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On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts

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  • Christopher Krauss

    (University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany)

  • Klaus Herrmann

    (University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany)

Abstract

This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and MR(3)-STAR(1)-GARCH(1,1) processes contaminated with reversible and non-reversible jumps are used to model the cointegration relationship. In a Monte Carlo simulation, the power and size properties of ten cointegration tests are assessed. We find that in high-frequency settings typical for stock price data, power is still acceptable, with the exception of strong or very frequent non-reversible jumps. Phillips–Perron and PGFF tests perform best.

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  • Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," JRFM, MDPI, vol. 10(1), pages 1-24, February.
    • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:1:p:7-:d:89525
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    2. Osabuohien-Irabor Osarumwense & Julian I. Mbegbu, 2017. "Power and Size analysis of Co-integration tests in Conditional Heteroskedascity: A Monte Carlo Simulation," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 17-34, September.
    3. Stübinger, Johannes & Walter, Dominik & Knoll, Julian, 2017. "Financial market predictions with Factorization Machines: Trading the opening hour based on overnight social media data," FAU Discussion Papers in Economics 19/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Johannes St binger & Jens Bredthauer, 2017. "Statistical Arbitrage Pairs Trading with High-frequency Data," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 650-662.
    5. Clegg, Matthew & Krauss, Christopher & Rende, Jonas, 2017. "partialCI: An R package for the analysis of partially cointegrated time series," FAU Discussion Papers in Economics 05/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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