scholarly journals Evaluasi Kualitas Pengungkapan Value at Risk Perbankan Indonesia

2019 ◽  
pp. 276
Author(s):  
Buddi Wibowo ◽  
Hasna Fadhila
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Method ◽  
Market Risk ◽  
Risk Measurement ◽  
Risk Level ◽  
Risk Disclosure ◽  
Historical Simulation ◽  
Risk Disclosures

Market risk measurement of bank investment portfolios is a still problem not only among practitioners, but  also among academicians. The accuracy and quality of market risk disclosures are important issues because  transparency of the bank risk level encourages market control in the form of market discipline and it also  improve the quality of risk management carried out internally by the bank. This research measures the quality of Value at Risk disclosures carried out by Indonesian banks. The accuracy of Value at Risk in this research is measured from the Value at Risk component which contains information of yield volatility of bank trading treasury activities. To measure Value at Risk disclosure, this research runs various methods of Value at Risk measurement. This research shows Historical Simulation is a Value at Risk method that is most widely used by Indonesia banks. The empirical test results show that the Value at Risk parametric method using asymmetric volatility have better quality than the Value at Risk Historical Simulation method. This research shows that Value at Risk as measured by Historical Simulation method contains the least information of bank trading treasury yields. Keywords: value at risk; disclosure; market risk; volatility

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.

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.

Portfolio Market Risk Assessment Using Value-at-Risk (VaR) Method – Historical Stimulation

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sergei Radukanov ◽  
◽  
◽  
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Theoretical Aspect ◽  
Market Risk ◽  
Risk Measurement ◽  
Gamble Company ◽  
Historical Simulation ◽  
Toyota Motor Corporation

One of the main VaR methods on theoretical aspect – Historical Simulation to Portfolio is explained in this article. Portfolio market risk measurement is carried out towards the shares of the particular companies – The Procter & Gamble Company (PG), Toyota Motor Corporation (TM) and Nokia Corporation (NOK).

scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

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.

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 Value-at-Risk Estimation of Equity Market Risk in India

2021 ◽  
Vol 9 (1) ◽  
pp. 1-24
Author(s):  
Jitender
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Moving Average ◽  
Equity Market ◽  
Market Risk ◽  
Financial Instrument ◽  
Historical Simulation ◽  
Distributed Models

Abstract The value-at-risk (Va) method in market risk management is becoming a benchmark for measuring “market risk” for any financial instrument. The present study aims at examining which VaR model best describes the risk arising out of the Indian equity market (Bombay Stock Exchange (BSE) Sensex). Using data from 2006 to 2015, the VaR figures associated with parametric (variance–covariance, Exponentially Weighted Moving Average, Generalized Autoregressive Conditional Heteroskedasticity) and non-parametric (historical simulation and Monte Carlo simulation) methods have been calculated. The study concludes that VaR models based on the assumption of normality underestimate the risk when returns are non-normally distributed. Models that capture fat-tailed behaviour of financial returns (historical simulation) are better able to capture the risk arising out of the financial instrument.

Sign in / Sign up

Export Citation Format

Share Document