This paper presents an option positioning that allows us to infer forward variances from option portfolios. The forward variances we construct from equity index options help to predict (i) growth in measures of real economic activity, (ii) Treasury bill returns, (iii) stock market returns, and...

The structural uncertainty model with Bayesian learning, advanced by Weitzman (AER 2007), provides a framework for gauging the effect of structural uncertainty on asset prices and risk premiums. This paper provides an operational version of this approach that incorporates realistic priors about...

The structural uncertainty model with Bayesian learning, advanced by Weitzman (AER 2007), provides a framework for gauging the effect of structural uncertainty on asset prices and risk premiums. This paper provides an operational version of this approach that incorporates realistic priors about...

We provide a brief overview of applications of generalized method of moments in finance. The models examined in the empirical finance literature, especially in the asset pricing area, often imply moment conditions that can be used in a straight forward way to estimate the model parameters...

We develop a new solution method for a broad class of discrete-time dynamic portfolio choice problems. The method efficiently approximates conditional expectations of the value function by using (i) a decomposition of the state variables into a component observable by the investor and a...

We propose and analyze a new nonlinear time series model based on local mixtures of linear regressions, referred to as experts, with thick-tailed disturbances. The mean function of each expert is an affine function of covariates that may include lags of the dependent variable and/or lags of...