DOI: https://doi.org/10.14710/medstat.14.2.125-136

RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

*Tarno Tarno scopus  -  Department of Statistics, Diponegoro University, Indonesia
Trimono Trimono  -  Data Science Study Program, UPN Veteran Jawa Timur, Indonesia
Di Asih I Maruddani  -  Department of Statistics, Diponegoro University, Indonesia
Yuciana Wilandari  -  Department of Statistics, Diponegoro University, Indonesia
Rianti Siswi Utami  -  School of Mathematics and Statistics, The University of New South Wales Sidney, Australia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is  Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

Fulltext View|Download
Keywords: stocks portfolio; loss risk; heteroskedastic; VaR, Backtesting
Funding: Faculty of Science and Mathematics, Universitas Diponegoro under contract 4861/UN7.5.8/PP/2019

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE) More recent articles
Most cited articles
ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL ANALISIS PENGARUH LOKASI DAN KARAKTERISTIK KONSUMEN DALAM MEMILIH MINIMARKET DENGAN METODE REGRESI LOGISTIK DAN CART ANALISIS EFISIENSI BANK PERKREDITAN RAKYAT DI KOTA SEMARANG DENGAN PENDEKATAN DATA ENVOLEPMENT ANALYSIS More cited articles
  1. Ariefianto & Doddy M. (2012). Econometrics Essence and Applications with EViews. Jakarta: PT. Gelora Aksara Pratama
  2. Bollerslev. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327. doi: 10.1016/0304-4076(86)90063-1
  3. Daniel, W. W. (1989). Applied Nonparametric Statistics. Subtitles by Alex Tri Kantjono Widodo. Jakarta : Gramedia
  4. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Ination. Journal of Econometrica, 50(4), 987-1008. doi: 10.2307/1912773
  5. Faulina, R., & Suhartono. (2013). Hybrid ARIMA-ANFIS for Rainfall Prediction in Indonesia. International Journal of Science and Research (IJSR), 2(2), 159-162. doi: 10.31219/osf.io/vw8rx
  6. Ghani, I. M. Md, & Rahim, H.A. (2019). Modeling and Forecasting of Volatility using ARMA-GARCH: Case Study on Malaysia Natural Rubber Prices. IOP Conf. Ser.: Mater. Sci. Eng. 548012023. doi: 10.1088/1757-899X/548/1/012023
  7. Husnan, S. (1998). Elementary Portfolio Theory and Security Analysis. Second Edition. Yogyakarta: UPP-AMP YKPN
  8. Jadhav, D., Ramanathan, T.V., & Naik-Nimbakar, U.V. (2009). Modified Estimators of the Expected Shortfall. Journal of Emerging Market Finance, 8(2) : 87-107. doi: 10.1177/097265270900800201
  9. Kaya, P., & Guloglu, B. (2017). Modeling and Forecasting the Markets Volatility and VaR Dynamics of Commodity. Journal of BRSA Banking and Financial Markets, 11(1), 9-49
  10. Lai, K. (2016). Selecting an Optimal Portfolio for Investment in Stocks in India: A Sectoral Approach. Pacific Business Review International, 8(9)
  11. Maruddani, D.A.I. (2019). Value at Risk for Risk Measures on Stock Investment. Ponorogo: Wade Group
  12. Radovic, M., Radukic, S., & Njegomir, V. (2018). The Application of the Markowitzs Model in Efficient Portfolio Forming on the Capital Market in the Republic of Serbia. Economic Themes, 56(1), 17-34. doi: 10.2478/ethemes-2018-0002
  13. Rosadi, D. (2012). Econometrics and Applied Time Series with EViews. Yogyakarta: Andi Offset
  14. Siaw, R.O., Hene, E.D.O., & Evans, T. (2017). Investment Portfolio Optimization with GARCH. ELK Asia Pacific Journal of Finance and Risk Management, 8(2). doi: 10.16962/EAPJFRM/issn.2349-2325/2015
  15. Tsay, R. S. (2002). Analysis of Financial Time Series. New York: John Willey and Sons, Inc
  16. Wei, W.W.S. (2006). Time Series Analysis, Univariate and Multivariat Methods. Canada: Addison Wesley Publishing Combany
  17. Wabomba, M.S., Mutwiri, M.P., & Fredrick, M. (2016). Modelling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Science Journal of Applied Mathematics and Statistics. 4(2), 64-73. doi: 10.11648/j.sjams.20160402.18
  18. Zaier, I., Shu, C., Ouarda, T., Seidou, O., & Chebana, F. (2010). Estimation of ice thickness on lakes using artificial neural network ensembles. Journal of Hydrology, 383, 330-340. doi: 10.1016/j.jhydrol.2010.01.006
  19. Zhang, W., Zhang, S., & Zhao, P. (2019). On Double Value at Risk. Journal of Risk. 7(31) doi: 10.3390/risks7010031

Last update:

No citation recorded.

Last update: 2024-11-02 14:07:16

No citation recorded.