Autoregressive Conditional Heteroscedasticity (Arch) Models: A Review

Quality Technology and Quantitative Management, Vol. 1, No. 2, pp. 271-324, 2004

78 Pages Posted: 12 Sep 2005

See all articles by Stavros Antonios Degiannakis

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences

Evdokia Xekalaki

Athens University of Economics and Business

Abstract

Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

Keywords: ARCH models, Forecast Volatility.

Suggested Citation

Degiannakis, Stavros Antonios and Xekalaki, Evdokia, Autoregressive Conditional Heteroscedasticity (Arch) Models: A Review. Quality Technology and Quantitative Management, Vol. 1, No. 2, pp. 271-324, 2004, Available at SSRN: https://ssrn.com/abstract=798432

Stavros Antonios Degiannakis (Contact Author)

Department of Economic and Regional Development, Panteion University of Political and Social Sciences ( email )

136 Sygrou
Athens
Greece

Evdokia Xekalaki

Athens University of Economics and Business ( email )

76 Patission Street
GR-10434 Athens
Greece

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