Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/178587
Authors: 
Krauss, Christopher
Herrmann, Klaus
Year of Publication: 
2017
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 10 [Year:] 2017 [Issue:] 1 [Pages:] 1-24
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.
Subjects: 
cointegration testing
high-frequency
stylized facts
conditional heteroskedasticity
smooth transition autoregressive models
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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