scholarly journals Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

2021 ◽  
Author(s):  
Massimiliano Frezza ◽  
Sergio Bianchi ◽  
Augusto Pianese
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Computational Approach ◽  
Local Regularity ◽  
Price Process ◽  
Weighted Moving Average ◽  
Pointwise Regularity

AbstractA new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

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 A Delta Normal Approach for Modelling Risk Forecasting of Currency Portfolio

2020 ◽  
Vol 11 (4) ◽  
pp. 55-68
Author(s):  
Ardita Todri ◽  
Francesco Roberto Scalera
Keyword(s):  
Value At Risk ◽  
Average Method ◽  
Moving Average ◽  
Moving Window ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Risk Forecasting ◽  
Process Effects ◽  
International Trading ◽  
Weighted Moving Average

This research explores the benefits of a proactive model developed through delta normal approach implementation for the forecasting of currency portfolio volatility. The latter becomes a necessity for the Albanian agro exporters as they act in an international trading environment and face the de-Euroization process effects in domestic market. The forecasting of value at risk (VaR) at 99% confidence level is obtained through the implementation of a moving window containing 251 daily currency exchange rates logarithmic returns calculated by the exponentially weighted moving average method (EWMA). A decay factor of 0.94 is used in the simulated currency portfolios database (composed from six different currency positions) pertaining to 30 agro exporters in reference of 2018 year data. The analysis of incremental VaR decomposed in risk per currency unit and VaR contribution concludes that the implementation of this mechanism offers hedge opportunities and enables the agro exporters to undertake even speculative interventions.

scholarly journals Analisis Pembentukan Portofolio Optimal dan Penentuan Nilai Risiko pada Saham Syariah

2021 ◽  
Vol 1 (2) ◽  
pp. 487-498
Author(s):  
Ajeng Defi Aprilia ◽  
Ade Ali Nurdin ◽  
Muhamad Umar Mai
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Moving Average ◽  
Optimal Portfolio ◽  
Single Model ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Weighted Moving Average

The purpose of this research is to determine the optimal portfolio formation in Islamic stocks on the Jakarta Islamic Index (JII) which is listed on the Indonesia Stock Exchange with a single model. Then measure the risk value that may occur and be accepted by investors using the Value at Risk (VaR) method with the Exponentially Weighted Moving Average (EWMA) approach. By using the Single Index Model, 5 stocks are selected and form an optimal portfolio, namely ASII, ICBP, TLKM, UNTR and UNVR.

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.

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

Study on Medium-Term and Short-Term Wind Power Forecasting Methods

2013 ◽  
Vol 361-363 ◽  
pp. 318-322
Author(s):  
Gui Zhong Wu ◽  
Yuan Biao Zhang ◽  
Cheng Su ◽  
Yu Jie Liu
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Average Method ◽  
Moving Average ◽  
Forecasting Model ◽  
Power Prediction ◽  
Medium Term ◽  
Accuracy Rate ◽  
Short Term ◽  
Weighted Moving Average

In the paper, the wind power prediction is devided into medium-term forecasts and short-term forecasts. For medium-term forecasts, we use the weighted moving average method and BP neural network forecasting model, while for short-term forecasts, the ARMA model and combination forecasting model based on the maximum entropy principle are used. The application example shows that the weighted moving average method is easy and can precisely obtain the fluctuation trend of the wind power, while the accuracy rate of the BP neural network forecasting model is 91.23%, which is better than the former. The predictive results of the ARMA model are similar with actual trends and its accuracy rate is 88.98%. The combination model integrates the advantages of the BP neural network and ARMA model, and its accuracy rate is up to 92.58%.

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