Dynamic return scenario generation approach for large-scale portfolio optimisation framework

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F25%3A10253833" target="_blank" >RIV/61989100:27510/25:10253833 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10614-023-10541-w#citeas" target="_blank" >https://link.springer.com/article/10.1007/s10614-023-10541-w#citeas</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10614-023-10541-w" target="_blank" >10.1007/s10614-023-10541-w</a>

Result language

angličtina

Original language name

Dynamic return scenario generation approach for large-scale portfolio optimisation framework

Original language description

In this paper, we propose a complex return scenario generation process that can be incorporated into portfolio selection problems. In particular, we assume that returns follow the ARMA-GARCH model with stable-distributed and skewed t-copula dependent residuals. Since the portfolio selection problem is large-scale, we apply the multifactor model with a parametric regression and a nonparametric regression approaches to reduce the complexity of the problem. To do this, the recently proposed trend-dependent correlation matrix is used to obtain the main factors of the asset dependency structure by applying principal component analysis (PCA). However, when a few main factors are assumed, the obtained residuals of the returns still explain a non-negligible part of the portfolio variability. Therefore, we propose the application of a novel approach involving a second PCA to the Pearson correlation to obtain additional factors of residual components leading to the refinement of the final prediction. Future return scenarios are predicted using Monte Carlo simulations. Finally, the impact of the proposed approaches on the portfolio selection problem is evaluated in an empirical analysis of the application of a classical mean-variance model to a dynamic dataset of stock returns from the US market. The results show that the proposed scenario generation approach with nonparametric regression outperforms the traditional approach for out-of-sample portfolios.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

50206 - Finance

Project

<a href="/en/project/GA23-06280S" target="_blank" >GA23-06280S: New approaches to forecasting of financial time series within fuzzy-probabilistic setting</a><br>

Continuities

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

Publication year

2025

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

1572-9974

Volume of the periodical

65

Issue of the periodical within the volume

2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

25

Pages from-to

819-843

UT code for WoS article

001151126100001

EID of the result in the Scopus database

2-s2.0-85182475871