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Comparative Analysis of Retirement Benefits in Private Pension Funds and Public Pension System Cover

Comparative Analysis of Retirement Benefits in Private Pension Funds and Public Pension System

Open Access
|May 2020

Abstract

This paper identifies the conditions under which the private pension funds generate superior retirement outcomes compared to public pension system. The research objective is to determine the probability of success of the selected investment strategies in achieving the public pension system replacement rate, and the probability of the realization of extremely unfavourable outcomes. The methodology used in this paper includes the comparative analysis of simulated financial results of the four selected investment strategies implemented in the private pension fund model and the defined retirement benefits generated within the public pension system. For the simulation of the financial results at retirement, Monte Carlo simulation technique has been used. The authors have found that the success rate of the private pension fund in achieving superior financial results in comparison to public pension system is high, but only for the contribution rates higher than 10%. At low contributions rates, the extremely aggressive strategy is the only one that generates moderate success rate. Also, the probability of realization of extremely unfavourable financial results is lowest for the conservative strategy, which suggests that for the relatively high levels of the contribution rate, it is the most appropriate option for the pension fund members.

DOI: https://doi.org/10.2478/ethemes-2019-0009 | Journal eISSN: 2217-3668 | Journal ISSN: 0353-8648
Language: English
Page range: 145 - 164
Submitted on: May 14, 2019
Accepted on: Jun 19, 2019
Published on: May 4, 2020
Published by: University of Niš, Faculty of Economics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Stevan Luković, Srđan Marinković, published by University of Niš, Faculty of Economics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.