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

Posted: 1 Nov 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: October 30, 2008

Abstract

We model high-frequency trading processes by a multivariate multiplicative error model that is driven by component-specific observation driven dynamics as well as a common latent autoregressive factor. The model is estimated using efficient importance sampling techniques. Applying the model to five minute return volatilities, trade sizes and trading intensities from four liquid stocks traded at the NYSE, we show that a subordinated common process drives the individual components and captures a substantial part of the dynamics and cross-dependencies of the variables. Common shocks mainly affect the return volatility and the trade size. Moreover, we identify effects that capture rather genuine relationships between the individual trading variables.

Keywords: Multiplicative error model, common factor, efficient importance sampling, intra-day trading process

JEL Classification: C15, C32, C52

Suggested Citation

Hautsch, Nikolaus, Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model (October 30, 2008). Journal of Economic Dynamics and Control, Vol. 32, 2008. Available at SSRN: https://ssrn.com/abstract=1292498

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 )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

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