Abstract
This paper explores two issues in beta estimation, specifically, time variation and thin trading. In a multivariate GARCH approach, the paper conducts an analysis of the importance of assumptions made about the correlation structure in the multivariate GARCH model. The results of Monte Carlo analysis and an empirical application to Australian stock data demonstrate that it is better to allow for time variation in the correlation structure. The paper then develops a selectivity corrected time varying beta estimator. The results of a Monte Carlo experiment show that the new estimator performs well in handling the censoring in the data. Further, when the model is applied to individual stock data for Australia it provides a model that captures the impacts of censoring and thin trading on time varying beta risk.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
