Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105338
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dc.titleRobust estimation in capital asset pricing model
dc.contributor.authorWong, W.-K.
dc.contributor.authorBian, G.
dc.date.accessioned2014-10-28T05:14:47Z
dc.date.available2014-10-28T05:14:47Z
dc.date.issued2000
dc.identifier.citationWong, W.-K.,Bian, G. (2000). Robust estimation in capital asset pricing model. Journal of Applied Mathematics and Decision Sciences 4 (1) : 65-82. ScholarBank@NUS Repository.
dc.identifier.issn11739126
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105338
dc.description.abstractBian and Dickey (1996) developed a robust Bayesian estimator for the vector of regression coefficients using a Cauchy-type g-prior. This estimator is an adaptive weighted average of the least squares estimator and the prior location, and is of great robustness with respect to flat-tailed sample distribution. In this paper, we introduce the robust Bayesian estimator to the estimation of the Capital Asset Pricing Model (CAPM) in which the distribution of the error component is well-known to be flat-tailed. To support our proposal, we apply both the robust Bayesian estimator and the least squares estimator in the simulation of the CAPM and in the analysis of the CAPM for US annual and monthly stock returns. Our simulation results show that the Bayesian estimator is robust and superior to the least squares estimator when the CAPM is contaminated by large normal and/or non-normal disturbances, especially by Cauchy disturbances. In our empirical study, we find that the robust Bayesian estimate is uniformly more efficient than the least squares estimate in terms of the relative efficiency of one-step ahead forecast mean square error, especially for small samples. © Journal of Applied Mathematics & Decision Sciences.
dc.sourceScopus
dc.subjectBayesian Estimate
dc.subjectCapital Asset Pricing Model
dc.subjectCauchy-type g-prior
dc.subjectFlat-tailed Distribution
dc.subjectLeast Squares Estimate
dc.subjectRobustness
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleJournal of Applied Mathematics and Decision Sciences
dc.description.volume4
dc.description.issue1
dc.description.page65-82
dc.identifier.isiutNOT_IN_WOS
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