Comparison of Stock Price Risk Measurement of BUMN Banks

Authors

  • Muslimin Muslimin Faculty of Economics and Business, University of Lampung

DOI:

https://doi.org/10.31334/bijak.v19i1.2024

Keywords:

Value at Risk, Covid-19 Pandemic, GARCH Model, Risk Management

Abstract

Currently, most people around the world suffered and has changing the ways of their lives. This resulted in a slowdown in global economic growth in 2020. This also affected stock markets in Indonesia in almost all sectors. In addition, the stock market performance of the financial industry was also significantly affected, including state-owned banks. This study aims to analyze the potential loss from investing in the stock market of the state bank for the next 15 days by reviewing the risk value as a tool to measure the maximum loss. The findings show that Autoregressive AR(1)-GARCH(1) is suitable for determining the models mean and variance, which are used to calculate the Value at risk (VaR) of each bank. The VaR measurement for all banks shows a negative sign indicating the investor's maximum loss from holding one of the shares of that bank for the projected period of time. Measurement of risk will be one of the things that investors will consider when investing in financial markets.

References

Akhmadi, Y., Mustofa, I., Rika, H. M., & Hanggraeni, D. (2019). Penilaian Value At Risk Dengan Pendekatan Extreme Value Theorydan Generalized Pareto Distribution Studi Kasus Bank Bumn Di Indonesia Pada Periode Tahun 2008-2018. Managament Insight: Jurnal Ilmiah Manajemen, 13(1), 63–72. https://doi.org/10.33369/insight.14.1.63-72

Ambya, Gunarto, T., Hendrawaty, E., Kesumah, F. S. D., & Wisnu, F. K. (2020). Future natural gas price forecasting model and its policy implication. International Journal of Energy Economics and Policy, 10(5), 58–63. https://doi.org/10.32479/ijeep.9676

Brockwell, P. ., & Davis, R. . (2002). Introduction to Time Series and Forecasting. Springer-Verlag.

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

Granger, C. W. ., & Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.

Lee, J. H. ., & King, M. . (1993). A locally most mean powerful based score test for ARCH and GARCH regression disturbances. Journal of Business and Economics Statistics, 11(1), 17–27.

Meng, X., & Taylor, J. W. (2020). Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data. European Journal of Operational Research, 280(1), 191–202. https://doi.org/10.1016/j.ejor.2019.07.011

Tsay, R. S. (2010). Analysis of Financial Time Series: Third Edition. In Analysis of Financial Time Series: Third Edition. https://doi.org/10.1002/9780470644560

Wong, H., & Li, W. K. (1995). Portmanteau test for conditional heteroscedasticity, using ranks of squared residuals. Journal of Applied Statistics, 22(1), 121–134. https://doi.org/10.1080/757584402

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Published

2021-12-31