Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87704
Authors: 
Bodnar, Taras
Hautsch, Nikolaus
Year of Publication: 
2013
Series/Report no.: 
CFS Working Paper 2013/19
Abstract: 
We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks.
Subjects: 
multiplicative error model
trading processes
copula
DCC-GARCH
liquidity risk
JEL: 
C32
C58
C46
Document Type: 
Working Paper

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