This paper investigates whether the use of robust covariance improves portfolio performance and, in the presence of uncertainty, whether the 1/N strategy is as good as you think. In addition to sample covariance, we use a battery of robust covariance matrix. Our empirical evidence has two...

The purpose of this paper is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall's Tau by counting the proportion of simultaneous co-movements in series when their...

In this paper we develop a portfolio optimization strategy based on the extraction of option-implied distributions and the application of robust asset allocation. We compute the option-implied probability density functions of the constituents of the Euro Stoxx 50 Index. To obtain the...

This contribution studies the application of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models. VAR models have become one of the most applied models for the analysis of multivariate time series. Econometric standard software usually provides parameter estimators that...

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or...

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting;...

This paper reworks and expands on the results of existing simulation studies, investigating the performance of various robust estimators of scale for Tukey's three corner distributions. We focus attention on the popular biweight A-estimator, but also propose a new estimator based on the...

In time series regressions with nonparametrically autocorrelated errors, it is now standard empirical practice to use kernel-based robust standard errors that involve some smoothing function over the sample autocorrelations. The underlying smoothing parameter b, which can be defined as the ratio...

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting;...