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

Author

Listed:
  • Samir Orujov

    (LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO EPE - Université de Brest - CNRS - Centre National de la Recherche Scientifique)

  • Victor Elvira

    (The University of Edinburgh)

  • Audrey Poterie

    (LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO EPE - Université de Brest - CNRS - Centre National de la Recherche Scientifique)

  • Farid Rajabov

    (UCL - University College London [UCL])

  • Francois Septier

    (LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO EPE - Université de Brest - CNRS - Centre National de la Recherche Scientifique, UBS - Université de Bretagne Sud)

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.

Suggested Citation

  • Samir Orujov & Victor Elvira & Audrey Poterie & Farid Rajabov & Francois Septier, 2025. "VS-LTGARCHX: A Flexible Variable Selection in Log-TGARCHX Models," Post-Print hal-04283159, HAL.
    • Handle: RePEc:hal:journl:hal-04283159
    DOI: 10.1515/jtse-2023-0035
    Note: View the original document on HAL open archive server: https://hal.science/hal-04283159v3
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