scholarly journals Pengukuran Value at Risk pada Aset Perusahaan dengan Metode Simulasi Monte Carlo

Jurnal MIPA ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Leony P. Tupan ◽  
Tohap Manurung ◽  
Jantje D. Prang
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Monte Carlo Data ◽  
Closing Price ◽  
The Mean ◽  
Mean Variance

Telah dilakukan penelitian untuk mengukur Value at Risk (VaR) pada aset perusahaan PT. Indo Tambangraya Megah Tbk (ITMG), PT. Bank Mandiri Tbk (BMRI), dan PT. Astra International Tbk (ASII) serta portofolio yang dapat dibentuk oleh ketiga aset tersebut menggunakan metode simulasi Monte Carlo. Data yang digunakan adalah data return harian diperoleh dari harga penutupan (closing price) saham harian ketiga perusahaan tersebut selama periode tahun 2011. Bobot masing-masing portofolio ditentukan dengan metode Mean Variance Efficient Portofolio. Hasil pengukuran menunjukan bahwa jika dana yang diinvestasikan sebesar Rp 100.000.000,00 dengan tingkat kepercayaan 95% dengan periode adalah 1 hari, maka VaR ITMG sebesar Rp 4.103.963,33, VaR BMRI sebesar Rp 4.060.096,67, dan VaR ASII sebesar Rp 3.353.913,33. Sedangkan VaR portofolio1 (terdiri dari aset ITMG dan BMRI) adalah Rp 3.726.543,33. VaR portofolio2 (terdiri dari aset ITMG dan ASII) adalah Rp 3.233.133,33. VaR portofolio3 (terdiri dari aset BMRI dan ASII) adalah Rp 3.278.933,33. VaR portofolio4 (terdiri dari aset ITMG, BMRI, dan ASII) adalah Rp 3.218.906,67. Nilai VaR portofolio yang lebih rendah dari VaR aset tunggal disebabkan karena adanya efek diversifikasi.Research has been conducted to measure the Value at risk (VaR) at assets PT. Indo Tambangraya Megah Tbk (ITMG), PT. Bank Mandiri Tbk (BMRI), and PT. Astra International Tbk (ASII) and portfolios that can be formed by the three assets using Monte Carlo simulation method. The data used daily return data by the three assets obtained from the closing price of daily stock over a period in 2011. The weight of each portfolio is determined by the Mean Variance Efficient Portfolio method. If the funds invested amounting to Rp 100.000.000,00 with 95% confidence level and the period is 1 day, then the results from measurement VaR ITMG is Rp 4.103.963,33, VaR BMRI is Rp 4.060.096,67 and VaR ASII is Rp 3.353.913,33. While VaR portofolio1 (consists of ITMG and BMRI asset) is Rp 3.726.543,33. VaR portofolio2 (consists of ITMG and ASII asset) Rp 3.233.133,33. VaR portofolio3 (consists of BMRI and ASII asset) is Rp 3.278.933,33. VaR portofolio4 (consists of ITMG, BMRI and ASII asset) is Rp 3.218.906,67. VaR portfolios are lower than VaR of each single asset due to diversification effects.

scholarly journals APPLICATION OF MONTE CARLO SIMULATION METHODS IN RISK MANAGEMENT

2007 ◽  
Vol 8 (3) ◽  
pp. 165-168 ◽  
Author(s):  
Alexander Suhobokov
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Value At Risk ◽  
Simulation Method ◽  
Monte Carlo Algorithm ◽  
Simulation Methods ◽  
Monte Carlo Simulation Method

The paper deals with Monte Carlo simulation method and its application in Risk Management. The author with the help of MATLAB 7.0 introduces new modification of Monte Carlo algorithm aimed at fast and effective calculation of financial organization's Value at Risk (VaR) by the example of Parex Bank's FOREX exposure.

scholarly journals Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Electricity Sector ◽  
Cost Functions ◽  
Density Estimator ◽  
Monte Carlo Simulation Method ◽  
Commercial Building ◽  
Complementary Method ◽  
The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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