Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model

Rizki Apriva Hidayana; Herlina Napitupulu; Sukono Sukono

doi:10.47194/orics.v2i4.181

Authors

Rizki Apriva Hidayana
rizki20011@mail.unpad.ac.id
Herlina Napitupulu
Sukono Sukono

Keywords:

Risk, ARIMA, GJR-GARCH, Value-at-Risk

Abstract

Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.

References

Ali, A. (2022). Risk Analysis And Stock Return Of Multiple Sector Manufacturing Industry On The Indonesia Stock Exchange For The 2017-2022 Period. Asia Pacific Journal of Business Economics and Technology, 2(05), 48-62.

Dokov, S., Stoyanov, S. V., & Rachev, S. 2008. Computing VaR and AVaR of skewed-t distribution. Journal of Applied Functional Analysis, 3, 189-209.

Dritsaki, C. 2017. An empirical evaluation in GARCH volatility modeling: Evidence from the Stockholm stock exchange. Journal of Mathematical Finance, 7(2), 366-390.

Dwipa, N. M. S. 2016. Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value-at-Risk. Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika, 3(1), 25-38.

Hidayana, R., Napitupulu, H., & Sukono, S. (2022). An investment decision-making model to predict the risk and return in stock market: An Application of ARIMA-GJR-GARCH. Decision Science Letters, 11(3), 235-246.

Olowe, R. A. 2010. Oil price volatility, global financial crisis and the month-of-the-year effect. International Journal of Business and Management, 5(11), 156.

Su, Y. C., Huang, H. C., & Lin, Y. J. 2011. GJR-GARCH model in value-at-risk of financial holdings. Applied Financial Economics, 21(24), 1819-1829.

Sulistianingsih, E., & Rosadi, D. (2021, June). Risk analysis of five stocks indexed by LQ45 using credible value at risk and credible expected tail loss. In Journal of Physics: Conference Series (Vol. 1918, No. 4, p. 042023). IOP Publishing.

Tandelilin, E. 2010. Portofolio and investment. Edisi Pertama. Yogyakarta: BPFE. Tsay, R. S. 2005. Analysis of financial time series (Vol. 543). Chicago: John Wiley & Sons.

Xu, S. N., Zhao, K., & Sun, J. J. 2015. An empirical analysis of exchange rates based on ARIMA-GJR-GARCH model. In Applied Engineering Sciences: Proceedings of the 2014 AASRI International Conference on Applied Engineering Sciences, Hollywood, LA, USA (Vol. 1, p. 83). CRC Press.