Modelling conditional heteroscedasticity in nonstationary series

Pavel Čížek
Additional contact information
Pavel Čížek: Tilburg University, Center for Economic Research

Chapter 3 in Statistical Tools for Finance and Insurance, 2011, pp 101-132 from Springer

Abstract: 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.

Keywords: Change Point; Forecast Error; Stock Index; GARCH Model; Stochastic Volatility Model (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-642-18062-0_3

Ordering information: This item can be ordered from
http://www.springer.com/9783642180620

DOI: 10.1007/978-3-642-18062-0_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().