Richard T. Baillie ,
Michigan State University, USA, King’s College London & Rimini Center for Economic Analysis, Italy
Fabio Calonaci , Queen Mary University of London
George Kapetanios , King’s College London
January 7, 2019
This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential approach of Fama and MacBeth (1973). However, the hierarchical method uses very flexible bandwidth selection methods in kernel weighted regressions which can emphasize local, or recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point of time. The choice of bandwidths and weighting schemes, are achieved by cross validation. This leads to consistent estimators of the risk premia and factor loadings. Also, out of sample forecasting for stocks and two large portfolios indicates that the hierarchical method leads to statistically significant improvement in forecast RMSE.
J.E.L classification codes: C22; F31; G01; G15
Keywords:Asset pricing model, FamaMacBeth model, estimation of beta, kernel weighted regressions, cross validation, time-varying parameter regressions