Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Regime-dependent Characteristics of KOSPI Return

  • Received : 2014.08.14
  • Accepted : 2014.11.11
  • Published : 2014.11.30
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Abstract

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).

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References

  1. Ardia, D. and Mullen, K. (2010). DEoptim: Differential evolution optimization in R. R package version 2.0-4, URL http://CRAN.R-project.org/package=DEoptim.
  2. Bauwens, L., Preminger, A. and Rombouts, J. V. K. (2012). Theory and inference for a Markov switching GARCH model, Econometrics Journal, 13, 218-244.
  3. 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. https://doi.org/10.1007/s10436-007-0089-8
  4. Cai, J. (1994). A Markov model of unconditional variance in ARCH, Journal of Business and Economic Statistics, 12, 309-316.
  5. 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.
  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. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  7. Gray, S. (1996). Modelling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, 42, 27-62. https://doi.org/10.1016/0304-405X(96)00875-6
  8. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica, 57, 357-384. https://doi.org/10.2307/1912559
  9. Hamilton, J. D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime, Journal of Econometrics, 64, 307-333. https://doi.org/10.1016/0304-4076(94)90067-1
  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. https://doi.org/10.1016/j.jbankfin.2008.08.001
  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. https://doi.org/10.1093/jjfinec/nbh020
  12. Klaassen, F. (2002). Improving GARCH volatility forecasts, Empirical Economics, 27, 363-394. https://doi.org/10.1007/s001810100100
  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. https://doi.org/10.1111/j.1540-6261.1989.tb02647.x
  15. Wilfling, B. (2009). Volatility regime-switching in European exchange rates prior to monetary unification, Journal of International Money and Finance, 28, 240-270. https://doi.org/10.1016/j.jimonfin.2008.08.005

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