Arbeitspapier
A reality check on the GARCH-MIDAS volatility models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 2/2021
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
Portfolio Choice; Investment Decisions
Financial Forecasting and Simulation
- Thema
-
GARCH-MIDAS models
component variance forecasts
macro-variables
data snooping
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Virk, Nader
Javed, Farrukh
Awartani, Basel
- Ereignis
-
Veröffentlichung
- (wer)
-
Örebro University School of Business
- (wo)
-
Örebro
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Virk, Nader
- Javed, Farrukh
- Awartani, Basel
- Örebro University School of Business
Entstanden
- 2021
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