Arbeitspapier

Copula-based dynamic conditional correlation multiplicative error processes

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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.

Language: Englisch

Bibliographic citation: Series: CFS Working Paper ; No. 2013/19

Classification: Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Financial Econometrics
Specific Distributions; Specific Statistics

Subject: multiplicative error model
trading processes
copula
DCC-GARCH
liquidity risk

Event: Geistige Schöpfung

(who): Bodnar, Taras
Hautsch, Nikolaus

Event: Veröffentlichung

(who): Goethe University Frankfurt, Center for Financial Studies (CFS)

(where): Frankfurt a. M.

(when): 2013

Handle: http://hdl.handle.net/10419/87704

URN: urn:nbn:de:hebis:30:3-324967

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

Bodnar, Taras
Hautsch, Nikolaus
Goethe University Frankfurt, Center for Financial Studies (CFS)

Time of origin

2013

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