Copula shrinkage and portfolio allocation in ultra-high dimensions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F22%3A00560357" target="_blank" >RIV/00216208:11640/22:00560357 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.jedc.2022.104508" target="_blank" >https://doi.org/10.1016/j.jedc.2022.104508</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jedc.2022.104508" target="_blank" >10.1016/j.jedc.2022.104508</a>

Result language
angličtina
Original language name
Copula shrinkage and portfolio allocation in ultra-high dimensions
Original language description
Copulas prove to be a convenient tool in modeling joint distributions. As the data dimensionality grows, obtaining precise and well-conditioned estimates of copula-based distributions becomes a challenge. Currently, copula-based high dimensional settings are typically used for as many as a few hundred variables and require large data samples for estimation to be precise. In this paper, we handle the problem of estimation of Gaussian and t copulas in ultra-high dimensions, up to thousands of variables that use up to 30 times shorter sample lengths. Specifically, we employ recently developed large covariance matrix shrinkage tools to obtain precise and well-conditioned estimates of copula matrix parameters. Simulations show that shrinkage copulas significantly outperform traditional estimators, especially in high dimensions. We also illustrate benefits of this approach for the problem of allocation of large portfolios of stocks. Our experiments show that the shrinkage estimators applied to t copula-based dynamic models deliver better portfolios in terms of cumulative return and maximum downfall over portfolio lifetime than traditional benchmarks.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics

Project
<a href="/en/project/GA20-28055S" target="_blank" >GA20-28055S: ECONOMETRICS WITH OVERPARAMETERIZATION AND WEAK IDENTIFICATION</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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