A reality check on the GARCH-MIDAS volatility models
(2024) In European Journal of Finance 30(6). p.575-596- 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 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.
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https://lup.lub.lu.se/record/db6b7048-2a23-46c0-a8ad-07a17d701d6f
- author
- Virk, Nader ; Javed, Farrukh LU ; Awartani, Basel and Hyde, Stuart
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- component variance forecasts, data snooping, Forecasting, GARCH-MIDAS models, macro-variables
- in
- European Journal of Finance
- volume
- 30
- issue
- 6
- pages
- 22 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85161443132
- ISSN
- 1351-847X
- DOI
- 10.1080/1351847X.2023.2217220
- language
- English
- LU publication?
- yes
- id
- db6b7048-2a23-46c0-a8ad-07a17d701d6f
- date added to LUP
- 2023-08-23 08:58:32
- date last changed
- 2024-07-17 12:44:38
@article{db6b7048-2a23-46c0-a8ad-07a17d701d6f, abstract = {{<p>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.</p>}}, author = {{Virk, Nader and Javed, Farrukh and Awartani, Basel and Hyde, Stuart}}, issn = {{1351-847X}}, keywords = {{component variance forecasts; data snooping; Forecasting; GARCH-MIDAS models; macro-variables}}, language = {{eng}}, number = {{6}}, pages = {{575--596}}, publisher = {{Taylor & Francis}}, series = {{European Journal of Finance}}, title = {{A reality check on the GARCH-MIDAS volatility models}}, url = {{http://dx.doi.org/10.1080/1351847X.2023.2217220}}, doi = {{10.1080/1351847X.2023.2217220}}, volume = {{30}}, year = {{2024}}, }