Robust test of long run risk and valuation risk model - Goutham Gopalakrishna (Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), 2017)
This paper tests the long run risk and valuation risk model using a robust estimation procedure. The persistent long run component of consumption growth process is proxied by a news based index that is created using a random forest algorithm. This news index is shown to predict aggregate long term consumption growth with an R-square of 57% and is robust to inclusion of other commonly used predictors. I theoretically derive an estimatable bias term in adjusted Euler equation of the model that arises due to measurement error in consumption data and show that this bias term is non-zero. Using a three pass estimation procedure that accounts for this bias, I show that the long run risk and valuation risk model fails to explain cross section of equity returns. This contrasts to the results from regular two pass Fama-MacBeth estimation procedure that implies that the same model explains the cross section of asset returns with statistically significant risk premia estimates.
Keywords: G12; E21; E44; 330; Long run risk; Valuation risk; Machine Learning; Three pass filter; Media; Consumption