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Erscheinungsjahr: 
2017
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 10 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-24
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
cointegration testing
high-frequency
stylized facts
conditional heteroskedasticity
smooth transition autoregressive models
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