Template-type: ReDIF-Paper 1.0
Author-Name: Guorui Bian
Author-Workplace-Name: Department of Statistics East China Normal University.
Author-Name: Michael McAleer
Author-Person: pmc90 
Author-Workplace-Name: Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute,
	The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of
	Economic Research, Kyoto University. 
Author-Name: Wing-Keung Wong
Author-Workplace-Name: Department of Economics, Hong Kong Baptist University.
Title: Robust Estimation and Forecasting of the Capital Asset Pricing Model
Abstract: In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear 
	regression model with underlying student t distribution. We obtain the closed form of the estimators, 
	derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for 
	estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least 
	squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that 
	the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead 
	forecast mean square error in small samples.
Classification-JEL: C1, C2, G1.
Keywords: Maximum likelihood estimators; Modified maximum likelihood estimators; Student t family; Capital 
	asset pricing model; Robustness.
Note: The third author would like to thank Robert B. Miller and Howard E. Thompson for their continuous guidance 
	and encouragement. For financial support, the first author is grateful to East China Normal University, 
	the second author acknowledges the Australian Research Council, National Science Council, Taiwan, and the 
	Japan Society for the Promotion of Science, and the third author wishes to acknowledge Hong Kong Baptist 
	University.
Length: 23 pages 
Creation-Date: 2012 
Revision-Date: 2012-04 
Number: 2012-09 
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1209.txt
File-URL: https://eprints.ucm.es/id/eprint/15059/1/1209.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1209