generalised hyperbolic models, Annals of Finance, 5, 49-68.
  5. Cai, J. (1994). A Markov model of unconditional variance in ARCH, Journal of Business and Economic Statistics, 12, 309-316.
  6. Glosten, L. R., Jagannathan, 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, 48, 1779-1801.
  7. Gray, S. (1996). Modelling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, 42, 27-62.
  8. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica, 57, 357-384.
  9. Hamilton, J. D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime, Journal of Econometrics, 64, 307-333.
  10. Henry, O. T. (2009). Regime switching in the relationship between equity returns and short-term interest rates in the UK, Journal of Banking and Finance, 33, 405-414.
  11. Haas, M., Mittnik, S. and Paollela, M. S. (2004). A new approach in Markov-switching GARCH models, Journal Of Financial Econometrics, 2, 493-530.
  12. Klaassen, F. (2002). Improving GARCH volatility forecasts, Empirical Economics, 27, 363-394.
  13. Marcucci, J. (2005) Forecasting stock market volatility with regime-switching GARCH models, Studies in Nonlinear Dynamics & Econometrics, 9, 1-6.
  14. Schwert, G. W. (1989). Why does stock market volatility change over time?, Journal of Finance, 44, 1115-1153.
  15. Wilfling, B. (2009). Volatility regime-switching in European exchange rates prior to monetary unification, Journal of International Money and Finance, 28, 240-270.

피인용 문헌

  1. The GARCH-GPD in market risks modeling: An empirical exposition on KOSPI vol.27, pp.6, 2016,