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Modeling the Volatility of Returns on Investment Units of Voluntary Pension Funds in Serbia

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  • Radojković Ivan D.

    (University of Niš, Faculty of Pedagogy, Republic of Serbia)

  • Radović Ognjen V.

    (University of Niš, Faculty of Economics, Republic of Serbia)

  • Stevanović Kristina R.

    (University of Niš, Faculty of Pedagogy, Republic of Serbia)

Abstract

The purpose of this paper is to model and analyze the volatility of returns on investment units in voluntary pension funds in Serbia, focusing on five funds: Dunav, Generali Basic, Generali Index, DDOR Garant Ekvilibrio, and Raiffeisen Future. Given the growing significance of voluntary pension funds, the study explores the role of investment units as a crucial financial instrument that allows diversification and optimization of long-term returns. Methodologically, the study applies the Extreme Value Theory (EVT) using the Generalized Pareto Distribution (GPD) to model the extreme events in the distribution tails, a key component for risk management. The ARCH test was used initially to assess heteroskedasticity in the time series, but the absence of significant volatility changes negated the application of GARCH models. Instead, EVT was implemented to capture rare, yet impactful, fluctuations. Additionally, Value at Risk (VaR) and Expected Shortfall (ES) were estimated based on the fitted GPD model, offering more robust risk quantification for extreme losses. The results indicate that all return series are highly correlated, with extreme values predominantly occurring in shorter bursts. GPD models successfully captured these extremes, and VaR and ES measures highlighted the periods of elevated risk, particularly during financial crises. This research presents an original contribution to the analysis of investment unit volatility, providing practical insights for fund managers in balancing risk and return in a volatile market environment.

Suggested Citation

  • Radojković Ivan D. & Radović Ognjen V. & Stevanović Kristina R., 2024. "Modeling the Volatility of Returns on Investment Units of Voluntary Pension Funds in Serbia," Economic Themes, Sciendo, vol. 62(4), pages 541-560.
  • Handle: RePEc:vrs:ecothe:v:62:y:2024:i:4:p:541-560:n:1007
    DOI: 10.2478/ethemes-2024-0029
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    References listed on IDEAS

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    1. Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, vol. 181(1), pages 44-52.
    2. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    3. Laurens Haan & Cécile Mercadier & Chen Zhou, 2016. "Adapting extreme value statistics to financial time series: dealing with bias and serial dependence," Finance and Stochastics, Springer, vol. 20(2), pages 321-354, April.
    4. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    5. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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