Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/244577 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Working Paper No. 2/2021
Verlag: 
Örebro University School of Business, Örebro
Zusammenfassung: 
We employ a battery of model evaluation tests for a broad-set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gains of macro-variables in forecasting total (long run) variance by GM models are overstated (understated). Estimation of different components of volatility is crucial for designing differentiated investing strategies, risk management plans and pricing of derivative securities. Therefore, researchers and practitioners should be wary of data mining bias, which may contaminate a forecast that may appear statistically validated using robust evaluation tests.
Schlagwörter: 
GARCH-MIDAS models
component variance forecasts
macro-variables
data snooping
JEL: 
C32
C52
G11
G17
Dokumentart: 
Working Paper

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