Dynamic Volatility Modeling of Indonesian Insurance Company Stocks

Budiandru Budiandru

Abstract


The Indonesian capital market is one of the investment destination countries for investors in developed countries. The development of economic conditions in Indonesia itself is considered suitable for investors to invest. Insurance sector stocks are one of the sectors that are the target of investors. This study predicts the share price of insurance companies. Data in daily form from 2010 to 2020 uses the Autoregressive Conditional Heteroskedasticity - Generalized Autoregressive Conditional Heteroscedasticity (ARCH - GARCH) method. The results showed that forecasting that was carried out until 2025 all the insurance companies studied experienced an upward trend in stock prices. Investors can manage their funds by increasing or decreasing the insurance stock portfolio and adjusting the asset allocation with the investment strategy.


Keywords


Insurance; ARCH; GARCH; Forecasting; Investment

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References


Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock Price Prediction Using the ARIMA Model. International Conference on Computer Modelling and Simulation, 105–111. https://doi.org/10.1109/UKSim.2014.67

Almezweq, M. (2015). How Does Life Insurance Business Perform and Behave : the Case of the Uk Industry.

Bahloul, S., Mroua, M., & Naifar, N. (2017). The Impact of Macroeconomic and Conventional Stock Market Variables on Islamic Index Returns under Regime Switching. Borsa Istanbul Review, 17(1), 62–74. https://doi.org/10.1016/j.bir.2016.09.003

Barth, M. E., Konchitchki, Y., & Landsman, W. R. (2013). Cost of Capital and Earnings Transparency. Journal of Accounting and Economics, 55(3), 206–224. https://doi.org/10.1016/j.jacceco.2013.01.004

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

Cafasso, P. A. L., Chela, J. L., & Kimura, H. (2018). Market Risk-Based Capital for Brazilian Insurance Companies: A Stochastic Approach. Future Business Journal, 4(2), 206–218. https://doi.org/10.1016/j.fbj.2018.06.005

Caporale, G. M., Cerrato, M., & Zhang, X. (2017). Analyzing the Determinants of Insolvency Risk for General Insurance Firms in the UK. Journal of Banking and Finance, 84, 107–122. https://doi.org/10.1016/j.jbankfin.2017.07.011

Chen, J., & Imam, P. (2013). Causes of Asset Shortages in Emerging Markets. Review of Development Finance, 3(1), 22–40. https://doi.org/10.1016/j.rdf.2012.12.002

Gour, B., & Gupta, M. C. (2012). A Review on Solvency Margin in Indian Insurance Companies. International Journal of Recent Research and Review, 2, 43–47.

Grmanová, E., & Strunz, H. (2017). The efficiency of Insurance Companies: Application of DEA and Tobit Analyses. Journal of International Studies, 10(3), 250–263. https://doi.org/10.14254/2071-8330.2017/10-3/18

Gulsun, I., & Umit, G. (2010). Early Warning Model with Statistical Analysis Procedures in Turkish Insurance Companies. African Journal of Business Management, 4(5), 623–630.

Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting. Knowledge-Based Systems, 23(8), 800–808. https://doi.org/10.1016/j.knosys.2010.05.004

Hung, J. C. (2009). A Fuzzy GARCH Model Applied to Stock Market Scenario using a Genetic Algorithm. Expert Systems with Applications, 36(9), 11710–11717. https://doi.org/10.1016/j.eswa.2009.04.018

Jhongpita, P., Sinthupinyo, S., & Chaiyawat, T. (2012). Using Decision Tree Learner to Classify Solvency Position for Thai Non-life Insurance Companies. International Journal of the Computer, the Internet and Management, 19(3), 41–46. Retrieved from http://arxiv.org/abs/1203.3031

Khan, H. H., Naz, I., Qureshi, F., & Ghafoor, A. (2017). Heuristics and Stock Buying Decision: Evidence from Malaysian and Pakistani Stock Markets. Borsa Istanbul Review, 17(2), 97–110. https://doi.org/10.1016/j.bir.2016.12.002

Kim, H. Y., & Won, C. H. (2018). Forecasting the Volatility of Stock Price Index : A Hybrid Model Integrating LSTM with Multiple GARCH-type Models. Expert Systems With Applications, 103(1), 25–37. https://doi.org/10.1016/j.eswa.2018.03.002

Kuo, C. Y. (2016). Does the Vector Error Correction Model Perform Better Than Others in Forecasting Stock Price? An Application of Residual Income Valuation Theory. Economic Modelling, 52, 772–789. https://doi.org/10.1016/j.econmod.2015.10.016

Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001). An Intelligent Stock Trading Decision Support System through Integration of Genetic Algorithm Based Fuzzy Neural Network and Artiÿcial Neural Network. Fuzzy Sets and Systems, 118, 21–45.

