Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F22%3A10247908" target="_blank" >RIV/61989100:27510/22:10247908 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007%2Fs10614-021-10167-w" target="_blank" >https://link.springer.com/article/10.1007%2Fs10614-021-10167-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10614-021-10167-w" target="_blank" >10.1007/s10614-021-10167-w</a>

Result language

angličtina

Original language name

Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach

Original language description

This paper investigates the implications for portfolio theory of using multivariate semiparametric estimators and a copula-based approach, especially when the number of risky assets becomes substantial. Parametric, nonparametric, and semiparametric regression methods are compared to approximate their returns in large-scale portfolio selection problems. Semiparametric regression models are used to prove that, under certain assumptions, the variability of the errors decreases as the number of factors increases. Moreover, a copula principal component analysis (PCA)-based approach is proposed, and its superiority to the classical Pearson PCA approach is demonstrated. Empirical analyses validate the suggested approaches and evaluate the impact of different approximation methods on portfolio selection problems. Here, the ex-ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward-risk or drawdown-based performance measures are compared. The results show that the proposed methodologies outperform the traditional approach for out-of-sample portfolios, especially when the dependence structure is represented by the Pearson linear correlation. (C) 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50200 - Economics and Business

Project

<a href="/en/project/GA20-16764S" target="_blank" >GA20-16764S: A generalized approach to stochastic dominance: theory and financial applications</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

Name of the periodical

Computational Economics

ISSN

0927-7099

e-ISSN

—

Volume of the periodical

60

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

27

Pages from-to

833-859

UT code for WoS article

000693705500002

EID of the result in the Scopus database

2-s2.0-85114314059