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An ACD-ECOGARCH(1,1) Model

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  • Claudia Czado
  • Stephan Haug

Abstract

In this paper we introduce an ACD-ECOGARCH(1,1) model. An exponential autoregressive conditional duration model is used to describe the dependence structure in durations of ultra-high-frequency financial data. The innovation process of the ACD model then defines the interarrival times of a compound Poisson process. We use this compound Poisson process as the background driving Lévy process of an exponential continuous time GARCH(1,1) process. The dynamics of the random time transformed log-price process are then described by the latter process. To estimate its parameters we construct a quasi maximum likelihood estimator under the assumption that all jumps of the log-price process are observable. Finally, the model is fitted for illustrative purpose to General Motors tick-by-tick data of the New York Stock Exchange. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oupjournals.org, Oxford University Press.

Suggested Citation

  • Claudia Czado & Stephan Haug, 2010. "An ACD-ECOGARCH(1,1) Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(3), pages 335-344, Summer.
  • Handle: RePEc:oup:jfinec:v:8:y:2010:i:3:p:335-344
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbp023
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    Cited by:

    1. Istvan Barra & Siem Jan Koopman & Agnieszka Borowska, 2016. "Bayesian Dynamic Modeling of High-Frequency Integer Price Changes," Tinbergen Institute Discussion Papers 16-028/III, Tinbergen Institute, revised 16 Feb 2018.

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