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Dynamic Conditional Correlation Multiplicative Error Processes
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- Bodnar, Taras (author)
- Stockholms universitet,Matematiska institutionen
- Hautsch, Nikolaus (author)
- (creator_code:org_t)
- Elsevier BV, 2016
- 2016
- English.
- In: Journal of Empirical Finance. - : Elsevier BV. - 0927-5398 .- 1879-1727. ; 36, s. 41-67
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business (hsv//eng)
Keyword
- Multiplicative error model
- Trading processes
- Gaussian domain
- DCC-GARCH
- Liquidity risk
- Statistics
- statistik
Publication and Content Type
- ref (subject category)
- art (subject category)
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