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

Szabolcs Blazsek, Alvaro Escribano and Licht Adrian
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Licht Adrian: School of Business, Universidad Francisco Marroquín, Guatemala City 01010, Guatemala

Studies in Nonlinear Dynamics & Econometrics, 2024, vol. 28, issue 1, 61-82

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.

Keywords: dynamic conditional score (DCS); expected return; generalized autoregressive score (GAS); maximum likelihood (ML) conditions for score-driven models; volatility forecasting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/snde-2021-0083

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