scholarly journals A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification

2012 ◽  
Vol 82 (1) ◽  
pp. 71-102 ◽  
Author(s):  
Stavros Degiannakis ◽  
Pamela Dent ◽  
Christos Floros
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Simulation Approach

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

scholarly journals Stochastic approximation schemes for economic capital and risk margin computations

2019 ◽  
Vol 65 ◽  
pp. 182-218 ◽  
Author(s):  
David Barrera ◽  
Stéphane Crépey ◽  
Babacar Diallo ◽  
Gersende Fort ◽  
Emmanuel Gobet ◽  
...  
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Stochastic Approximation ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Point Of View ◽  
Economic Capital ◽  
Approximation Schemes ◽  
Risk Margin ◽  
A Value

We consider the problem of the numerical computation of its economic capital by an insurance or a bank, in the form of a value-at-risk or expected shortfall of its loss over a given time horizon. This loss includes the appreciation of the mark-to-model of the liabilities of the firm, which we account for by nested Monte Carlo à la Gordy and Juneja [17] or by regression à la Broadie, Du, and Moallemi [10]. Using a stochastic approximation point of view on value-at-risk and expected shortfall, we establish the convergence of the resulting economic capital simulation schemes, under mild assumptions that only bear on the theoretical limiting problem at hand, as opposed to assumptions on the approximating problems in [17] and [10]. Our economic capital estimates can then be made conditional in a Markov framework and integrated in an outer Monte Carlo simulation to yield the risk margin of the firm, corresponding to a market value margin (MVM) in insurance or to a capital valuation adjustment (KVA) in banking parlance. This is illustrated numerically by a KVA case study implemented on GPUs.

scholarly journals Quantifying and Correcting the Bias in Estimated Risk Measures

2007 ◽  
Vol 37 (2) ◽  
pp. 365-386 ◽  
Author(s):  
Joseph Hyun Tae Kim ◽  
Mary R. Hardy
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Bootstrap Techniques ◽  
Practical Guideline ◽  
Estimated Risk ◽  
Conditional Tail Expectation

In this paper we explore the bias in the estimation of the Value at Risk and Conditional Tail Expectation risk measures using Monte Carlo simulation. We assess the use of bootstrap techniques to correct the bias for a number of different examples. In the case of the Conditional Tail Expectation, we show that application of the exact bootstrap can improve estimates, and we develop a practical guideline for assessing when to use the exact bootstrap.

scholarly journals ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

2021 ◽  
Vol 10 (4) ◽  
pp. 192
Author(s):  
IRENE MAYLINDA PANGARIBUAN ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Variance Reduction ◽  
Reduction Method ◽  
Random Numbers ◽  
Control Variates ◽  
Popular Method ◽  
Maximum Loss

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.

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.

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