Dynamic Modelling of Large Dimensional Covariance Matrices
نویسندگان
چکیده
منابع مشابه
Large Dynamic Covariance Matrices
Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In ...
متن کاملExact Separation of Eigenvalues of Large Dimensional Sample Covariance Matrices
Let B n = (1/N)T 1/2 n is a Hermitian square root of the nonnegative definite Hermitian matrix T n. It is shown in Bai and Silverstein (1998) that, under certain conditions on the eigenvalues of T n , with probability one no eigenvalues lie in any interval which is outside the support of the limiting empirical distribution (known to exist) for all large n. For these n the interval corresponds t...
متن کاملOn the Eigenvectors of Large Dimensional Sample Covariance Matrices
Let {a,}, i,j=1,2 ,..., be i.i.d. random variables, and for each n let M, = (l/s) V, Vz, where V, = (vi,). i = 1,2, . . . . n, j = 1,2, . . . . s = s(n), and n/s -+ y > 0 as n + co. Necessary and sufficient conditions are given to establish the convergence in distribution of certain random variables defined by M,. When E(uf,) < co these variables play an important role toward understanding the ...
متن کاملOn Testing for Diagonality of Large Dimensional Covariance Matrices
Datasets in a variety of disciplines require methods where both the sample size and the dataset dimensionality are allowed to be large. This framework is drastically different from the classical asymptotic framework where the number of observations is allowed to be large but the dimensionality of the dataset remains fixed. This paper proposes a new test of diagonality for large dimensional cova...
متن کاملConsistent Estimation of Large - Dimensional Sparse Covariance Matrices
Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices X of dimension n×p, where p and n are both large. Results from random matrix theory show very clearly that in this setting, standard estimators like the sample covariance matrix perform in general very poorly. In this “large n, larg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2007
ISSN: 1556-5068
DOI: 10.2139/ssrn.901072