Modelling conditional heteroscedasticity in nonstationary series

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of instability or uncertainty in last decades such as Asian crisis (1997), start of the European currency (1999), the “dot-Com” technology-bubble crash (2000–2002) or the terrorist attacks (September, 2001), the war in Iraq (2003) and the current global recession (2008–2009), mostly used econometric models are based on the assumption of stationarity and time homogeneity; in other words, structure and parameters of a model are supposed to be constant over time. This includes linear and nonlinear autoregressive (AR) and moving-average models and conditional heteroscedasticity (CH) models such as ARCH (Engel, 1982) and GARCH (Bollerslev, 1986), stochastic volatility models (Taylor, 1986), as well as their combinations.
Original languageEnglish
Title of host publicationStatistical Tools for Finance and Insurance, Second Edition
EditorsP. Cizek, W.K. Härdle, R. Weron
Place of PublicationHeidelberg
PublisherSpringer Verlag
Pages101-132
ISBN (Print)9783642180
Publication statusPublished - 2011

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