We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a...

We investigate the factor exposure of smart beta ETFs under model uncertainty using Bayesian variable selection. We find that smart beta ETFs have exposures to several factors, including size, value, momentum, quality, volatility/low beta, and dividend yield. The average return contribution of...

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) can unleash the power of no-arbitrage by creating a tighter link between cross-sectional and time-series variation of interest rates. This paper presents a new econometric framework for estimation of affine Gaussian DTSMs...

We propose a unified set of distance-based performance metrics that address the power and extreme-error problems inherent in traditional measures for asset-pricing tests. From a Bayesian perspective, the distance metrics coherently incorporate both pricing errors and their standard errors....

We build a novel macro-finance model that combines a semi-structural macroeconomic module with arbitrage-free yield-curve dynamics. We estimate it for the United States and the euro area using a Bayesian approach and jointly infer the real equilibrium interest rate (r*), trend inflation (π*),...

Incorporating arbitrage-free term-structure dynamics into a semi-structural macro-model, we jointly estimate the real equilibrium interest rate (r*), trend inflation, and term premia for the United States and the euro area, using a Bayesian approach. The natural real rate and trend inflation are...

In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...