scholarly journals VaR BASED RISK MANAGEMENT

2013 ◽  
Vol 1 ◽  
pp. 25-33
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Financial Risk ◽  
Heavy Tails ◽  
Moving Average ◽  
Laplace Distribution ◽  
Asymmetric Laplace Distribution ◽  
New Approach ◽  
Market Indexes ◽  
Weighted Moving Average

In this paper we discuss the Value–at–Risk concept and we analyse the market risk by using EWMA approach. EWMA (exponentially weighted moving average) forecasting technique is a popular measure of various risks in financial risk management. We will compare standard EWMA, robust EWMA and skewed EWMA forecast of VaR. JP Morgan standard EWMA is derived from Gaussian distribution. Robust EWMA is based on Laplace distribution and skewed EWMA is a new approach derived from an asymmetric Laplace distribution. Asymmetric Laplace distribution takes into account both skewness and heavy tails in return distribution and the time varying nature of them in practice. Skewed EWMA VaR is a generalization of the standard EWMA method. Using these approaches we will analyse selected financial series (three European market indexes and one exchange rate). We have found andconfirmed that skewed EWMA forecasting of VaR outperforms the standard EWMA method.

RISIKO PASAR: PERBANDINGAN MODEL EWMA DAN GARCH PADA NILAI TUKAR RUPIAH TERHADAP US DOLLAR

2010 ◽  
Vol 5 (2) ◽  
pp. 153
Author(s):  
Ari Christianti
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Risk Model ◽  
Moving Average ◽  
Var Model ◽  
Banking Supervision ◽  
Us Dollar ◽  
Exponential Weighted Moving Average ◽  
The Garch Model ◽  
Weighted Moving Average

Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision. Using daily exchange rate from January 2006-February 2008, will be compared measuring volatility between EWMA (Exponential Weighted Moving Average) and GARCH (Generalized Autoregressive Conditional Heterocedasticity). The results show that GARCH methods have considerably better power properties in measuring the volatility than the EWMA methods. However, the number of exceptions from the GARCH model, although much less than the EWMA model but the numbers were still above 5% and 1% (confidence level of 95% and 99%). The arguments for explained this finding is a pressure from stakeholders or the existence of an economic events that result in changes in exposure due to the different policies. As a result, the VaR model would be inaccurate to reality.Keywords: volatility, backtesting, EWMA, and GARCH

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.

MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY

2006 ◽  
Vol 05 (03) ◽  
pp. 503-512 ◽  
Author(s):  
JING YAO ◽  
ZHONG-FEI LI ◽  
KAI W. NG
Keyword(s):  
Empirical Study ◽  
Value At Risk ◽  
Stock Exchange ◽  
Statistical Tests ◽  
Moving Average ◽  
Specification Test ◽  
Model Risk ◽  
Lr Test ◽  
Weighted Moving Average ◽  
Better Than

This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.

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 Risk assessment for ancillary services

2019 ◽  
Vol 9 (3) ◽  
pp. 1561
Author(s):  
Omer Hadzic ◽  
Smajo Bisanovic
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Power System ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Ancillary Services ◽  
Conditional Value At Risk ◽  
Risk Optimization ◽  
System Risk

The power trading and ancillary services provision comprise technical and financial risks and therefore require a structured risk management. Focus in this paper is on financial risk management that is important for the system operator faces when providing and using ancillary services for balancing of power system. Risk on ancillary services portfolio is modeled through value at risk and conditional value at risk measures. The application of these risk measures in power system is given in detail to show how to using the risk concept in practice. Conditional value at risk optimization is analysed in the context of portfolio selection and how to apply this optimization for hedging a portfolio consisting of different types of ancillary services.

scholarly journals Risk Measurement of Futures Portfolio: An Empirical Study Based on PGARCH - EVT - Copula Model

2017 ◽  
Vol 4 (5) ◽  
pp. 45
Author(s):  
Liang Su ◽  
Lan-Ya Ma
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Financial Risk ◽  
Financial Time Series ◽  
Simulation Method ◽  
Expected Shortfall ◽  
Risk Measurement ◽  
Copula Model ◽  
Financial Risk Management ◽  
The Right

Financial risk management takes an important part of continuing financial globalization. From the point of financial risk management, financial risk should be controlled at the right level. Considering the characteristics of financial time series, we construct the PGARCH-EVT-Copula model that includes different aspects of statistical features in measuring the risk. With this model, we measure Value at Risk and Expected Shortfall of the futures portfolio and compare them in the risk measurement and testify the reliability with the help of Monte-Carlo simulation method. Finally, we draw a conclusion that at 95% confidence level, Expected Shortfall can better estimate the risk of assets price extreme changing. This paper provides a risk management method for stabilizing the financial market.

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 Strategic Wholesale Pricing for an Incumbent Supplier Facing with a Competitive Counterpart

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jianwu Sun
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Financial Risk ◽  
Wholesale Price ◽  
Pricing Strategy ◽  
Conditional Value At Risk ◽  
Profit Function ◽  
Financial Risk Management ◽  
Future Loss ◽  
Wholesale Pricing

We introduce a wholesale pricing strategy for an incumbent supplier facing with a competitive counterpart. We propose a profit function which considers both the present loss and future loss from a wholesale price and then study the optimal wholesale prices for different objectives about this profit function for the incumbent supplier. First, we achieve an optimal wholesale price for the incumbent supplier to maximize his expected profit. Then, to reduce the risk originating from the fluctuation in the competitive supplier’s wholesale price, we integrate the conditional value-at-risk (CVaR) measure in financial risk management into this study and derive an optimal wholesale price to maximize CVaR about profit for the incumbent supplier. Besides, the properties of the two optimal wholesale prices are discussed. Finally, some management insights are suggested for the incumbent supplier in a competitive setting.

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