A reality check on the GARCH-MIDAS volatility models

Nader Virk (), Farrukh Javed () and Basel Awartani ()
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Nader Virk: Plymouth Business School, Postal: Plymouth Business School, PL4 8AA Plymouth, United Kingdom
Farrukh Javed: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Basel Awartani: Westminster Business School, Postal: Westminster Business School, 35 Marylebone, London, NW15LS, United Kingdom, https://erf.org.eg/affiliates/basel-awartani/

No 2021:2, Working Papers from Örebro University, School of Business

Abstract: 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.

Keywords: GARCH-MIDAS models; component variance forecasts; macro-variables; data snooping (search for similar items in EconPapers)
JEL-codes: C32 C52 G11 G17 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2021-03-30
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
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