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Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model

Nikolaus Hautsch ()

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: We introduce a multivariate multiplicative error model which is driven by component- specific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects in the processes of high-frequency return volatilities, trade sizes and trading intensities. The model is estimated by simulated maximum likelihood using efficient importance sampling. Analyzing five minutes data from four liquid stocks traded at the New York Stock Exchange, we find that volatilities, volumes and intensities are driven by idiosyncratic dynamics as well as a highly persistent common factor capturing most causal relations and cross-dependencies between the individual variables. This confirms economic theory and suggests more parsimonious specifications of high-dimensional trading processes. It turns out that common shocks affect the return volatility and the trading volume rather than the trading intensity.

Keywords: Multiplicative error models; common factor; efficient importance sampling; intraday trading process. (search for similar items in EconPapers)
JEL-codes: C15 C32 C52 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2007-09
New Economics Papers: this item is included in nep-ets and nep-mst
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model (2008) Downloads
Working Paper: Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model (2007) Downloads
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