We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilitiesand correlations. The model estimation is feasible in large dimensions and the positive definiteness of the conditional...

We propose a multivariate nonparametric technique for generating reliable short-term 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 study the problem of learning the probability distribution of a multinomial variable from an observed sequence of signals, starting in a condition of ignorance about this distribution. We show that not all signals are suited for producing non-vacuous inferences under prior ignorance. To...

Consider a relaxed multinomial setup, in which there may be mistakes in observing the outcomes of the processthis is often the case in real applications. What can we say about the next outcome if we start learning about the process in conditions of prior ignorance? To answer this question we...

The imprecise Beta model (IBM) of Bernard (1996) and Walley (1996) is the most popular model for learning about a binomial random variable under prior ignorance. Piatti et al. (2005) show that there is a fundamental issue with the interpretation of results produced by the IBM in applications....

We characterize the robustness of subsampling procedures by deriving a general formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even if they are applied to...

We solve analytically the Merton's problem of an investor with time-additive power utility. For general state dynamics, we prove existence of two power series representations of the relevant optimal policies and value functions, which hold for all admissible risk aversion parameters. We...

We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which...

In this paper we solve an intertemporal portfolio problem with correlation risk, using a new approach for the simultaneous modeling of stochastic correlation and volatility. The solutions of the model are in closed form and include an optimal portfolio demand for hedging correlation risk. We...

In a continuous-time, pure exchange economy on a finite horizon financial agents display ambiguity aversion for a neighborhood of indistinguishable model specifications that are constrained in their relative entropy from a given reference model. We characterize equilibrium optimal consumption-...