A large literature studies the predictability of stock returns by other lagged nancialvariables in a predictive regression setting. A common feature of widely used testingprocedures is a failing robustness, which may lead to misleading conclusions determinedby the particular features of a small...

We introduce a new class of flexible and tractable matrix a±ne jump-diffusions (AJD) to modelmultivariate sources of financial risk. We first provide a complete transform analysis of this model class,which opens a range of new potential applications to, e.g., multivariate option pricing with...

This paper analyzes the relation between correlation risk and the cross-section of hedge fund returns.Legal framework and investment mandate imply that hedge funds can be severely exposed tocorrelation risk: Hedge funds ability to enter long-short positions can be useful to reduce marketbeta,...

In a Lucas orchard with heterogeneous beliefs, we study the link between market-wide uncertainty, difference of opinionsand co-movement of stock returns. We show that this link plays an important role in explaining the dynamics of equilibriumvolatility and correlation risk premia. In our...

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 apply perturbation theory to solve the optimal control problem of an investor with time-additive power utility over intermediate consumption and final wealth. Under general conditions we show existence of a power series representation for the prevailing optimal consumption and investment...

This paper focuses on the robust Effcient Method of Moments (EMM) estimation of a general parametric stationary process and proposes a broad framework for constructing robust EMM statistics in this context. This extends the application field of robust statistics to very general time series...

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...