RT Generic
T1 Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones
A1 Blazsek, Szabolcs
A1 Escribano, Álvaro
A1 Licht, Adrian
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB In 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.
SN 2340-5031
YR 2020
FD 2020-11-05
LK https://hdl.handle.net/10016/31339
UL https://hdl.handle.net/10016/31339
LA eng
DS e-Archivo
RD 16 jul. 2024