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@masterthesis{oai:export,
author = {Irina Pimenova},
title = {Semi-parametric estimation of elliptical distribution in case of high dimensionality},
year = {2012},
publisher = {Wirtschaftswissenschaftliche Fakultät},
abstract = {This paper is devoted to the problem of high dimensionality in finance. We consider a joint multivariate density estimator of elliptical distribution which relies on a non-parametric estimation of a generator function. The factor model is employed in order to obtain a consistent covariance matrix estimator. We provide a simulation study that suggests that the considered estimator significantly outperforms the one based on the sample covariance matrix estimator. We also provide an empirical study using an example of a S&P500 portfolio. The returns of the resulted distribution are fat tailed and have a high peak. The comparison with other distributions illustrates the inappropriateness of normal or Student t distribution to fit the financial returns. Calculations of VaR are provided as an example of possible applications.},
url = { \url{http://edoc.hu-berlin.de/docviews/abstract.php?id=39706} },
url = { \url{urn:nbn:de:kobv:11-100205587} },
timestamp = {2017-03-30T16:42:49Z}
}