TY  - UNPD
A1  - Bodnar, Taras
A1  - Hautsch, Nikolaus
T1  - Copula-based dynamic conditional correlation multiplicative error processes : [Version 18 April 2013]
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2013,19
N2  - 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.
T3  - CFS working paper series - 2013, 19 
KW  - multiplicative error model
KW  - trading process
KW  - copula
KW  - DCC-GARCH
KW  - liquidity risk
Y1  - 2013
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32496
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-324967
IS  - Version 18 April 2013
PB  - Center for Financial Studies
CY  - Frankfurt, M.
ER  -