Comparison of Value at Risk (VaR) in Risk Analysis: Historical, Variance Covariance and Monte Carlo Methods

Page Numbers: 126-134
Published: 2024-09-04
Digital Object Identifier: 10.58578/mjms.v2i3.3778
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  • Meirinda Fauziyah Mulawarman University, Indonesia
  • Andrea Tri Rian Dani Mulawarman University, Indonesia
  • Hadi Koirudin Mulawarman University, Indonesia
  • Ennesya Estya Budi Mulawarman University, Indonesia
  • Khairunnisa Avrilia Mulawarman University, Indonesia
  • Noor Hikmah Watika Mulawarman University, Indonesia

Abstract

Value at Risk (VaR) is a method used to measure financial risk in a company. VaR calculations are often used to calculate the level of loss from shares in a company, such as bank shares. The aim of this research is to determine the level of losses in Bank Central Asia shares using the historical method, the Variance-covariance method, and the Monte Carlo method. the results showed that with an initial investment of $50 and using the Historical method at a significant level of 95%, the VaR value was obtained at $16.42 or IDR. 267.301 and at the 90% significant level, the VaR value was obtained at $12.41 or IDR. 202.022. Based on the Variance-covariance method with an initial investment of 50$ at the 95% significant level, the VaR value is  obtained at $16.42 or IDR. 267,301 and at the 90% significant level, the VaR value is obtained at $12.79 or IDR. 208.208. Meanwhile, based on the Monte Carlo method with an initial investment of $50, at a significant level of 95%, the VaR value is obtained at $16.46 or IDR. 267,952 and at the 90% significance level, the VaR value is obtained at $12.84 or IDR. 209.022. Based on the three methods used, it was concluded that the Monte Carlo method gave greater results compared to the other two methods.

Keywords: Historical; Monte Carlo; Stocks; Value at Risk; Variance-covariance
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Fauziyah, M., Dani, A. T. R., Koirudin, H., Budi, E. E., Avrilia, K., & Watika, N. H. (2024). Comparison of Value at Risk (VaR) in Risk Analysis: Historical, Variance Covariance and Monte Carlo Methods. Mikailalsys Journal of Mathematics and Statistics, 2(3), 126-134. https://doi.org/10.58578/mjms.v2i3.3778

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