Consistent Estimation of Pricing Kernels from Noisy Price Data

Abstract

If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $ε$-entropy, non-parametric estimation, pricing kernel, inverse problems.