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Clustering Korean Stock Return Data Based on GARCH Model
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 Title & Authors
Clustering Korean Stock Return Data Based on GARCH Model
Park, Man-Sik; Kim, Na-Young; Kim, Hee-Young;
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 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.
 Keywords
Generalized autoregressive conditional heteroscedasticity;conditional variance;clustering analysis;
 Language
Korean
 Cited by
1.
붓스트랩 방법을 이용한 일반화 자기회귀 조건부 이분산모형에서의 조건부 분산 예측,김희영;박만식;

Communications for Statistical Applications and Methods, 2009. vol.16. 2, pp.287-297 crossref(new window)
2.
MCMC 방법을 이용한 ARMA-GARCH 모형에서의 예측 방법 연구,채화연;최보승;김기환;박유성;

응용통계연구, 2011. vol.24. 2, pp.293-305 crossref(new window)
1.
A Study for Forecasting Methods of ARMA-GARCH Model Using MCMC Approach, Korean Journal of Applied Statistics, 2011, 24, 2, 293  crossref(new windwow)
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