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Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model

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  • Hautsch, Nikolaus

Abstract

We introduce a multivariate multiplicative error model which is driven by componentspecific 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 highfrequency 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. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2007/25.

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Date of creation: 2007
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Handle: RePEc:zbw:cfswop:200725

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Keywords: Multiplicative Error Models; Common Factor; Efficient Importance Sampling; Intraday Trading Process;

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References

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Citations

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Cited by:
  1. Nikolaus Hautsch & Dieter Hess & David Veredas, 2011. "The impact of macroeconomic news on quote adjustments, noise and informational volatility," ULB Institutional Repository 2013/136190, ULB -- Universite Libre de Bruxelles.
  2. Taras Bodnar & Nikolaus Hautsch, 2012. "Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes," SFB 649 Discussion Papers SFB649DP2012-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," CFS Working Paper Series 2009/31, Center for Financial Studies (CFS).
  4. Fei Chen & Francis X. Diebold & Frank Schorfheide, 2012. "A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities," NBER Working Papers 18078, National Bureau of Economic Research, Inc.
  5. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
  6. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.

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