Publication: Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones
Loading...
Identifiers
Publication date
2020-11-05
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
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.
Description
Keywords
Volatility, Risk Premium, Dynamic Conditional Score, Generalized Autoregressive Score