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Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility

  • Szabolcs Blazsek , Alvaro Escribano EMAIL logo and Adrian Licht

Abstract

We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.


Corresponding author: Alvaro Escribano, Department of Economics, Universidad Carlos III de Madrid, Getafe 28903, Spain, E-mail:

Funding source: Universidad Francisco Marroquin

Funding source: Agencia Estatal de Investigacion

Award Identifier / Grant number: 2019/00419/001

Funding source: Comunidad de Madrid

Award Identifier / Grant number: MadEco-CM S2015/HUM-3444

Funding source: Ministerio de Economia, industria y Competitividad

Award Identifier / Grant number: ECO2016-00105-001 and MD 2014-0431

Acknowledgments

A previous version of this paper was presented at the MKE Annual Conference (December 2020, Budapest). We thank the helpful comments and suggestions of Robert Lieli, Gabor Korosi, and conference participants. The authors are also thankful for the comments of Matthew Copley and the anonymous reviewer. All remaining errors are our own.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: Blazsek and Licht acknowledge funding from Universidad Francisco Marroquin. Escribano acknowledges funding from Ministerio de Economia, Industria y Competitividad (ECO2016-00105-001 and MDM 2014-0431), Comunidad de Madrid (MadEco-CMS2015/HUM-3444), and Agencia Estatal de Investigacion (2019/00419/001).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2021-0083).


Received: 2021-09-07
Accepted: 2022-02-16
Published Online: 2022-03-07

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