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Title: Bayesian reconciliation of the return predictability Authors:  Borys Koval - Vienna University of Economics and Business (Austria) [presenting]
Sylvia Fruehwirth-Schnatter - WU Vienna University of Economics and Business (Austria)
Leopold Soegner - Institute for Advanced Studies (Austria)
Abstract: A VAR for the returns, dividend growth, and dividend price ratio is estimated, where the Bayesian Control Function approach is applied to account for endogeneity. Motivated by financial literature we impose a stationarity condition on the auto-regressive dividend price ratio process by means of Bayesian priors. We develop two new priors, Jeffrey prior and prior based on frequentist bias-corrected approach and compare our Bayesian estimation routine to other approaches proposed in the literature (e.g., uniform and reference prior) by means of an extensive simulation study. In terms of MSE, MAE, and credible interval coverage, the approach proposed in this article leads to superior performance relative to ordinary least squares estimation, a frequentist bias-corrected approach, and Bayesian estimation using priors proposed in the literature. We apply our methodology to financial data for the S\&P 500 and find strong evidence for return predictability after properly accounting for the correlation structure and imposing theory-motivated restrictions on the dividend price ratio.