COMPSTAT 2022: Start Registration
View Submission - COMPSTAT2022
Title: Linear regressions on time series Authors:  Francisco Blasques - VU University Amsterdam (Netherlands)
Sebastien Laurent - AMU (France)
Christian Francq - CREST and University Lille III (France) [presenting]
Abstract: A score-driven autoregressive conditional beta (ACB) model is introduced that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The time-varying betas are allowed to depend on past shocks and exogenous variables. We establish the existence of a stationary solution for the ACB model, the invertibility of the score-driven filter for the time-varying betas, and the asymptotic properties of one-step and multi-step Gaussian QMLEs for the new ACB model. The finite sample properties of these estimators are studied by means of an extensive Monte Carlo study. Finally, we also propose a strategy to test for the constancy of the conditional betas. In a financial application, we find evidence for time-varying conditional betas and highlight the empirical relevance of the ACB model in a portfolio and risk management empirical exercise.