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

Samir Orujov et al.

Journal of Time Series Econometrics2024https://doi.org/10.1515/jtse-2023-0035article
AJG 2ABDC B
Weight
0.30

What the paper says

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.

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https://doi.org/https://doi.org/10.1515/jtse-2023-0035

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@article{samir2024,
  title        = {{VS-LTGARCHX: A Flexible Variable Selection in Log-TGARCHX Models}},
  author       = {Samir Orujov et al.},
  journal      = {Journal of Time Series Econometrics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2023-0035},
}

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.