The Study of Bayesian Clustering of Generalized Autoregressive Conditional Heteroscedasitcity (일반화 자기회귀 조건부 이분산 모형들의 베이지안 군집분석에 관한 연구: 주식자료에 응용)

Hee Kyung Yoo (유희경); 정수정; Sung Kyung (성경)

The Study of Bayesian Clustering of Generalized Autoregressive Conditional Heteroscedasitcity

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2012, 7(6), pp.245~254
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Published : December 31, 2012

Hee Kyung Yoo ¹, 정수정 ², Sung Kyung ³

¹강원대학교
²(주)시벅스테크놀러지
³목원대학교

Accredited

ABSTRACT

There are many data on stocks in the stock market. They can be characterized by GARCH (,) models, specifically in this paper we assume that each stock data follows GARCH(1,1) model. This paper presents a model-based clustering method of stock data into several groups where each group has the same model and the same parameters. For the choosing the number of groups, we exploit the BIC (Bayesian Information Criterion). And the group parameters which present the characteristics of groups are estimated through Bayesian approach.

KEYWORDS

Regression, Bayesian, Cluster Analysis, Clustering, Autoregression

Citation status

* References for papers published after 2024 are currently being built.

[book] Ardia, D. / 2008 / Financial Risk Management with Bayesian Estimation of GARCH Model : theory and applications / Springer

[journal] Asai, M. / 2006 / Comparison of MCMC methods for Estimating GARCH models / Journal of the Japan Statistical Society 36(2) : 199~212

[journal] Banfield, J. / 1993 / Model-Based Gaussian and Non-Gaussian Clustering / Biometrics 49(3) : 803~821

[journal] Bauwens, L. / 1998 / Bayesian Inference on GARCH Models Using the Gibbs Sampler / The Econometrics Journal 1(1) : 23~46

[journal] Bauwens, L. / 2007 / Bayesian clustering of many GARCH models / Econometric Reviews 26 : 365~386

[book] Caiado, J. / 2007 / A GARCH-based method for clustering of financial time series: International stock markets evidence / Munich Personal RePEc Archive

[journal] Chris, F. / 2002 / Model-Based Clustering, Discriminant Analysis, and Density Estimation / Journal of the American Statistical Association 97 : 611~631

[book] Fruhwirth-Schnatter, S. / 2006 / Finite mixture and Markov switching models, Springer Series in Statistics / Springer

[report] Fruhwirth-Schnatter, S. / 2005 / Model-based Clustering of multiple time-series / Johannes Kepler Universitat Linz

[other] McLachlan, G. / 2000 / Finite Mixture Models / Wiley Series in Probability and Statistics

[journal] Nakatsuma, T. / 2000 / Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach / Journal of Econometrics 95(1) : 57~69

[journal] Otranto, E. / 2008 / Clustering Heteroskedastic Time Series by Model-Based Procedures / Computational Statistics & Data Analysis 52(10) : 4685~4698

[other] Pamminger, C. / 2008 / Bayesian clustering of categorical time series : an approach using finite mixtures of markov chain models / VDM Verlag Dr. Mueller

KJCKorea
Journal Central

Journal of Knowledge Information Technology and Systems KCI Impact Factor : 0.0