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...

We consider a class of nonlinear time series expressed as a local mixture of a finite number of linear autoregressions. The mixing weights are continuous functions of lagged observations while the densities of the innovation terms in each autoregression can be very general and are only assumed...

This article presents evidence on the quality of Taylor series approximations to expected utility. To provide a transparent assessment in a broader setting, we assume that log portfolio returns follow a Gram--Charlier distribution that incorporates skewness and excess kurtosis and consider an...

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...

Substantial progress has been made in developing more realistic option pricing models. Empirically, however, it is not known whether and by how much each generalization improves option pricing and hedging. The authors fill this gap by first deriving an option model that allows volatility,...