Dynamic Forecasting of Sovereign Wealth Fund Effects on State-Owned Firm Stocks Using Hybrid ARIMA–GARCH Models

Creative Commons License

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

Abstract

This study examines the forecasting dynamics of Indonesia's Sovereign Wealth Fund, DANANTARA, on the BUMN-20 Index using a hybrid ARIMA-GARCH model. The establishment of DANANTARA in February 2025 generated mixed reactions among capital market participants, with the Indonesian Stock Exchange suspended trading after share prices declined by more than five percent. Using daily time-series data from June 2018 to August 2026, this study employs a hybrid ARIMA(1,1,3)-GARCH(1,1) framework processed through E-Views 12. The results indicate that the BUMN-20 Index is predominantly driven by broader market movements, as evidenced by the highly significant IHSG return coefficient (β =1.237, p < 0.001). In the mean equation, the DANANTARA announcement and launch events do not produce statistically significant shifts in average returns. However, in the variance equation, the announcement significantly elevated conditional volatility (γ = 0.025, p = 0.009), suggesting heightened market uncertainty. The volatility persistence measure (α + β = 0.952) indicates that shocks to the BUMN-20 Index decay slowly, with a half-life of approximately 14 trading days. The forecasting results suggest a gradually declining trajectory for the index, though this does not imply a causal relationship with DANANTARA. These findings contribute to the literature by demonstrating that the primary channel through which a nascent SWF affects domestic equity markets is volatility rather than returns. The policy implications highlight the importance of for transparent governance and consistent communication in mitigating market uncertainty surrounding DANANTARA's operations.

References

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014(1), 614342. https://doi.org/10.1155/2014/614342

Adewole, A. I. (2024). On the Hybrid of Arima and Garch Model in Modelling Volatilities in Nigeria Stock Exchange. Bima Journal of Science and Technology, 8(2A), 169–180. DOI: 10.56892/bima.v8i1.601

Almarashi, A. M., Abbasi, U., Saman, H., Alzahrani, M. R., & Khan, K. (2018). Modelling volatility in stock prices using ARCH/GARCH. Sci. Int, 30(1), 89–94.

Babu, C. N., & Reddy, B. E. (2014). Selected Indian stock predictions using a hybrid ARIMA-GARCH model. 2014 International Conference on Advances in Electronics Computers and Communications, 1–6. https://doi.org/10.1109/ICAECC.2014.7002382

Balding, C. (2011). A portfolio analysis of sovereign wealth funds. London: Imperial College Press.

Beck, R., & Fidora, M. (2008). The impact of sovereign wealth funds on global financial markets. Occasional Paper Series European Central Bank., 43(6), 349–358No 91/July 2008.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Boretos, G. P. (2009). The future of the global economy. Technological Forecasting and Social Change, 76(3), 316–326. https://doi.org/10.1016/j.techfore.2008.06.003

Bortolotti, B., Fotak, V., Megginson, W., & Miracky, W. (2008). The financial impact of sovereign wealth fund investments in listed companies. European Finance Associaion 2009 Bergen Meetings Paper. University of Oklahoma.

Box, G.E.P., et al. (2015) Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken.

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer.

Chand, S., Kamal, S., & Ali, I. (2012). Modeling and volatility analysis of share prices using ARCH and GARCH models. World Applied Sciences Journal, 19(1), 77–82. DOI: 10.5829/idosi.wasj.2012.19.01.79

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773

Fernandes, N. (2014). The impact of sovereign wealth funds on corporate value and performance. Journal of Applied Corporate Finance, 26(1), 76–84. https://doi.org/10.1111/jacf.12056

Franses, P. H., & Van Dijk, D. (1996). Forecasting stock market volatility using (non‐linear) Garch models. Journal of Forecasting, 15(3), 229–235. https://doi.org/10.1002/(SICI)1099-131X(199604)15:3%3C229::AID-FOR620%3E3.0.CO;2-3

Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800

Kamruzzaman, M., Khudri, M. M., & Rahman, M. M. (2017). Modeling and predicting stock market returns: A case study on Dhaka stock exchange of Bangladesh. Dhaka University Journal of Science, 65(2), 97–101. https://doi.org/10.3329/dujs.v65i2.54515

