Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25526
Authors: 
Hautsch, Nikolaus
Year of Publication: 
2007
Series/Report no.: 
CFS Working Paper 2007/25
Abstract: 
We introduce a multivariate multiplicative error model which is driven by componentspecific 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 highfrequency 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.
Subjects: 
Multiplicative Error Models
Common Factor
Efficient Importance Sampling
Intraday Trading Process
JEL: 
C15
C32
C52
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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