We study the asymptotics of the spectral distribution for large empirical covariance matrices composed of independent 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.ouinonouirechercheInternationa
We compute spectral densities of large sample auto-covariance matrices of stationary stochastic proc...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...
International audienceThis paper presents a novel approach to characterize the dynamics of the limit...
We study the asymptotic of the spectral distribution for large empirical covariance matric...
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...
29 pagesInternational audienceIn this paper we derive an extension of the Marchenko-Pastur theorem t...
International audienceThis paper studies the behaviour of the empirical eigenvalue distribution of l...
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...
the main goal of this thesis is to develop the theory of spectral covariances and limit theorems for...
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...
We compute spectral densities of large sample auto-covariance matrices of stationary stochastic proc...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...
International audienceThis paper presents a novel approach to characterize the dynamics of the limit...
We study the asymptotic of the spectral distribution for large empirical covariance matric...
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...
29 pagesInternational audienceIn this paper we derive an extension of the Marchenko-Pastur theorem t...
International audienceThis paper studies the behaviour of the empirical eigenvalue distribution of l...
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...
the main goal of this thesis is to develop the theory of spectral covariances and limit theorems for...
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...
We compute spectral densities of large sample auto-covariance matrices of stationary stochastic proc...
Consider the empirical autocovariance matrix at a given non-zero time lag based on observations from...
International audienceThis paper presents a novel approach to characterize the dynamics of the limit...