Time inhomogeneous multiple volatility modelling
AbstractPrice variations observed at speculative markets exhibit positive autocorrelation and cross correlation among a set of assets, stock market indices, exchange rates etc. A particular problem in investigating multivariate volatility processes arises from the high dimensionality implied by a simultaneous analysis of variances and covariances. Parametric volatility models as e.g. the multivariate version of the prominent GARCH model become easily intractable für empirical work. We propose an adaptive procedure that aims to identify periods of second order homogeneity for each moment in time. Similar to principal component analysis the dimensionality problem is solved by transforming a multivariate series into a set of univariate processes. We discuss thoroughly implementation issues which naturally arise in the framework of adaptive modelling. Theoretical and Monte Carlo results are given. The empirical performance of the new method is illustrated by an application 1,0 a bivariate exchange rate series and a 23-dimensional system of asset returns. Empirical results of the FX~analysis are compared to a parametric approach, namely the multivariate GARCH model. --
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Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2001,7.
Date of creation: 2001
Date of revision:
stochastic volatility model; adaptive estimation; local homogeneity;
Other versions of this item:
- Wolfgang Haerdle & Helmut Herwartz & Volodia Spokoiny, 2000. "Time Inhomogeneous Multiple Volatility Modelling," Econometric Society World Congress 2000 Contributed Papers 1429, Econometric Society.
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