VS-LTGARCHX: A Flexible Variable Selection in Log-TGARCHX Models

Orujov Samir (), Elvira Victor, Poterie Audrey, Rajabov Farid and Septier Francois
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Orujov Samir: Université Bretagne Sud, UMR CNRS 6205, LMBA, F-56000 Vannes, France
Elvira Victor: School of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, UK
Poterie Audrey: Université Bretagne Sud, UMR CNRS 6205, LMBA, F-56000 Vannes, France
Rajabov Farid: Institute of Finance and Technology, University College London, London WC1E 6BT, UK
Septier Francois: Université Bretagne Sud, UMR CNRS 6205, LMBA, F-56000 Vannes, France

Journal of Time Series Econometrics, 2025, vol. 17, issue 1, 1-34

Abstract: The log-TGARCHX model is less restrictive in terms of the inclusion of exogenous variables and asymmetry lags compared to the GARCHX model. Nevertheless, adding less (or more) covariates than necessary may lead to under- or overfitting, respectively. In this context, we propose a new algorithm, called VS-LTGARCHX, which incorporates a variable selection procedure into the log-TGARCHX estimation process. Furthermore, the VS-LTGARCHX algorithm is applied to extremely volatile BTC markets using 42 conditioning variables. Interestingly, our results show that the VS-LTGARCHX models outperform benchmark models, namely the log-GARCH(1,1) and log-TGARCHX(1,1) models, in one-step-ahead forecasting.

Keywords: GARCH; log-GARCHX; variable selection; Bitcoin volatility (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 C65 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/jtse-2023-0035

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