Implementation of Monte Carlo Simulation to Measure Value at Risk (VaR) in BNI Bank Stock Investments
DOI:
https://doi.org/10.31605/jomta.v8i1.6293Keywords:
Investment risk, Monte Carlo simulation, Stock return, Value at RiskAbstract
This study applies the Value at Risk (VaR) method using Monte Carlo simulation to estimate the maximum potential loss in BNI stock investments. Daily closing prices of BNI stock for 2024 were analyzed to calculate returns and assess risk at confidence levels of 99%, 95%, and 90%. The Monte Carlo simulation, performed with 1,000 iterations, produced estimated maximum losses of approximately 9.7%, 7.4%, and 5.9% of the total investment for the respective confidence levels. These findings demonstrate that a higher confidence level corresponds to a larger potential loss, highlighting the usefulness of VaR combined with Monte Carlo simulation as a tool for evaluating and managing stock investment risk.
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[6] P. Raj, “International Journal of Research Publication and Reviews Analyzing the Measurement and Management of Market Risk through The Tool - Value at Risk ( VaR ),” vol. 5, no. 8, pp. 3987–3989, 2024.
[7] P. Siber, M. Carlo, M. On, P. T. Xyz, S. Portofolio, and J. A. Bisnis, “Jurnal Administrasi Bisnis Terapan COMPARISON OF VALUE AT RISK USING HISTORICAL AND,” vol. 6, no. 2, 2024, doi: 10.7454/jabt.v6i2.1105.
[8] D. Handayani and M. N. A. Thariq, “Measurement of Value at Risk ( VaR ) in Stock Invest- ment Using Monte Carlo Simulation Method,” vol. 210, no. Best 2021, pp. 237–243, 2022.
[9] M. Lin, I. Sengupta, and W. Wilson, “Estimation of VaR with jump process : application in corn and soybean markets arXiv : 2311 . 00832v3 [ q-fin . MF ] 9 Jun 2024,” pp. 1–26, 2024.
[10] E. Lazar, J. Pan, and S. Wang, “On the estimation of Value-at-Risk and Expected Shortfall at extreme levels,” J. Commod. Mark., vol. 34, no. November 2023, p. 100391, 2024, doi: 10.1016/j.jcomm.2024.100391.
[11] M. R. Darmawan, F. Atha, and P. Widyono, “Comparative Analysis : Value at Risk ( VaR ) with Parametric Method , Monte Carlo Simulation , and Historical Simulation of Mining Companies in Indonesia,” vol. 5, no. 4, pp. 445–450, 2024.
[12] Box.et.al, Time Series Analysis (Forecastinf and Control), vol. 11, no. 1. 2019. [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI
[13] J. W. and C. J. J Reifferscheidt and 1, “Item-based Reliability-centred Life-Cycle Costing using Monte Carlo Simulation Item-based Reliability-centred Life-Cycle Costing using”, doi: 10.1088/1742-6596/2018/1/012034.
[14] H. Sibarani, Solikhun, W. Saputra, I. Gunawan, and Z. M. Nasution, “Penerapan Metode K-Means Untuk Pengelompokkan Kabupaten/Kota Di Provinsi Sumatera Utara Berdasarkan Indikator Indeks Pembangunan Manusia,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 1, pp. 154–161, 2022, doi: 10.36040/jati.v6i1.4590.
[15] A. Sayyidatun, U. Anto, F. Mika, A. H. Habbe, N. Nagu, and U. Hasanuddin, “The Effects of Learning Per Shade And Current Ratio On Stock Fot Return For Food And Beverage Sector Companies Listed On The IDX During The Covid-19 Pandemic,” vol. 11, no. 03, pp. 1328–1334, 2022.
Published
2026-04-30
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