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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50200 - Economics and Business

Projekt

<a href="/cs/project/GA20-16764S" target="_blank" >GA20-16764S: Zobecněný přístup ke stochastické dominanci: teorie a finanční aplikace</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

Computational Economics

ISSN

0927-7099

e-ISSN

—

Svazek periodika

60

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

27

Strana od-do

833-859

Kód UT WoS článku

000693705500002

EID výsledku v databázi Scopus

2-s2.0-85114314059