Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model

CRC Discussion Paper No. 649

Posted: 6 Aug 2008

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

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Date Written: August 5, 2008

Abstract

We introduce a multivariate multiplicative error model which is driven by component-specific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects in the processes of high-frequency return volatilities, trade sizes and trading intensities. The model is estimated by simulated maximum likelihood using efficient importance sampling. Analyzing five minutes data from four liquid stocks traded at the New York Stock Exchange, we find that volatilities, volumes and intensities are driven by idiosyncratic dynamics as well as a highly persistent common factor capturing most causal relations and cross-dependencies between the individual variables. This confirms economic theory and suggests more parsimonious specifications of high-dimensional trading processes. It turns out that common shocks affect the return volatility and the trading volume rather than the trading intensity.

Suggested Citation

Hautsch, Nikolaus, Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model (August 5, 2008). CRC Discussion Paper No. 649. Available at SSRN: https://ssrn.com/abstract=1206022

Nikolaus Hautsch (Contact Author)

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

GrĂ¼neburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

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