68 research outputs found
Large Dimensional Analysis and Optimization of Robust Shrinkage Covariance Matrix Estimators
This article studies two regularized robust estimators of scatter matrices
proposed (and proved to be well defined) in parallel in (Chen et al., 2011) and
(Pascal et al., 2013), based on Tyler's robust M-estimator (Tyler, 1987) and on
Ledoit and Wolf's shrinkage covariance matrix estimator (Ledoit and Wolf,
2004). These hybrid estimators have the advantage of conveying (i) robustness
to outliers or impulsive samples and (ii) small sample size adequacy to the
classical sample covariance matrix estimator. We consider here the case of
i.i.d. elliptical zero mean samples in the regime where both sample and
population sizes are large. We demonstrate that, under this setting, the
estimators under study asymptotically behave similar to well-understood random
matrix models. This characterization allows us to derive optimal shrinkage
strategies to estimate the population scatter matrix, improving significantly
upon the empirical shrinkage method proposed in (Chen et al., 2011).Comment: Journal of Multivariate Analysi
A Robust Statistics Approach to Minimum Variance Portfolio Optimization
We study the design of portfolios under a minimum risk criterion. The
performance of the optimized portfolio relies on the accuracy of the estimated
covariance matrix of the portfolio asset returns. For large portfolios, the
number of available market returns is often of similar order to the number of
assets, so that the sample covariance matrix performs poorly as a covariance
estimator. Additionally, financial market data often contain outliers which, if
not correctly handled, may further corrupt the covariance estimation. We
address these shortcomings by studying the performance of a hybrid covariance
matrix estimator based on Tyler's robust M-estimator and on Ledoit-Wolf's
shrinkage estimator while assuming samples with heavy-tailed distribution.
Employing recent results from random matrix theory, we develop a consistent
estimator of (a scaled version of) the realized portfolio risk, which is
minimized by optimizing online the shrinkage intensity. Our portfolio
optimization method is shown via simulations to outperform existing methods
both for synthetic and real market data
Minimum variance portfolio optimization in the spiked covariance model
International audience—We study the design of minimum variance portfolio when asset returns follow a low rank factor model. Using results from random matrix theory, an optimal shrinkage approach for the isolated eigenvalues of the covariance matrix is developed. The proposed portfolio optimization strategy is shown to have good performance on synthetic data but not always on real data sets. This leads us to refine the data model by considering time correlation between samples. By updating the shrinkage of the isolated eigenvalues accounting for the unknown time correlation, our portfolio optimization method is shown to have improved performance and achieves lower risk values than competing methods on real financial data sets
Signal from noise retrieval from one and two-point Green's function - comparison
We compare two methods of eigen-inference from large sets of data, based on
the analysis of one-point and two-point Green's functions, respectively. Our
analysis points at the superiority of eigen-inference based on one-point
Green's function. First, the applied by us method based on Pad?e approximants
is orders of magnitude faster comparing to the eigen-inference based on
uctuations (two-point Green's functions). Second, we have identified the source
of potential instability of the two-point Green's function method, as arising
from the spurious zero and negative modes of the estimator for a variance
operator of the certain multidimensional Gaussian distribution, inherent for
the two-point Green's function eigen-inference method. Third, we have presented
the cases of eigen-inference based on negative spectral moments, for strictly
positive spectra. Finally, we have compared the cases of eigen-inference of
real-valued and complex-valued correlated Wishart distributions, reinforcing
our conclusions on an advantage of the one-point Green's function method.Comment: 14 pages, 8 figures, 3 table
Complete diagrammatics of the single ring theorem
Using diagrammatic techniques, we provide explicit functional relations
between the cumulant generating functions for the biunitarily invariant
ensembles in the limit of large size of matrices. The formalism allows to map
two distinct areas of free random variables: Hermitian positive definite
operators and non-normal R-diagonal operators. We also rederive the
Haagerup-Larsen theorem and show how its recent extension to the eigenvector
correlation function appears naturally within this approach.Comment: 18 pages, 6 figures, version accepted for publicatio
On the Transmit Beamforming for MIMO Wiretap Channels: Large-System Analysis
With the growth of wireless networks, security has become a fundamental issue
in wireless communications due to the broadcast nature of these networks. In
this work, we consider MIMO wiretap channels in a fast fading environment, for
which the overall performance is characterized by the ergodic MIMO secrecy
rate. Unfortunately, the direct solution to finding ergodic secrecy rates is
prohibitive due to the expectations in the rates expressions in this setting.
To overcome this difficulty, we invoke the large-system assumption, which
allows a deterministic approximation to the ergodic mutual information.
Leveraging results from random matrix theory, we are able to characterize the
achievable ergodic secrecy rates. Based on this characterization, we address
the problem of covariance optimization at the transmitter. Our numerical
results demonstrate a good match between the large-system approximation and the
actual simulated secrecy rates, as well as some interesting features of the
precoder optimization.Comment: Published in Lecture Notes in Computer Science 8317, pp. 90-102,
2014. (Proceedings of International Conference on Information-Theoretic
Security (ICITS), Singapore, November 2013
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