文章基本信息

标题：Pricing Ability of Four Factor Model using Quantile Regression: Evidences from India
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作者：Prashant Sharma ; Prashant Gupta ; Anurag Singh 等
期刊名称：International Journal of Economics and Financial Issues
电子版ISSN：2146-4138
出版年度：2016
卷号：6
期号：4
页码：1815-1826
语种：English
出版社：EconJournals
摘要：With the assumption that the returns are normally distributed with no fat tails, most of the existing studies have used ordinary least square (OLS) method to test the pricing ability of asset pricing models. These assumptions are not valid in numerous cases. Thus, to overcome such problem, the present study tests the pricing ability of Cahart (1997) four factor model using quantile regression which provides superior fitting of pricing factors than the traditional OLS model. The study uses daily data of Indian firms for period from December 1993 to March 2016. The results of the study reveal that the quantile regression model is having superior fitting across all percentile levels than OLS as it fails to fit these four factors across all percentile levels. Keywords : asset pricing, Fama-French factor model, quantile regression, Cahart’s momentum JEL Classifications: C30, G11, G12
其他摘要：With the assumption that the returns are normally distributed with no fat tails, most of the existing studies have used ordinary least square (OLS) method to test the pricing ability of asset pricing models. These assumptions are not valid in numerous cases. Thus, to overcome such problem, the present study tests the pricing ability of Cahart (1997) four factor model using quantile regression which provides superior fitting of pricing factors than the traditional OLS model. The study uses daily data of Indian firms for period from December 1993 to March 2016. The results of the study reveal that the quantile regression model is having superior fitting across all percentile levels than OLS as it fails to fit these four factors across all percentile levels. Keywords : asset pricing, Fama-French factor model, quantile regression, Cahart’s momentum JEL Classifications: C30, G11, G12