Comparison of Historical Simulation and Variance-Covariance Methods for Value at Risk Estimation of BBRI Stock
Keywords:
BBRI Stock, Kupiec Backtesting, Historical Simulation, Value at Risk, Variance-Covariance.Abstract
Stock investment is exposed to market risk arising from fluctuations in stock prices. Therefore, accurate risk measurement is essential for investors and risk managers. Among the various tools available for quantifying investment risk, Value at Risk (VaR) has gained widespread adoption as a method for determining the worst expected loss under a given probability threshold. This study compares the Historical Simulation and Variance-Covariance methods in estimating the Value at Risk of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) stock using daily closing price data from January 2, 2024, to December 31, 2025. The Jarque-Bera normality test indicated that the return data were not normally distributed, suggesting the presence of non-normal characteristics in the return distribution. Based on an assumed investment value of IDR 10,000,000, the VaR estimates at the 95% confidence level were IDR 334,739 and IDR 356,444 using Historical Simulation and Variance-Covariance, respectively. At the 99% confidence level, the estimated VaR values were IDR 534,695 and IDR 500,158, respectively. Kupiec Proportion of Failures (POF) backtesting showed that both methods produced statistically valid VaR estimates. However, Historical Simulation generated a more conservative risk estimate at the 99% confidence level, indicating a greater ability to capture extreme losses under non-normal return distributions. Therefore, Historical Simulation is recommended as the preferred method for measuring the market risk of BBRI stock.
References
[1] P. Jorion, Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed. New York: McGraw-Hill, 2007.
[2] P. H. Kupiec, "Techniques for Verifying the Accuracy of Risk Measurement Models," The Journal of Derivatives, vol. 3, no. 2, pp. 73–84, 1995, doi: 10.3905/jod.1995.407942.
[3] B. H. Wicaksono, Y. Wilandari, and A. Rusgiyono, "Perbandingan Metode Variance Covariance dan Historical Simulation untuk Mengukur Risiko Investasi Reksa Dana," Jurnal Gaussian, vol. 3, no. 4, pp. 585–594, 2014, doi: 10.14710/j.gauss.3.4.585-594.
[4] M. Y. T. Irsan, E. Priscilla, and S. Siswanto, "Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value at Risk on Banking Stock Portfolio," Jurnal Matematika, Statistika dan Komputasi, vol. 19, no. 1, pp. 241–250, 2022, doi: 10.20956/j.v19i1.21436.
[5] G. C. Bukit, "Analisis Backtesting dan Value at Risk (VaR) dengan Metode Simulasi Historis dalam Sub Sektor Bank," E-Proceeding of Management, vol. 8, no. 2, pp. 772–778, 2021.
[6] Y. Mauren, "A Comparative Analysis of Value at Risk Measurement Methods: Variance-Covariance, Historical Simulation, and Monte Carlo Approaches," 2024.
[7] A. P. Saniah, "Pengukuran Risiko Menggunakan Value at Risk dengan Metode Variance Covariance, Simulasi Historis dan Monte Carlo," 2024.
[8] A. Nurhaliza, A. Saputra, and M. Mirtawati, "Evaluasi Akurasi Value at Risk Metode Simulasi Historis melalui Backtesting pada Saham BBCA Tahun 2024," KONTAN: Jurnal Ekonomi, Manajemen dan Bisnis, 2025.
[9] M. Farianda and A. Risman, "Analisis Value at Risk (VaR) Portofolio Saham BBCA dan BBRI Menggunakan Metode Variance-Covariance, Historical Simulation, dan Monte Carlo," 2025.
[10] C. M. Jarque and A. K. Bera, "A Test for Normality of Observations and Regression Residuals," International Statistical Review, vol. 55, no. 2, pp. 163–172, 1987.
[11] K. Singh, “Transformational Approach to Analytical Value-at-Risk for near Normal Distributions,” Journal of Risk and Financial Management, vol. 14, no. 2, p. 51, 2021, doi: 10.3390/jrfm14020051.
[12] Y. A. Ghulam and B. A. Joo, “Quantifying Downside Risk in Euro Area Stock Markets: A Value at Risk Study,” Review of Economics and Development Studies, vol. 9, no. 2, pp. 99–109, 2023, doi: 10.47067/reads.v9i2.486.
[13] E. Samunderu and Y. T. Murahwa, “Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach,” Journal of Risk and Financial Management, vol. 14, no. 11, p. 540, 2021, doi: 10.3390/jrfm14110540.























