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A reality check on the GARCH-MIDAS volatility models

Virk, Nader ; Javed, Farrukh LU ; Awartani, Basel and Hyde, Stuart (2023) In European Journal of Finance
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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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
component variance forecasts, data snooping, Forecasting, GARCH-MIDAS models, macro-variables
in
European Journal of Finance
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
2023-08-23 08:58:32
@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}},
  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}},
  year         = {{2023}},
}