Copula-based dynamic conditional correlation multiplicative error processes

Taras Bodnar and Nikolaus Hautsch

No 2013/19, CFS Working Paper Series from Center for Financial Studies (CFS)

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.

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)
Date: 2013
New Economics Papers: this item is included in nep-ets
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Working Paper: Copula-based dynamic conditional correlation multiplicative error processes (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:201319

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