scholarly journals Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo

2021 ◽  
Author(s):  
Zhijian He
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Quasi Monte Carlo ◽  
Sensitivity Estimation

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

scholarly journals Análise de um modelo inteligente de contratação de energia elétrica no curto prazo para distribuidoras

2012 ◽  
Vol 23 (6) ◽  
pp. 711-725
Author(s):  
Juan G. Lazo Lazo ◽  
Delberis Araujo Lima ◽  
Karla Figueiredo
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Para Determinar

Neste artigo é apresentada uma nova abordagem de um modelo inteligente de otimização sob incerteza para determinar a contratação de energia elétrica no curto prazo (referente aos leilões A-1 e Ajuste) para distribuidoras de energia elétrica. Nesse modelo estão incluídas todas as regras de contratação e repasse à tarifa, definidas pela ANEEL, para as distribuidoras. O processo de otimização utiliza um algoritmo genético, e busca minimizar o custo associado à contratação de energia elétrica, as penalidades por subcontratação e o custo da liquidação (compra/venda) desta energia ao PLD (Preço de Liquidação das Diferenças). A contratação ótima é calculada considerando vários cenários de consumo, obtidos a partir de simulação Monte Carlo, para um período de cinco anos de análise. As decisões de contratação do modelo são tomadas nos dois primeiros anos desse período. A avaliação dos resultados do sistema é feita considerando uma combinação entre o Valor Esperado (VE) da distribuição de custos e o CVaR (Conditional Value at Risk), para os diferentes cenários de consumo. O modelo também usa o PLD_robusto, que busca minimizar a exposição da distribuidora ao PLD. Para ilustrar os resultados do modelo proposto é apresentado um estudo de caso baseado em dados reais. Os resultados obtidos são comparados com alguns resultados de contratação que não consideram o modelo de otimização proposto. Essa comparação é feita para se verificar o quanto o método proposto pode ser melhor que soluções baseadas apenas em análises intuitivas. Além disso, estudos adicionais são apresentados considerando os mecanismos de compensação de sobras e déficits, notadamente MCSD4% e MCSD_Ex-post, previstos na legislação vigente do setor elétrico para minimizar os riscos associados à contratação de energia para as distribuidoras.

scholarly journals Pengukuran Conditional Value At Risk (CVAR) Pada Aset Tunggal dengan Metode Simulasi Monte Carlo

2021 ◽  
Vol 18 (1) ◽  
pp. 130-135
Author(s):  
T N Ondja ◽  
S Musdalifah ◽  
D Lusiyanti ◽  
Andri
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Central Asia ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Monte Carlo Data

Conditional value at risk (CVaR) merupakan suatu ukuran risiko yang memperhitungkan kerugian melebihi tingkat Value at risk (VaR). Tujuan dalam penelitian ini adalah mendapatkan hasil pengukuran Conditional Value at Risk (CVaR) pada aset tunggal dengan menggunakan metode simulasi Monte Carlo. Data yang digunakan dalam penelitian ini ialah harga penutupan saham harian PT. Bank Central Asia Tbk (BBCA.JK) dengan tingkat kepercayaan . Dari hasil perhitungan CVaR pada tingkat kepercayaan 99%, menghasilkan nilai CVaR sebesar . Hal ini dapat diartikan bahwa ada keyakinan sebesar  kerugian yang mungkin akan diderita investor tidak akan melebihi  dalam jangka waktu satu hari setelah tanggal 30 September 2020, atau dengan kata lain ada kemungkinan sebesar  bahwa kerugian investasi pada periode tersebut sebesar  atau lebih dalam jangka waktu satu hari dengan dana awal yang diinvestasikan sebesar Rp

scholarly journals Incorporating stand level risk management options into forest decision support systems

2018 ◽  
Vol 26 (3) ◽  
pp. e013
Author(s):  
Kyle Eyvindson ◽  
Rami Saad ◽  
Ljusk Ola Eriksson
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Decision Support ◽  
Value At Risk ◽  
Net Present Value ◽  
Conditional Value At Risk ◽  
Management Options ◽  
Monte Carlo Approach ◽  
Small Forest

Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions.Area of study: A case study examines a small forest holding from Jönköping, Sweden.Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand.Main results: Visual representation of the errors can be used to highlight which decision would be most beneficial dependent on the decision maker’s opinion of the forest inventory results. At a stand level, risk preferences can be rather easily incorporated into the current forest decision support software.Research highlights: Forest management operates under uncertainty and risk. Methods are available to describe this risk in an understandable fashion for the decision maker.

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