Abstract
dc:descriptionDynamic factor models for forecasting stock return covariance matrix. Factor models are quite popular for forecasting covariances as they reduce the impact of the idiosyncratic return component on forecasts based on the full sample covariance matrix. I examine empirically whether introducing factor dynamics brings any benefits in the context of forecasting the return covariance matrix. When dynamic factor models are used in a global minimum variance problem, the resulting portfolio has marginally lower volatility than when naive historical or static factor models are used. Slightly sharper results in favor of the dynamic factor models are obtained when looking for the minimum tracking error portfolio and when estimating VaR. However, the differences between dynamic and static factor models are not overwhelming.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Finance
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Serbin, Vitaly A.
- Contributors dc:contributor
-
- Lakonishok, Josef
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3030475
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/87427