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Modelling conditional heteroscedasticity in nonstationary series

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Statistical Tools for Finance and Insurance

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.

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Author information

Authors and Affiliations

  1. Center for Economic Research, Tilburg University, Tilburg, Netherlands

    Pavel Čížek

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  1. Pavel Čížek
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Editors and Affiliations

  1. Fac. Economics & Business Administration, Dept. Econometrics &, Tilburg University, Tilburg, 5000, Netherlands

    Pavel Cizek

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. , Institute of Organization and, Wrocław University of Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland

    Rafał Weron

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Čížek, P. (2011). Modelling conditional heteroscedasticity in nonstationary series. In: Cizek, P., Härdle, W., Weron, R. (eds) Statistical Tools for Finance and Insurance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18062-0_3

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