Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks,...

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

We develop a Gaussian discrete time essentially affine term structure model with long memory state variables. This feature reconciles the strong persistence observed in nominal yields and inflation with the theoretical implications of affine models, especially for long maturities. We...

Empirical tests for bubbles typically focus on the stationarity properties of the dividend yield. Evidence of nonstationarity in the dividend yield is viewed as proof of bubbles, while stationarity is interpreted as absence of bubbles. For economies with arbitrary pricing kernels but stationary...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

We document that the first and third cross-sectional moments of the distribution of GDP growth rates made by professional forecasters can predict equity excess returns, a finding which is robust to controlling for a large set of well established predictive factors. We show that introducing...