Dynamic conditional correlation multiplicative error processes
Taras Bodnar and
Nikolaus Hautsch ()
Journal of Empirical Finance, 2016, vol. 36, issue C, pages 41-67
We introduce a 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 copula-type transformation. 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 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 and out-of-sample forecasts of the multiplicative error model.
Keywords: Multiplicative error model; Trading processes; Gaussian domain; DCC-GARCH; Liquidity risk (search for similar items in EconPapers)
JEL-codes: C32 C58 C46 (search for similar items in EconPapers)
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Persistent link: http://EconPapers.repec.org/RePEc:eee:empfin:v:36:y:2016:i:c:p:41-67
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