Copula shrinkage and portfolio allocation in ultra-high dimensions

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Copula shrinkage and portfolio allocation in ultra-high dimensions

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Copula shrinkage and portfolio allocation in ultra-high dimensions

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA20-28055S" target="_blank" >GA20-28055S: EKONOMETRIE S PŘEPARAMETRIZOVANÝMI MODELY A SLABOU IDENTIFIKACÍ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Economic Dynamics & Control

ISSN

0165-1889

e-ISSN

1879-1743

Svazek periodika

143

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

104508

Kód UT WoS článku

000847422800006

EID výsledku v databázi Scopus

2-s2.0-85136064507