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

Author

Listed:
  • Blazsek Szabolcs
  • Licht Adrian

    (School of Business, Universidad Francisco Marroquín, Guatemala City, 01010, Guatemala)

  • Escribano Alvaro

    (Department of Economics, Universidad Carlos III de Madrid, Getafe, 28903, Spain)

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.

Suggested Citation

  • Blazsek Szabolcs & Licht Adrian & Escribano Alvaro, 2024. "Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(1), pages 61-82, February.
    • Handle: RePEc:bpj:sndecm:v:28:y:2024:i:1:p:61-82:n:7
    DOI: 10.1515/snde-2021-0083
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