Robust estimation techniques based on symmetric probability distributions are often substituted for OLS to obtain efficient regression parameters with thick-tail distributed data. The empirical, simulation and theoretical results in this paper show that with skewed distributed data, symmetric...

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on ad-dressing the EIV issue: instead of viewing EIV biases as estimation errors that potentiallycontaminate next-stage risk premium estimates, we consider them to be return innovationsthat follow a...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

This paper investigates, both in finite samples and asymptotically, statistical inference on predictive regressions where time series are generated by present value models of asset prices. We show that regression-based tests, including optimal robust tests such as Jasson and Moreira's...