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Robust test of Long Run Risk and Valuation risk model

G. Gopalakrishna

Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna

Abstract: 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.

JEL-codes: G12 E21 E44 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mac
Date: 2017-09
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