Blazsek, SzabolcsEscribano, ÁlvaroLicht, AdrianUniversidad Carlos III de Madrid. Departamento de Economía2020-11-052020-11-052020-11-052340-5031https://hdl.handle.net/10016/31339In this paper, we introduce Beta-t-QVAR (quasi-vector autoregression) for the joint modelling of score-driven location and scale. Asymptotic theory of the maximum likelihood (ML) estimatoris presented, and sufficient conditions of consistency and asymptotic normality of ML are proven. Forthe joint score-driven modelling of risk premium and volatility, Dow Jones Industrial Average (DJIA)data are used in an empirical illustration. Prediction accuracy of Beta-t-QVAR is superior to theprediction accuracies of Beta-t-EGARCH (exponential generalized AR conditional heteroscedasticity),A-PARCH (asymmetric power ARCH), and GARCH (generalized ARCH). The empirical results motivate the use of Beta-t-QVAR for the valuation of DJIA options.engAtribución-NoComercial-SinDerivadas 3.0 EspañaVolatilityRisk PremiumDynamic Conditional ScoreGeneralized Autoregressive Scoreworking paperC22C58DT/0000001850