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Clustering Korean Stock Return Data Based on GARCH Model

이분산 시계열모형을 이용한 국내주식자료의 군집분석

  • Park, Man-Sik (Department of Biostatistics & Department of Preventive Medicine, Medical Research Center for Environmental Toxico-Genomics and Proteomics, College of Medicine, Korea University) ;
  • Kim, Na-Young (Marketing Department, Private Banker Business Div. Samsung Securities Co., LTD.) ;
  • Kim, Hee-Young (Department of Biostatistics & Department of Preventive Medicine, Medical Research Center for Environmental Toxico-Genomics and Proteomics, College of Medicine, Korea University)
  • 박만식 (고려대학교 의과대학 의학통계학교실 및 의과학연구원(유전체 및 단백체 독성연구소)) ;
  • 김나영 (삼성증권 마케팅파트) ;
  • 김희영 (고려대학교 의과대학 의학통계학교실 및 의과학연구원(유전체 및 단백체 독성연구소))
  • Published : 2008.11.30

Abstract

In this study, we considered the clustering analysis for stock return traded in the stock market. Most of financial time-series data, for instance, stock price and exchange rate have conditional heterogeneous variability depending on time, and, hence, are not properly applied to the autoregressive moving-average(ARMA) model with assumption of constant variance. Moreover, the variability is font and center for stock investors as well as academic researchers. So, this paper focuses on the generalized autoregressive conditional heteroscedastic(GARCH) model which is known as a solution for capturing the conditional variance(or volatility). We define the metrics for similarity of unconditional volatility and for homogeneity of model structure, and, then, evaluate the performances of the metrics. In real application, we do clustering analysis in terms of volatility and structure with stock return of the 11 Korean companies measured for the latest three years.

References

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Cited by

  1. A Study for Forecasting Methods of ARMA-GARCH Model Using MCMC Approach vol.24, pp.2, 2011, https://doi.org/10.5351/KJAS.2011.24.2.293