scholarly journals PENERAPAN METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) DALAM PENGUKURAN RISIKO INEVSTASI SAHAM PORTOFOLIO UNTUK VOLATILITAS HETEROGEN

2018 ◽  
Vol 7 (3) ◽  
pp. 248-259
Author(s):  
Heni Dwi Wulandari ◽  
Mustafid Mustafid ◽  
Hasbi Yasin
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Moving Average ◽  
Simulation Method ◽  
Investment Risk ◽  
Exponentially Weighted Moving Average ◽  
Historical Simulation ◽  
Updating Procedure ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Risk measurement is important in making an investment. One tool used in the measurement of investment risk is Value at Risk (VaR). VaR represents the greatest possible loss of investment with a given period and level of confidence. In the calculation of Value at Risk requires the assumption of normality and homogeneity. However, financial data rarely satisfies that assumption. Exponentially Weighted Moving Average is one method that can be used to overcome the existence of a heterogeneous variant. Daily volatility is calculated using the EWMA method by taking a decay factor of 0.94. VaR portfolio of ASII, BBNI and PTBA stocks is calculated using historical simulation method from the revised portfolio return with Hull and White volatility updating procedure. VaR values obtained are valid at a 99% confidence level based on the validity test of Kupiec PF and Basel rules. Keywords: Value at Risk (VaR), Portfolio, EWMA, Historical Simulation, Volatility Updating

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.

scholarly journals FORECASTING VALUE-AT-RISK WITH TIME-VARYING VARIANCE, SKEWNESS AND KURTOSIS IN AN EXPONENTIAL WEIGHTED MOVING AVERAGE FRAMEWORK

2015 ◽  
Vol 10 (01) ◽  
pp. 1550005 ◽  
Author(s):  
ALEXANDROS GABRIELSEN ◽  
AXEL KIRCHNER ◽  
ZHUOSHI LIU ◽  
PAOLO ZAGAGLIA
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Moving Average ◽  
Time Varying ◽  
Financial Return ◽  
Historical Simulation ◽  
Exponential Weighted Moving Average ◽  
First Four Moments ◽  
Skewness And Kurtosis ◽  
Weighted Moving Average

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average (EWMA) model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram–Charlier density in which skewness and kurtosis appear directly in the functional form of this density. In this setting, Value-at-Risk (VaR) can be described as a function of the time-varying higher moments by applying the Cornish-Fisher expansion series of the first four moments. An evaluation of the predictive performance of the proposed model in the estimation of 1-day and 10-day VaR forecasts is performed in comparison with the historical simulation, filtered historical simulation and generalized autoregressive conditional heteroscedasticity (GARCH) model. The adequacy of the VaR forecasts is evaluated under the unconditional, independence and conditional likelihood ratio tests as well as Basel II regulatory tests. The results presented have significant implications for risk management, trading and hedging activities as well as in the pricing of equity derivatives.

Sign in / Sign up

Export Citation Format

Share Document