Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review
Stavros Degiannakis and
Evdokia Xekalaki
MPRA Paper from University Library of Munich, Germany
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. (search for similar items in EconPapers)
JEL-codes: C1 C10 C2 C3 C4 C5 (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)
Published in Quality Technology and Quantitative Management 2.1(2004): pp. 271-324
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/80487/1/MPRA_paper_80487.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/96331/1/MPRA_paper_80487.pdf revised version (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:80487
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().