Lahmiri, S. (2018). Minute-ahead Stock Price Forecasting Based on Singular Spectrum Analysis and Support Vector Regression. Applied Mathematics and Computation, 320, 444–451. https://doi.org/10.1016/j.amc.2017.09.049

Lama, A., Jha, G. K., Paul, R. K., & Gurung, B. (2015). Modeling and Forecasting of Price Volatility: An Application of GARCH and EGARCH models. Agricultural Economics Research Review, 28(1), 73. https://doi.org/10.5958/0974-0279.2015.00005.1

Malik, H. (2011). Determinants of Insurance Companies Profitability : an Analysis of Insurance Sector of Pakistan. Academic Research International, 1(3), 315–321.

Nurfadila, S., Hidayat, R. R., & Sulasmiyati, S. (2015). Analisis Rasio Keuangan dan Risk Based Capital untuk Menilai Kinerja Keuangan Perusahaan Asuransi. Jurnal Administrasi Bisnis, 22(1), 1–9.

Nurlatifah, A. F., & Mardian, S. (2016). Kinerja Keuangan Perusahaan Asuransi Syariah Di Indonesia: Surplus on Contribution. Akuntabilitas, 9(1), 73–96. https://doi.org/10.15408/akt.v9i1.3590

Pahlavani, M., & Roshan, R. (2015). The Comparison of ARIMA and hybrid ARIMA-GARCH Models in Forecasting the Exchange Rate of Iran. International Journal of Business and Development Studies, 7(1), 31–50. Retrieved from http://ijbds.usb.ac.ir/article_2198_4150a14b626373be361539db9796e1de.pdf

Primayanti, A., & Denny, E. (2016). The Determinant of Financial Health on Sharia Life Insurance. Diponegoro Journal Of Management, 5(3), 1–14.

Rahmawati, A. (2017). Kinerja Keuangan dan Tingkat Pengembalian Saham: Studi Pada Perusahaan Asuransi di Bursa Efek Indonesia. Esensi, 7(1), 1–14. https://doi.org/10.15408/ess.v7i1.4724

Sumartono, & Harianto, K. A. (2018). Kinerja Keuangan Perusahaan Asuransi Di Indonesia dan Faktor-faktor yang Mempengaruhinya. Jurnal Manajemen Dan Akuntansi, 6(1), 1–14.

Taib, C. M. I. C., & Benth, F. E. (2012). Pricing of Temperature Index Insurance. Review of Development Finance, 2(1), 22–31. https://doi.org/10.1016/j.rdf.2012.01.004

Tarsono, O., Ardheta, P. A., & Amriyani, R. (2020). The Influence of Net Premium Growth, Claim Ratio, and Risk-Based Capital on the Financial Performance of Life Insurance Companies. Advances in Economics, Business and Management Research, 127, 65–68. https://doi.org/10.2991/aebmr.k.200309.015

Trabelsi, N., & Naifar, N. (2017). Are Islamic Stock Indexes Exposed to Systemic Risk? Multivariate GARCH Estimation of CoVaR. Research in International Business and Finance, 42(7), 727–744. https://doi.org/10.1016/j.ribaf.2017.07.013

Uygur, U., & Taş, O. (2014). The Impacts of Investor Sentiment on Different Economic Sectors: Evidence from Istanbul Stock Exchange. Borsa Istanbul Review, 14(4), 236–241. https://doi.org/10.1016/j.bir.2014.08.001

Wajdi, M. (2019). On the co-movements among Stock Prices and Exchange Rates Cointegration : a VAR / VECM Approach. Journal of Finance and Investment Analysis, 8(1), 61–75.

Wang, W. (2018). A Big Data Framework for Stock Price Forecasting using Fuzzy Time Series. Multimedia Tools and Applications, 77(8), 10123–10134. https://doi.org/10.1007/s11042-017-5144-5

Williams, R. M., Arthur Jr, C., & Heins. (1964). Risk Management and Insurance. New York: McGraw-Hill.


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