On the limiting spectral distribution of the covariance matrices of time-lagged processes

DOI10.1016/j.jmva.2010.06.014zbMath1202.15037OpenAlexW1973519516MaRDI QIDQ604359

Christian Y. Robert, Mathieu Rosenbaum

Publication date: 10 November 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.06.014

zbMATH Keywords

eigenvaluesrandom matrix theoryadaptive estimationlimiting spectral distributioncovariance matricescontinuous-time Gaussian processeslagged processestime lag estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20) Markov processes: estimation; hidden Markov models (62M05) Random matrices (algebraic aspects) (15B52)

Related Items (7)

Inference for time-varying lead-lag relationships from ultra-high-frequency data ⋮ On the Asymptotic Structure of Brownian Motions with a Small Lead-Lag Effect ⋮ Estimation of the lead-lag parameter from non-synchronous data ⋮ No arbitrage and lead-lag relationships ⋮ Convergence of the spectrum of empirical covariance matrices for independent MRW processes ⋮ Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions ⋮ Wavelet-Based Methods for High-Frequency Lead-Lag Analysis

Cites Work

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• A limit theorem for the eigenvalues of product of two random matrices
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
• Estimation of the lead-lag parameter from non-synchronous data
• Regularized estimation of large covariance matrices
• The Limiting Eigenvalue Distribution of a Multivariate F Matrix

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