IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i7p1134-d1624073.html
   My bibliography  Save this article

Asymmetric Shocks and Pension Fund Volatility: A GARCH Approach with Macroeconomic Predictors to an Unexplored Emerging Market

Author

Listed:
  • Cristiana Tudor

    (Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

  • Aura Girlovan

    (Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

  • Gabriel Robert Saiu

    (Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

  • Daniel Dumitru Guse

    (Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

Abstract

Financial stability analysis requires volatility modeling, especially in emerging nations where pension fund systems are very vulnerable to macrofinancial risks. In order to examine the volatility dynamics of Romania’s private pension system, this study uses daily net asset value (NAV) data from 2012 to 2024 to evaluate four GARCH-type models: standard GARCH (sGARCH), exponential GARCH (EGARCH), Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and component GARCH (C-GARCH). The analysis includes domestic and international equity indices (BET, STOXX), government bond yields (ROMGB 10Y, ROMANI 5Y), short-term interbank rates (ROBOR ON), and exchange rate fluctuations (RON/EUR). Current findings indicate that EGARCH captures asymmetric fluctuations in pension fund performance, where positive shocks generate larger increases in volatility than negative ones, highlighting an atypical asymmetry pattern. Furthermore, the stabilizing effects of government bonds are overshadowed by stock market behavior, which becomes the primary driver of risk. Fluctuations in exchange rates further increase volatility, especially in markets vulnerable to external disturbances. The findings offer empirical evidence for the necessity of more cautious risk management approaches and highlight the importance of regulatory oversight in maintaining market confidence. The study underscores the importance of customized allocation frameworks that reduce vulnerability to disruptive events while maintaining prospects for sustained growth. This new dataset contributes to enhancing the comprehension of pension fund volatility within the context of emerging markets. These insights can assist managers and policymakers seeking to fortify retirement outcomes.

Suggested Citation

  • Cristiana Tudor & Aura Girlovan & Gabriel Robert Saiu & Daniel Dumitru Guse, 2025. "Asymmetric Shocks and Pension Fund Volatility: A GARCH Approach with Macroeconomic Predictors to an Unexplored Emerging Market," Mathematics, MDPI, vol. 13(7), pages 1-29, March.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1134-:d:1624073
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/7/1134/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/7/1134/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. Doan, Bao & Papageorgiou, Nicolas & Reeves, Jonathan J. & Sherris, Michael, 2018. "Portfolio management with targeted constant market volatility," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 134-147.
    4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    5. Victoria Åžeulean & Luiza MoÅŸ, 2010. "Determinant Factors Of The Investment Performance Of Voluntary Pension Funds In Romania," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(12), pages 1-47.
    6. Gabriel Chodorow-Reich, 2014. "Effects of Unconventional Monetary Policy on Financial Institutions," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(1 (Spring), pages 155-227.
    7. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    8. Alda, Mercedes, 2017. "The relationship between pension funds and the stock market: Does the aging population of Europe affect it?," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 83-97.
    9. G. R. Jafari & A. Bahraminasab & P. Norouzzadeh, 2007. "Why Does The Standard Garch(1, 1) Model Work Well?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1223-1230.
    10. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    11. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    12. Alexandra DARMAZ-GUZUN, 2018. "Analysis of the investments made on the Romanian capital market by the privately managed pension funds – Pillar II," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(616), A), pages 49-60, Autumn.
    13. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    14. Debopam Rakshit & Ranjit Kumar Paul & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Modeling Asymmetric Volatility: A News Impact Curve Approach," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
    15. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    16. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    17. Dyson, A.C.L. & Exley, C.J., 1995. "Pension Fund Asset Valuation and Investment," British Actuarial Journal, Cambridge University Press, vol. 1(5), pages 965-977, December.
    18. Dyson, A.C.L. & Exley, C.J., 1995. "Pension Fund Asset Valuation and Investment," British Actuarial Journal, Cambridge University Press, vol. 1(3), pages 471-557, August.
    19. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    20. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    21. Shehu Usman Rano Aliyu, 2012. "Does inflation have an impact on stock returns and volatility? Evidence from Nigeria and Ghana," Applied Financial Economics, Taylor & Francis Journals, vol. 22(6), pages 427-435, March.
    22. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    23. Ferreira, Nuno B. & Menezes, Rui & Mendes, Diana A., 2007. "Asymmetric conditional volatility in international stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 73-80.
    24. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    25. Mariana Petrova & Teodor Todorov, 2023. "Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds," Risks, MDPI, vol. 11(11), pages 1-30, November.
    26. Ling-Ni Boon & Marie Brière & Sandra Rigot, 2018. "Regulation and Pension Fund Risk-Taking," Post-Print hal-02315479, HAL.
    27. Erginbay Ugurlu & Eleftherios Thalassinos & Yusuf Muratoglu, 2014. "Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 72-87.
    28. Head, S.J. & Adkins, D.R. & Cairns, A.J.G. & Corvesor, A.J. & Cule, D.O. & Exley, C.J. & Johnson, I.S. & Spain, J.G. & Wise, A.J., 2000. "Pension Fund Valuations and Market Values," British Actuarial Journal, Cambridge University Press, vol. 6(1), pages 55-141, June.
    29. Seda Peksevim & Metin Ercan, 2024. "Do pension funds provide financial stability? Evidence from European Union countries," Journal of Financial Services Research, Springer;Western Finance Association, vol. 66(3), pages 297-328, December.
    30. Francesco Franzoni & José M. Marín, 2006. "Pension Plan Funding and Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 61(2), pages 921-956, April.
    31. Boon, L.N. & Brière, M. & Rigot, S., 2018. "Regulation and pension fund risk-taking," Journal of International Money and Finance, Elsevier, vol. 84(C), pages 23-41.
    32. Vasile ROBU & Irina Daniela CIŞMAŞU & Maria Iuliana SANDU, 2013. "The effect of the Romanian pension market concentration on the magnitude of pension revenues," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(579)), pages 23-36, February.
    33. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    3. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    5. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    6. Díaz-Hernández, Adán & Constantinou, Nick, 2019. "A multiple regime extension to the Heston–Nandi GARCH(1,1) model," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 162-180.
    7. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, July.
    8. Zouheir Mighri, 2018. "On the Dynamic Linkages Among International Emerging Currencies," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 427-473, June.
    9. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
    10. Liudas Giraitis & Remigijus Leipus & Peter M Robinson & Donatas Surgailis, 2003. "LARCH, Leverage and Long Memory," STICERD - Econometrics Paper Series 460, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    11. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    12. Liudas Giraitis, 2004. "LARCH, Leverage, and Long Memory," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 177-210.
    13. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    14. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    15. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    16. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    17. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    18. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    19. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    20. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1134-:d:1624073. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.