VS-LTGARCHX: A Flexible Subset Selection Approach for Estimation of log-TGARCHX Models and Its Application to BTC Markets

Victor Elvira (), Samir Orujov, Audrey Poterie (), Farid Rajabov and Francois Septier ()
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Victor Elvira: The University of Edinburgh
Samir Orujov: LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO - Université de Brest - CNRS - Centre National de la Recherche Scientifique
Audrey Poterie: LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO - Université de Brest - CNRS - Centre National de la Recherche Scientifique
Farid Rajabov: UCL - University College of London [London]
Francois Septier: LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO - Université de Brest - CNRS - Centre National de la Recherche Scientifique

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Abstract: The log-TGARCHX model is less restrictive in terms of inclusion of exogenous variables and asymmetry lags compared to the GARCHX model. However, adding less (more) covariates than necessary may lead to underfitting (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 the specified benchmark models in one-step-ahead forecasting.

Keywords: GARCHX log-GARCH Variable Selection Bitcoin Volatility; GARCHX; log-GARCH; Variable Selection; Bitcoin Volatility (search for similar items in EconPapers)
Date: 2023-11-13
Note: View the original document on HAL open archive server: https://hal.science/hal-04283159v2
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