The City College of New YorkCCNY
Department of Mathematics
Division of Science

Change-point detection for financial time series models

Mathematics Colloquium

Time and place

1 PM on Tuesday, March 31st, 2009; NAC 1/511E

Dr. В Siegfried Hoermann (University of Utah, Salt Lake City)

Abstract

Many standard methods in time series analysis work

under the assumption that the data investigated have been sampled from some weakly

stationary process. Especially, this is a crucial requirement in order that we can

produce meaningful estimates and reliable forecasts. It is therefore of considerable interest

to detect structural breaks in the data before any other statistical analysis is carried

out. In order to apply common procedures of change-point analysis we first have to understand

the dependence and dynamics of the data generating processes. The purpose of this talk

is to have a closer look at the structure of several financial time series models and to show

how these results can be used to identify breaks in the volatilities of univariate and

cross-volatilities of multivariate sequences. We propose a non-parametric method and show that our

results are widely applicable, e.g. for augmented GARCH sequences,

multivariate ARMA and dierent multivariate GARCH models

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