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Farrukh Javed
Universitetslektor
![Farrukh Javed . Foto](/sites/ehl.lu.se/files/styles/lu_personal_page_desktop/public/2024-05/FarrukhJaved.jpg.webp?itok=3VDO0DSD)
A reality check on the GARCH-MIDAS volatility models
Författare
Summary, in English
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 gain of macro-variables in forecasting total (long-run) variance by GM models is overstated (understated). Estimation of different components of volatility is crucial for designing differentiated investing strategies, risk management plans and pricing 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.
Avdelning/ar
- Statistiska institutionen
Publiceringsår
2023
Språk
Engelska
Publikation/Tidskrift/Serie
European Journal of Finance
Dokumenttyp
Artikel i tidskrift
Förlag
Taylor & Francis
Ämne
- Probability Theory and Statistics
Nyckelord
- component variance forecasts
- data snooping
- Forecasting
- GARCH-MIDAS models
- macro-variables
Aktiv
Inpress
ISBN/ISSN/Övrigt
- ISSN: 1351-847X