We develop a new framework for intertemporal portfolio choice when the covariance matrix of returns is stochastic. An important contribution of this framework is that it allows to derive optimal portfolio implications for economies in which the degree of correlation across different industries,...

We develop infinitesimally robust statistical procedures for general diffusion processes. We first prove existence and uniqueness of the times series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale)...

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these...

We propose a multivariate nonparametric technique for generating reliable shortterm historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest...

We propose a novel no-arbitrage framework, which exploits convex asset pricing constraints to study investors’ marginal utility of wealth or, more generally, Stochastic Discount Factors (SDFs). We establish a duality between minimum dispersion SDFs and penalized portfolio selection problems,...

We provide a model-free framework to study the global factor structure of exchange rates. To this end, we propose a new methodology to estimate international stochastic discount factors (SDFs) that jointly price cross-sections of international assets, such as stocks, bonds, and currencies, in...