Shrinkage for Gaussian and t copulas in ultra-high dimensions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F21%3A00545287" target="_blank" >RIV/00216208:11640/21:00545287 - isvavai.cz</a>
Alternative codes found
RIV/67985998:_____/21:00545259
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Shrinkage for Gaussian and t copulas in ultra-high dimensions
Original language description
Copulas are a convenient framework to synthesize joint distributions, particularly in higher dimensions. Currently, copula-based high dimensional settings are used for as many as a few hundred variables and require large data samples for estimation to be precise. In this paper, we employ shrinkage techniques for large covariance matrices in the problem of estimation of Gaussian and t copulas whose dimensionality goes well beyond that typical in the literature. Specifically, we use the covariance matrix shrinkage of Ledoit and Wolf to estimate large matrix parameters of Gaussian and t copulas for up to thousands of variables, using up to 20 times lower sample sizes. The simulation study shows that the shrinkage estimation significantly outperforms traditional estimators, both in low and especially high dimensions. We also apply this approach to the problem of allocation of large portfolios.
Czech name
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Czech description
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Project
<a href="/en/project/GA20-28055S" target="_blank" >GA20-28055S: ECONOMETRICS WITH OVERPARAMETERIZATION AND WEAK IDENTIFICATION</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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