A quantile-based asset pricing model - ANDO, Tomohiro (Institutional Knowledge at Singapore Management University, 2019)
It is well-known that the standard estimators of the risk premium in asset pricing models are biased when some price factors are omitted. To address this problem, we propose a novel quantile-based asset pricing model and a new estimation method. Our new asset pricing model allows for the risk premium to be quantile-dependent and our estimation method is applicable to models with unobserved factors. It avoids biased estimation results and always ensures a positive risk premium. The method is applied to the U.S., Japan, and U.K. stock markets. The empirical analysis demonstrates the clear beneﬁts of our approach.