We consider the problem of optimal asset allocation for portfolio with a large number of shares. The numerical solution relies on the estimation of the covariance matrix between the assets. Such estimation, typically obtained with maximum likelihood, is aected be the so-called \maximization estimation error", which grows with the dimension of the covariance matrix. The use of a robust estimator of the covariance matrix can reduce such estimation error considerably, even when data are outlier free and outperform the standard approaches when data have marked heavy tails or aected by the presence of outliers. The performance of our new robust estimator is studied with simulations, and real data.

Performance Assessment of Optimal Allocation for Large Portfolios

GROSSI, Luigi;
2010-01-01

Abstract

We consider the problem of optimal asset allocation for portfolio with a large number of shares. The numerical solution relies on the estimation of the covariance matrix between the assets. Such estimation, typically obtained with maximum likelihood, is aected be the so-called \maximization estimation error", which grows with the dimension of the covariance matrix. The use of a robust estimator of the covariance matrix can reduce such estimation error considerably, even when data are outlier free and outperform the standard approaches when data have marked heavy tails or aected by the presence of outliers. The performance of our new robust estimator is studied with simulations, and real data.
9783790826036
financial asset allocation; infuential data; robust estimators
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/345346
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact