Show simple item record

dc.contributor.authorBailer, Heiko Manfreden_US
dc.date.accessioned2009-10-06T22:54:55Z
dc.date.available2009-10-06T22:54:55Z
dc.date.issued2005en_US
dc.identifier.otherb5432905xen_US
dc.identifier.other64202942en_US
dc.identifier.otherThesis 55111en_US
dc.identifier.urihttp://hdl.handle.net/1773/8985
dc.descriptionThesis (Ph. D.)--University of Washington, 2005.en_US
dc.description.abstractStandard asset-pricing models entail expressions for expected returns in terms of coefficients relative to risk factors. Methods to estimate premiums of risk factors have, at its core, a single or multiple linear regression models. Ordinary least squares (OLS) estimation is the common choice. However, it is well established that financial returns are heavy-tailed, skewed, and vary over time. This dissertation shows that small fractions of outlying observations bias OLS estimates and inflate its variability. Outlying observations include months, firms, time periods, and gross errors. Some subset of outlying firms may have some economic value, which leads to a great fear of simply rejecting them. This dissertation uses exploratory data analysis and the robust MM-estimator to separate influential observations from the bulk of the data and to estimate risk premiums on both groups. The key results are: OLS alphas from the single-factor market model are often over-estimated due to outliers and positive asymmetry of the returns distribution. OLS betas are highly sensitive to outliers. Robust alphas and betas are superior in predicting future returns and risk, and are insensitive to the choice of returns type and returns that are dirty, e.g. not split or dividend adjusted.The risk premium as found by Fama & French (1992) to be flat for beta and negative for size is a small size firm and seasonality effect. The risk premiums for beta (size) are positive (negative) only in January and for a tiny number of influential small size firms. Once adjusted the beta (size) risk premiums become negative (positive), confirming partial results of Knez & Ready (1997). The seasonality effect appears to be small compared to influential firm effect, only since seasonal effects average out. The January effect is significant and spills over into February and March; in addition, size shows seasonal and book-to-market quarterly variability. Overall the MM-estimator is shown to be not only an easy-to-use alternative to the OLS estimator, unbiased towards small fractions of unusual observations, but also a tool that can be used to identify and analyze influential observations and to find trading strategies.en_US
dc.format.extentix, 171 p.en_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.rights.uriFor information on access and permissions, please see http://digital.lib.washington.edu/rw-faq/rights.htmlen_US
dc.subject.otherTheses--Statisticsen_US
dc.titleRobust estimation of factor models in financeen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record