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garchx: Flexible and Robust GARCH-X Modelling

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  • Sucarrat, Genaro

Abstract

The R package garchx provides a user-friendly, fast, flexible and robust framework for the estimation and inference of GARCH(p,q,r)-X models, where p is the ARCH order, q is the GARCH order, r is the asymmetry or leverage order, and 'X' indicates that covariates can be included. Quasi Maximum Likelihood (QML) methods ensure estimates are consistent and standard errors valid, even when the standardised innovations are non-normal or dependent, or both. Zero-coefficient restrictions by omission enable parsimonious specifications, and functions to facilitate the non-standard inference associated with zero-restrictions in the null-hypothesis are provided. Finally, in formal comparisons of precision and speed, the garchx package performs well relative to other prominent GARCH-packages on CRAN.

Suggested Citation

  • Sucarrat, Genaro, 2020. "garchx: Flexible and Robust GARCH-X Modelling," MPRA Paper 100301, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:100301
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    References listed on IDEAS

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    2. Escanciano, Juan Carlos, 2009. "Quasi-Maximum Likelihood Estimation Of Semi-Strong Garch Models," Econometric Theory, Cambridge University Press, vol. 25(2), pages 561-570, April.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    7. Peter Reinhard Hansen & Zhuo Huang & Howard Howan Shek, 2012. "Realized GARCH: a joint model for returns and realized measures of volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 877-906, September.
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    Cited by:

    1. Werge, Nicklas & Wintenberger, Olivier, 2022. "AdaVol: An Adaptive Recursive Volatility Prediction Method," Econometrics and Statistics, Elsevier, vol. 23(C), pages 19-35.
    2. Kejin Wu & Sayar Karmakar & Rangan Gupta, 2023. "GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables," Papers 2308.13346, arXiv.org, revised Sep 2024.
    3. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2021. "Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model," Econometrics and Statistics, Elsevier, vol. 20(C), pages 12-28.

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    More about this item

    Keywords

    Volatility; GARCH; covariates; robust; R;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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