This paper evaluates how adaptive learning agents weight different pieces of information when forming expectations with a recursive least squares algorithm. The analysis is based on a renewed and more general non-recursive representation of the learning algorithm, namely, a penalized weighted...

This paper introduces the package sensemakr for R and Stata, which implements a suite of sensitivity analysis tools for regression models developed in Cinelli and Hazlett (2020a). Given a regression model, sensemakr can compute sensitivity statistics for routine reporting, such as the robustness...

This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross sectional...

Stock factor returns exhibit greater predictability from the weighted least squares (WLS) estimator of autoregressions with time-varying volatility. The predictability transmits into superior mean-variance optimal portfolio performance that is hardly achieved by other strategies that utilize...

We propose a new forward-backward stochastic differential equation solver for highdimensional derivative pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our...

Original polynomial least squares (LS) fits data by constructing coefficients that minimize the sum of squared deviations of deterministic samples from assumed polynomials. Contemporary LS estimates existing polynomial coefficients by filtering out corrupting statistical errors. Kalman Filter...