Application: Value at Risk (VaR) Estimation
Value at Risk Estimation using the Kappa Distribution with Application to Insurance Data
... likelihood estimation methods for evaluating the parameters of the Kappa ...different estimation methods for this distribution under complete and censored ...the value at risk. Therefore, we ...
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Recursive Quantile Estimation with Application to Value at Risk
... empirical application study, the EWSA, CAViaR, Hybrid and Adaptive Hybrid methods are applied to generate 1-day 1% and 5% VaR estimates from the historical data of S&P500 and a hypothetical ...
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New Approach to Density Estimation and Application to Value at Risk
... also find that ES requires a larger sample size than VaR to provide the same level of accuracy. Kou et al. [20] suggest that coherent measures such as ES may not be as robust to changes in data within a finite sample. ...
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Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk
... have superior in-sample statistical performances, and (ii) they provide more accurate out-of-sample VaR measurements. Log-return time series data have been used from the S&P 500 index for the period of 1950 to 2017. All ...
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Estimation risk effects on backtesting for parametric value-at-risk models
... of Value-at-Risk (VaR) as the standard tool for measuring market ...reliable risk measures. If the underlying risk model is not cor- rectly specified, VaR estimates understate/overstate ...
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Extreme Value Theory and Value at Risk : Application to Oil Market
... The violations corresponding to the backtest in figure 5 are shown in figure 6. We use different plotting symbols to show violations of the conditional GPD, conditional normal and unconditional GPD quantile estimates. ...
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Extreme Value Theory and Value at Risk : Application to Oil Market
... The general observation would be that for the 95% VaR measures the EVT-based models and the others traditional models produce equally good VaR estimates (except for the Normal method at the 95% confidence level). As ...
28
Measuring Risk for WTI Crude Oil - An application of Value-at-Risk
... VaR estimation. The t-distribution however, seems to better capture the risk of S&P 500 while GED systematically underestimated the ...the risk, but GED accurately captured it, which would ...
54
Value at risk and extreme value theory : application to the Johannesburg Securities Exchange
... 3.2 Emerging markets Gençay and Selçuk (2004) compare the Variance-Covariance method with the normal and Student-t distribution, HS and the unconditional GPD VaR method. They test the models on the daily stock market ...
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Application of Nonparametric Quantile Regression to Estimating Value at Risk.
... We chose 3 other very popular VaR models as competitors to our models: ARMA-GARCH with skewed t-distributed errors, CaViaR model with Asymmetric Slope and Indirect GARCH. ARMA-GARCH is a very classical recursive time ...
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Value-at-risk estimation for a high-dimensional portfolio
... quadratic risk, for example) than simply extrapolating from the three or more separate averages (see Efron adn Morris ...of application of shrinkage method can be found in Ledoit and Wol↵ (2003a, 2003b, ...
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High-dimensional GARCH process segmentation with an application to Value-at-Risk
... financial risk is that the underlying asset returns are sta- ...change-point estimation, and demonstrate its good performance through an extensive simulation study and an application to the ...
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Consistent estimation of the Value at Risk when the error distribution of the volatility model is misspecified
... A simple adaptive method based on empirical moments of the residuals makes it possible to infer an optimal element within a class of potential instrumental densities. Important asymptotic efficiency gains are achieved by ...
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An Application of Extreme Value Theory for Measuring Financial Risk
... In both cases we use maximum likelihood estimation, which is one of the most common estimation procedures used in practice. We also compute likelihood-based interval estimates of the parameters and the ...
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Nonparametric estimation of Value-at-Risk
... popular risk measures, although the loss may be far from having a Normal shape or even a Student t ...Extreme value theory can be used to locate the tail of the distribution (see, Reiss and Thomas, 1997; ...
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Conditional Value at Risk and Average Value at Risk: Estimation and Asymptotics
... are one of the most important fators for investment deision making. In this paper, we onsider ways to estimate risk measures for a single asset at given market onditions. These information ould be useful for ...
36
Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics
... are one of the most important factors for investment decision making. In this paper, we consider ways to estimate risk measures for a single asset at given market conditions. These information could be useful for ...
36
Estimation of Value at Risk: Extreme value and robust approaches
... Extreme value theory (EVT) can be useful in defining supplementary risk meas- ures, because it provides more appropriate distributions to fit extreme ...extreme value index. The most prominent ...
13
Filtered Extreme Value Theory for Value At Risk Estimation
... successful risk management function to estimate unexpected loss in ...Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high ...
12
Value at Risk Estimation Using Extreme Value Theory
... extreme value distributions of Gumbel, Fréchet or Weibull ...Extreme Value distribution (GEV) is a standard form of these three distributions, and hence the series is shown to converge to ...
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