In this paper we develop the rst estimator of the covariance matrix that relies solely onforward-looking information. This estimator only uses price information from a cross-sectionof plain-vanilla options. In an out-of-sample study for US blue-chip stocks we show that aminimum-variance strategy...

Our context involves Cournot oligopolists producing NM products at constant marginal costs when preferences are quasi-linear. We identify relationships between second moments of unit costs and second moments of firm-level production. For example, a larger variance in unit costs of a product...

Many statistical applications require an estimate of a covariance matrix and/or its inverse.When the matrix dimension is large compared to the sample size, which happensfrequently, the sample covariance matrix is known to perform poorly and may suffer fromill-conditioning. There already exists...

This research focuses to develop some new techniques on statistical learning including methodology, computation and application. We also developed statistical quantification in nanomaterials. For a large number of random variables with temporal or spatial structures, we proposed shrink estimates...

We present the asymptotic properties of double-stage quantile regressionestimators with random regressors, where the first stage is based on quantile regressionswith the same quantile as in the second stage, which ensures robustness of the estimationprocedure. We derive invariance properties...

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the...

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex...

In this paper, the multipath time delay estimation (TDE) problem for a slow frequency hopping (SFH) system using rank revealing QR factorization method (RRQR) is considered. It gives precious information about numerical rank and null space. By applying the RRQR in association with the well-known...

The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample...

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex...