We study the asymptotic of the spectral distribution for large empirical covariance matrices composed of independent lognormal Multifractal Random Walk processes. The asymptotic is taken as the observation lag shrinks to 0. In this setting, we show that there exists a limiting spectral distribution whose Stieltjes transform is uniquely characterized by equations which we specify. We also illustrate our results by numerical simulations
the main goal of this thesis is to develop the theory of spectral covariances and limit theorems for...
Let Bn=Sn(Sn+αnTN)−1, where Sn and TN are two independent sample covariance matrices with dimension ...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...
We study the asymptotics of the spectral distribution for large empirical covariance matrices compos...
The probabilistic properties of eigenvalues of random matrices whose dimension increases indefinitel...
AbstractWe introduce a random matrix model where the entries are dependent across both rows and colu...
International audienceThis paper studies the behaviour of the empirical eigenvalue distribution of l...
29 pagesInternational audienceIn this paper we derive an extension of the Marchenko-Pastur theorem t...
AbstractResults on the analytic behavior of the limiting spectral distribution of matrices of sample...
Abstract. We derive the distribution of the eigenvalues of a large sample covariance matrix when the...
This article is concerned with the spectral behavior of $p$-dimensional linear processes in...
This article is concerned with the spectral behavior of p-dimensional linear processes in the modera...
We compute spectral densities of large sample auto-covariance matrices of stationary stochastic proc...
Abstract. In this paper, we improve known results on the convergence rates of spectral distri-bution...
We give asymptotic spectral results for Gram matrices of the form n −1 X n X T n where the entries o...
the main goal of this thesis is to develop the theory of spectral covariances and limit theorems for...
Let Bn=Sn(Sn+αnTN)−1, where Sn and TN are two independent sample covariance matrices with dimension ...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...
We study the asymptotics of the spectral distribution for large empirical covariance matrices compos...
The probabilistic properties of eigenvalues of random matrices whose dimension increases indefinitel...
AbstractWe introduce a random matrix model where the entries are dependent across both rows and colu...
International audienceThis paper studies the behaviour of the empirical eigenvalue distribution of l...
29 pagesInternational audienceIn this paper we derive an extension of the Marchenko-Pastur theorem t...
AbstractResults on the analytic behavior of the limiting spectral distribution of matrices of sample...
Abstract. We derive the distribution of the eigenvalues of a large sample covariance matrix when the...
This article is concerned with the spectral behavior of $p$-dimensional linear processes in...
This article is concerned with the spectral behavior of p-dimensional linear processes in the modera...
We compute spectral densities of large sample auto-covariance matrices of stationary stochastic proc...
Abstract. In this paper, we improve known results on the convergence rates of spectral distri-bution...
We give asymptotic spectral results for Gram matrices of the form n −1 X n X T n where the entries o...
the main goal of this thesis is to develop the theory of spectral covariances and limit theorems for...
Let Bn=Sn(Sn+αnTN)−1, where Sn and TN are two independent sample covariance matrices with dimension ...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...