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Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes

Taras Bodnar and Nikolaus Hautsch ()

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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 disentangling (multivariate) dynamics in higher order moments. To capture the latter, we propose 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.

Keywords: multiplicative error model; trading processes; copula; DCC-GARCH; liquidity risk (search for similar items in EconPapers)
JEL-codes: C32 C46 C58 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2012-07
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-rmg
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Working Paper: Copula-based dynamic conditional correlation multiplicative error processes (2013) Downloads
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