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

publication date

  • January 2022


  • 2022

International Standard Serial Number (ISSN)

  • 1081-1826

Electronic International Standard Serial Number (EISSN)

  • 1558-3708


  • 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


  • Economics
  • Statistics


  • dynamic conditional score (dcs); expected return; generalized autoregressive score (gas); maximum likelihood (ml) conditions for score-driven models; volatility forecasting