ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

Irene Maylinda Pangaribuan, K. Dharmawan, I. W. Sumarjaya
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引用次数: 1

Abstract

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation  or  normal VaR method gives US$30.0949.
风险分析方法模拟方法卡洛控制变量
风险价值(VaR)是一种在一定时期内以一定置信水平衡量最大损失的方法。蒙特卡罗模拟是最流行的计算VaR的方法。本研究的目的是证明控制变量法是一种可用于估计VaR的方差减少方法。此外,它还将结果与正常VaR方法或分析VaR计算进行比较。控制变量法用于从所有股票中寻找新的收益,这些股票被用作控制变量的估计量。然后使用新的返回值来定义生成N个随机数所需的参数。此外,生成的数字被用来寻找VaR值。然后,该方法被应用于估计游戏和电子竞技公司股票的投资组合,这些股票是EA、TTWO、AESE、TCEHY和ATVI。结果表明,蒙特卡罗模拟在1000模拟范围内给出了41.6428美元的VaR,而分析VaR计算或正常VaR方法给出了30.0949美元。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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