SwePub - search: WFRF:(Hautsch Nikolaus)

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1.	Bodnar, Taras, et al. (author) Dynamic Conditional Correlation Multiplicative Error Processes 2016 In: Journal of Empirical Finance. - : Elsevier BV. - 0927-5398 .- 1879-1727. ; 36, s. 41-67 Journal article (peer-reviewed)abstract We introduce a dynamic model for multivariate processes of (non-negative) high-frequency tradingvariables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model, we map the resulting residuals into aGaussian domain using a copula-type transformation. Based on high-frequency volatility, cumulativetrading volumes, trade counts and market depth of various stocks traded at the NYSE, we show thatthe proposed transformation is supported by the data and allows capturing (multivariate) dynamicsin higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficientlyflexible to be applicablein high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in tradingprocesses supports the usefulness of the approach. Taking these higher-order dynamics explicitlyinto account significantly improves the goodness-of-fit and out-of-sample forecasts of the multiplicative error model.

Bodnar, Taras, et al. (author)
Dynamic Conditional Correlation Multiplicative Error Processes
2016
In: Journal of Empirical Finance. - : Elsevier BV. - 0927-5398 .- 1879-1727. ; 36, s. 41-67
Journal article (peer-reviewed)abstract
- We introduce a dynamic model for multivariate processes of (non-negative) high-frequency tradingvariables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model, we map the resulting residuals into aGaussian domain using a copula-type transformation. Based on high-frequency volatility, cumulativetrading volumes, trade counts and market depth of various stocks traded at the NYSE, we show thatthe proposed transformation is supported by the data and allows capturing (multivariate) dynamicsin higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficientlyflexible to be applicablein high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in tradingprocesses supports the usefulness of the approach. Taking these higher-order dynamics explicitlyinto account significantly improves the goodness-of-fit and out-of-sample forecasts of the multiplicative error model.

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