scholarly journals Portfolio Optimization Using Value-at-Risk: Assessment Using Historical Simulation Method for Energy Stocks

2017 ◽  
Author(s):  
Debasis PATNAIK ◽  
Utkarsh TIWARI
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Portfolio Optimization ◽  
Value At Risk ◽  
Simulation Method ◽  
Historical Simulation

PENGUKURAN RISIKO PORTFOLIO DENGAN MENGGUNAKAN METODE VALUE AT RISK (VAR)

2013 ◽  
Author(s):  
Fajri Adrianto ◽  
Laela Susdiani
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Method ◽  
Simulation Method ◽  
Risk Measurement ◽  
Portfolio Risk ◽  
Historical Simulation ◽  
Stock Investment ◽  
Risk Investment

Value at Risk (VAR) is a risk measurement method that use in risk investment calculation. VAR shows risk in nominal. This research calculate risk portfolio of stock using VAR method and measure whether VAR value overvalued or underestimated. Using historical simulation method is found VAR value tend to decrease when stock investment consist more stocks in the portfolio. Risk investment calculation consistent with standar devistion as risk measurement, which the more investment diversified the less the risk in the investment. Then, using backtesting reveal that VAR tend too high in portfolio consisting small number of stocks. VAR value can accepted in the portfolio that consist many stocks or the more investment diversified the more accurate VAR value as risk measurement.

An Improved Historical Simulation Method to Estimate the Amount of Refined Oil Retail Value at Risk VaR

2013 ◽  
Vol 734-737 ◽  
pp. 1711-1718
Author(s):  
Yong Tao Wan ◽  
Zhi Gang Zhang ◽  
Lu Tao Zhao
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Financial Risk ◽  
Rapid Development ◽  
Simulation Method ◽  
Support Vector ◽  
Refined Oil ◽  
Historical Simulation ◽  
Oil Market

The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.

Incorporating the Time-Varying Tail-Fatness into the Historical Simulation Method for Portfolio Value-at-Risk

2006 ◽  
Vol 09 (02) ◽  
pp. 257-274 ◽  
Author(s):  
Chu-Hsiung Lin ◽  
Chang-Cheng Chang Chien ◽  
Sunwu Winfred Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Moving Average ◽  
Simulation Method ◽  
Estimation Accuracy ◽  
Time Varying ◽  
Historical Simulation ◽  
Extreme Risk ◽  
Weighted Moving Average

This study extends the method of Guermat and Harris (2002), the Power EWMA (exponentially weighted moving average) method in conjunction with historical simulation to estimating portfolio Value-at-Risk (VaR). Using historical daily return data of three hypothetical portfolios formed by international stock indices, we test the performance of this modified approach to see if it can improve the precise forecasting capability of historical simulation. We explicitly highlight the extended Power EWMA owns privileged flexibilities to capture time-varying tail-fatness and volatilities of financial returns, and therefore may promote the quality of extreme risk management. Our empirical results, derived from the Kupiec (1995) tests and failure ratios, show that our proposed method indeed offers substantial improvements on capturing dynamic returns distributions, and can significantly enhance the estimation accuracy of portfolio VaR.

A General Revised Historical Simulation Method for Portfolio Value-at-Risk

2005 ◽  
Vol 8 (2) ◽  
pp. 87-103 ◽  
Author(s):  
Chu-Hsiung Lin ◽  
Chang-Cheng Chang Chien ◽  
Sunwu Winfred Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Method ◽  
Historical Simulation

scholarly journals METODE HISTORIS UNTUK PERHITUNGAN VALUE AT RISK PADA MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDACITY IN MEAN

2016 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Alfi Reny Kusumaningtyas ◽  
Abdul Aziz
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Parametric Method ◽  
Simulation Method ◽  
Financial Industry ◽  
Main Method ◽  
Historical Simulation ◽  
Historical Method ◽  
Implementation Model ◽  
Main Motive

Investment is a commitment of the placement of the data on an object or a few investments with expectations will benefit in the future. The main motive is to seek investment gain or profit in a certain amount, but behind the good side there is one side that can harm or the risk of, for it required a measurement of risk where methods of value at risk (VaR) is very popular is widely used by the financial industry worldwide. Three main method on calculation of VaR historical method, parametric method and Monte Carlo method. So, the selected calculation of VaR GARCH-M model with historical simulation method on Bank Mandiri Tbk closing stock in 2005-2010. This research aims to know the calculation of VaR model GARCH-M through the historical method and implementation model GARCH-M on the computation of VaR via simulation on closing stock Bank Mandiri Tbk. Historical method approach is a model calculation of VaR is determined by the value of the past (historical) or return generated by simulation (repetition) of data used. The measures undertaken that explains the historical simulation method VaR models in the estimation of GARCH-M with a normal distribution, then apply GARCH-M in case of loss obtained by investors after investing with the help of Minitab software, E-views software and Matlab software.

scholarly journals Financial Risk Quantification of Indian Agro-Commodities using Value At Risk

2019 ◽  
Vol 8 (6) ◽  
pp. 5138-5144
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Risk ◽  
Commodity Prices ◽  
Simulation Method ◽  
Historical Trends ◽  
Spot Prices ◽  
Historical Simulation ◽  
Secondary Sources ◽  
Guar Seed

Indian commodity traders are exposed to various risks like price risk, market risk, financial risk, credit risk, etc. To understand the risk resulting in the financial impact, this paper attempts to assess the historical trends of commodity prices and probability of loss occurrence in the commodity invested. The present study analyses five Indian agro commodities namely, Almond, Cardamom, Cotton, Guar Seed and Wheat using the data collected from secondary sources like Multi Commodity Exchange (MCX), Securities Exchange Board of India (SEBI) etc. This paper uses the Historical Simulation method for the calculation of Value at Risk (VaR) by considering spot prices of the commodities on MCX for a five year period (2013-2018). It is established that Value at Risk (VaR) is a relevant measure to arrive at risk which is useful for the commodity traders to estimate the financial risk and thus control the risk exposure

scholarly journals Value-at-Risk Estimation Method Based on Normal Distribution, Logistics Distribution and Historical Simulation

2020 ◽  
Vol 1 (1) ◽  
pp. 13-18
Author(s):  
Dwi Susanti ◽  
Sukono Sukono ◽  
Maria Jatu Verrany
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Value At Risk ◽  
Risk Estimation ◽  
Estimation Method ◽  
Simulation Method ◽  
Logistic Distribution ◽  
Historical Simulation ◽  
Logistics Distribution ◽  
Portfolio Returns

This paper discusses the risk analysis of single stock and portfolio returns. The stock data analyzed are BNI, BRI shares and portfolio. After obtaining a stock return, value at risk (VaR) will be estimated using the normal distribution approach, logistic distribution, and historical simulation. From the VaR results, a backtest is then conducted to test the validity of the model and the backtest results for BNI and the portfolio produce a smaller QPS on the historical simulation method compared to the normal distribution and logistics distribution approaches. This shows that BNI VaR and VaR portfolios with the historical simulation method are more consistent than other methods. While the backtest results for BRI produced the smallest QPS on the normal distribution approach compared to the logistical distribution and historical simulation approaches. This shows that the VaR BRI using the normal distribution approach is more consistent than the other methods.

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