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 findings: First, the range of in-sample estimation horizon and out …

The purpose of this paper is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix … for estimating the covariance matrix of stock returns: the sample covariance matrix (also called the historical covariance … matrix) and shrinkage of the sample covariance matrix as formulated in Ledoit and Wolf (2004). Using a well …

We propose a jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high …-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate … covariance estimation and the jump robustness of the estimator are illustrated in a simulation study. In an application to the …

We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of … jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance …

. In a simulation experiment we compare these nonparametric estimators with correlations based on a robust covariance …

weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of … covariance matrix misspecification to these risk-based portfolios. Our results show that the equal-risk-contribution and inverse …-volatility weighted portfolio weights are relatively robust to covariance misspecification, but that the minimum-variance and maximum …

In this paper, we consider a robust method of estimating a realized covariance matrix calculated as the sum of cross … products of intraday high-frequency returns. According to recent papers in financial econometrics, the realized covariance … have been studied since the early 2000s, they have primarily investigated a low-dimensional covariance matrix with …

This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can … Random Matrix Theory and robust covariance matrix estimation. The proposed procedure will be explained through synthetic data …

. In a simulation experiment we compare these nonparametric estimators with correlations based on a robust covariance …