Abstract
This paper introduces an autoregressive conditional beta (ACB) model that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The model fits in the (quasi) score-driven approach recently proposed in the literature, and it is semi-parametric in the sense that the distributions of the innovations are not necessarily specified. 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 multistep 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.
| Original language | English |
|---|---|
| Article number | 105630 |
| Pages (from-to) | 1-22 |
| Number of pages | 22 |
| Journal | Journal of Econometrics |
| Volume | 238 |
| Issue number | 2 |
| Early online date | 14 Dec 2023 |
| DOIs | |
| Publication status | Published - Jan 2024 |
Bibliographical note
Funding Information:The authors gratefully acknowledge the editor, Torben Andersen, the associate editor and two anonymous referees for very useful comments on a previous version of this paper. We also thank Tim Bollerslev, Peter Reinhard Hansen, Andrew Harvey, Ilze Kalnina, Siem Jan Koopman and Julie Schnaitmann for helpful comments and discussions. Christian acknowledges the research support of the labex ECODEC while Sébastien acknowledges the research support of the French National Research Agency Grant ANR-17-EURE-0020 and the Excellence Initiative of Aix-Marseille University - A*MIDEX . Christian and Sébastien also acknowledge research support by the French National Research Agency Grant ANR-21-CE26-0007-01 for the their project MLEforRisk. Francisco Blasques acknowledges financial support of the Dutch Science Foundation (NWO) under the grant Vidi.195.099 .
Publisher Copyright:
© 2023 Elsevier B.V.
Funding
The authors gratefully acknowledge the editor, Torben Andersen, the associate editor and two anonymous referees for very useful comments on a previous version of this paper. We also thank Tim Bollerslev, Peter Reinhard Hansen, Andrew Harvey, Ilze Kalnina, Siem Jan Koopman and Julie Schnaitmann for helpful comments and discussions. Christian acknowledges the research support of the labex ECODEC while Sébastien acknowledges the research support of the French National Research Agency Grant ANR-17-EURE-0020 and the Excellence Initiative of Aix-Marseille University - A*MIDEX . Christian and Sébastien also acknowledge research support by the French National Research Agency Grant ANR-21-CE26-0007-01 for the their project MLEforRisk. Francisco Blasques acknowledges financial support of the Dutch Science Foundation (NWO) under the grant Vidi.195.099 .
Keywords
- Betas
- GARCH model
- Score driven model
- Time-varying parameters