Document Type

Journal Article

Department/Unit

Department of Economics

Abstract

Bian 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 at-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.

Publication Year

2000

Journal Title

Journal of Applied Mathematics and Decision Sciences

Volume number

4

Issue number

1

Publisher

Journal of Applied Mathematics and Decision Sciences

First Page (page number)

65

Last Page (page number)

82

Referreed

1

DOI

10.1155/S1173912600000043

ISSN (print)

2090-3359

Link to Publisher’s Edition

http://dx.doi.org/10.1155/S1173912600000043

Keywords

Robustness, Bayesian Estimate, Least Squares Estimate, Cauchy-type g-prior, Flattailed Distribution, Capital Asset Pricing Model

Included in

Economics Commons

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