Kotter, J., & Lel, U. (2008). Friends or foes? The stock price impact of sovereign wealth fund investments and the price of keeping secrets. International Finance Discussion Papers Board of Governors of the Federal Reserve System

Laeven, L and V Chhaochharia (2008), ‘DP6959 Sovereign Wealth Funds: Their Investment Strategies and Performance‘, CEPR Discussion Paper No. 6959. CEPR Press, Paris & London. https://cepr.org/publications/dp6959

Lim, C. M., & Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478–487. https://doi.org/10.1016/S2212-5671(13)00056-7

Lupiyoadi, R., Tjiptadi, A. P., & Syah, D. O. (2016, February). Analysis Of Factors Affecting The Use Of E-Commerce On Small and Medium Enterprises (SMEs) Creative Industries in Jabodetabek-Indonesia. In International Conference on Business and Information, pp. 273-293

Lyons, G. (2007). State capitalism: The rise of sovereign wealth funds (Vol. 15). HeinOnline.

Miswan, N. H., Ngatiman, N. A., Hamzah, K., & Zamzamin, Z. Z. (2014). Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Malaysia market properties and shares. Applied Mathematical Sciences, 8(140), 7001–7012. http://dx.doi.org/10.12988/ams.2014.47548

Mushtaq, R. (2011). Augmented dickey fuller test. https://dx.doi.org/10.2139/ssrn.1911068

Mustapa, F. H., & Ismail, M. T. (2019). Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model. Journal of Physics: Conference Series, 1366(1), 12130. doi:10.1088/1742-6596/1366/1/012130

Pahlavani, M., & Roshan, R. (2015). The comparison among ARIMA and hybrid ARIMA-GARCH models in forecasting the exchange rate of Iran. International Journal of Business and Development Studies, 7(1), 31–50. https://doi.org/10.22111/ijbds.2015.2198

Pástor, Ľ., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. https://doi.org/10.1016/j.jfineco.2013.08.007

Preece, D. (2024). The Future of Sovereign Wealth Funds: Challenges and Opportunities. The Palgrave Handbook of Sovereign Wealth Funds, 587–604.

Raymond, H. (2008). The effect of Sovereign Wealth Funds’ involvement on stock markets. Bank of France Occasional Paper, 7, 1–17.

Rohmah, M., Basyir, T., Abror, D., Masitoh, F. N., & Azmiyati, A. (2025). Dampak Globalisasi, Kemiskinan, Dan Kebijakan Makroekonomi Terhadap Stabilitas Ekonomi Indonesia. UTILITY: Jurnal Ilmiah Pendidikan Dan Ekonomi, 9(01), 1–25.

Rozanov, A. (2005). Who holds the wealth of nations? SSGA working paper. Retrieved from State Street Global Advisorshttp://piketty.pse.ens.fr/files/capital21c/xls/

Solihin, D., Arifin, A. L., & Nugroho, J. (2025). DANANTARA: PILAR EKONOMI ATAU BEBAN NEGARA? JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis Dan Inovasi Universitas Sam Ratulangi)., 12(1), 225–235.https://doi.org/10.35794/jmbi.v12i1.61256

Stockemer, D. (2019). Quantitative Methods for the Social Sciences. Springer International Publishing. https://doi.org/10.1007/978-3-319-99118-4

Supriyanto, E. E. (2021). Strategi Penerapan Kebijakan Sovereign Wealth Funds (SWFs) di Indonesia: Studi Literatur dan Studi Komparatif Oman. Jurnal Inovasi Ilmu Sosial Dan Politik (JISoP), 3(1), 10–21.https://doi.org/10.33474/jisop.v3i1.6959

Syah, D.O. (2019). Identifying vertical partnership among automotive component companies: empirical evidence from automotive industry in Jabodetabek, Indonesia. Economic Structures 8, 33. https://doi.org/10.1186/s40008-019-0149-z

Xu, S. N., Zhao, K., & Sun, J. J. (2015). An empirical analysis of exchange rates based on ARIMA-GJR-GARCH model. Applied Engineering Sciences: Proceedings of the 2014 AASRI International Conference on Applied Engineering Sciences, Hollywood, LA, USA, 1, 83.Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014(1), 614342. https://doi.org/10.1155/2014/614342