An investment decision-making model to predict the risk and return in stock market: An Application of ARIMA-GJR-GARCH

Hidayana, R.; Napitupulu, H.; Sukono, S.

Growing Science » Decision Science Letters » An investment decision-making model to predict the risk and return in stock market: An Application of ARIMA-GJR-GARCH

Journals

DSL Volumes

Volume 1 (10)
- Issue 1 (5)
- Issue 2 (5)

Volume 2 (30)
- Issue 1 (5)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (10)

Volume 3 (53)
- Issue 1 (15)
- Issue 2 (10)
- Issue 3 (19)
- Issue 4 (9)

Volume 4 (48)
- Issue 1 (10)
- Issue 2 (12)
- Issue 3 (14)
- Issue 4 (12)

Volume 5 (39)
- Issue 1 (12)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (9)

Volume 6 (30)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (7)

Volume 7 (41)
- Issue 1 (8)
- Issue 2 (8)
- Issue 3 (8)
- Issue 4 (17)

Volume 8 (38)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (14)
- Issue 4 (10)

Volume 9 (39)
- Issue 1 (8)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (8)

Volume 10 (43)
- Issue 1 (7)
- Issue 2 (8)
- Issue 3 (20)
- Issue 4 (8)

Volume 11 (49)
- Issue 1 (9)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (17)

Volume 12 (64)
- Issue 1 (12)
- Issue 2 (24)
- Issue 3 (13)
- Issue 4 (15)

Volume 13 (78)
- Issue 1 (21)
- Issue 2 (18)
- Issue 3 (19)
- Issue 4 (20)

Volume 14 (21)
- Issue 1 (21)

Countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 11 Issue 3 pp. 235-246 , 2022

An investment decision-making model to predict the risk and return in stock market: An Application of ARIMA-GJR-GARCH Pages 235-246 Download PDF

Authors: Rizki Apriva Hidayana, Herlina Napitupulu, Sukono Sukono

DOI: 10.5267/j.dsl.2022.3.003

Keywords: Stocks return and risk, ARIMA-GJR-GARCH, VaR, Investment decisions

Abstract: In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.

How to cite this paper

 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.

Refrences

Ali, G. (2013). EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH models for pathogens at marine recreational sites. Journal of Statistical and Econometric Methods, 2(3), 57-73.
Bakhtiar, S. M., Syata, I., Alwi, W., Ibnas, R., & Anugrawati, S. D. (2021, May). Estimation of Average Value at Risk (AVaR) on Sharia Joint-Stock Index Using Glosten, Jaggnathan and Runkle (GJR) model. In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020) (pp. 143-149). Atlantis Press.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Brooks, C. (2008). RATS Handbook to accompany introductory econometrics for finance. Cambridge Books.
Bucevska, V. (2013). An Empirical evaluation of GARCH models in value-at-risk estimation: Evidence from the Macedonian stock exchange. Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy, 4(1), 49-64.
Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). The econometrics of financial markets. Macroeconomic Dynamics, 2(4), 559-562.
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). GLOSTEN JAGANNATHAN RUNKLE-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICS (GJR-GARCH) METHODE FOR VALUE AT RISK (VaR) FORECASTING. Proceeding of ICMSE, 3(1), M-63.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Hartono, E. F. & Wahyuni, D. U. (2017). Analisis faktor-faktor keputusan investasi pada perusahaan property dan real estate. Jurnal Ilmu dan Riset Manajemen, 6(6).
Bursa Efek Indonesia. (2021). IDX Stock Index Handbook. Jakarta: BEI.
Kalfin, Sukono, & Carnia, E. (2019). Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix. International Journal of Recent Technology and Engineering (IJRTE), Volume-8 Issue-3, 7818-7822.
Lee, O. (2007). A study on Box-Cox transformed threshold GARCH (1, 1) process. Communications for Statistical Applications and Methods, 14(1), 141-146.
Mittnik, S., Paolella, M. S., & Rachev, S. T. (2002). Stationarity of stable power-GARCH processes. Journal of Econometrics, 106(1), 97-107.
Nilsson, C. (2017). Forecasting Swedish stock market volatility and value-at-risk: A comparison of EWMA and GARCH models.
Olowe, R.A. (2010). Oil Price Volatility. Global Financial Crisis and The Mont of The Year Effect. International Journal of Business and Management. Vol 5.
Rupert, D. (2011). Statistics and Data Analysis for Financial Engineering. 2nd ed., Spinger text in statistics, New York.
Sukono., Soeryana, E., and Simanjuntak, A. (2019). ARIMA-GARCH Model for Estimation of Value-at-Risk and Expected shortfall of Some Stocks in Indonesia Capital Market. Proceedings of the International Conference on Industrial Engineering and Operations Management Riyadh. Saudi Arabia.
Sukono., Susanti, D., Najmia, M., Lesmana, E., Napitupulu, H., Supian, S., and Putra, A.S. (2017). Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach. IOP Conference Series: Materials Science and Engineering. 332 012046.
Sukono, Kartiwa, A., Subartini, B., Hidayat, Y., & Bon, A.T. (2018). Estimation of Conditional Value-at-Risk under Assets Liability Model with Non Constant Volatility. Proceedings of the International Conference on Industrial Engineering and Operations Management, Bandung, Indonesia, March 6-8, 2018, 2726-2733.
Tamilselvan, M., & Vali, S. M. (2016). Forecasting stock market volitility-evidence from muscat security market using garch models. International Journal of Commerce and Finance, 2(1), 37-53.
Tsay, R.S. (2005). Analysis of financial time series. 2nd ed., Chicago: John Wiley & Sons. Inc. University of Chicago.
Ummah, A., Ahsan, M., & Anas, A. (2021). Students' investment decisions with intention as an intervening variable. Equilibrium: Jurnal Ekonomi Syariah, 9(1), 135-152.