[PDF] Top 20 Recursive Quantile Estimation with Application to Value at Risk
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Recursive Quantile Estimation with Application to Value at Risk
... Means and standard errors of the overall relative mean squared errors (ReMSE) for EWSA, CAViaR and Hybrid models are presented in Table 8.1 for α = 0.05 and Table 8.2 for α = 0.01. For all DGPs except IGARCH95, the ... See full document
120
Robust Inference with Quantile Regression in Stochastic Volatility Models with application to Value at Risk calculation
... widespread application in ...function, estimation is a challenging ...robust estimation techniques are desirable. Also, in the context of risk assessment when the underlying model is SV, ... See full document
142
Value at Risk Estimation using the Kappa Distribution with Application to Insurance Data
... in value at risk ...likelihood estimation methods for evaluating the parameters of the Kappa ...different estimation methods for this distribution under complete and censored ...the ... See full document
10
New Approach to Density Estimation and Application to Value at Risk
... To place the new approach in context, we provide empirical support by employing intra-day E-mini S&P 500 European-style weekly options data and also the E-mini S&P 500 Futures intra-day data from August 2009 to ... See full document
10
Application of Nonparametric Quantile Regression to Estimating Value at Risk.
... this estimation process does not suffer from this ...linear quantile regression, which means that the estimation is a global minimizer of ...two-stage estimation will achieve the global ... See full document
91
Bayesian CV@R/super quantile regression
... conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar, Royset and Miranda ...empirical application to data used by RRM as well to another data set on ... See full document
20
Varying the VaR for Unconditional and Conditional Environments,
... market risk measures accounting for the fat-tailed characteristic of futures ...Extreme value methods based on order statistics model the tail values of a distribution in an unconditional and conditional ... See full document
32
Estimation risk effects on backtesting for parametric value-at-risk models
... account estimation risk ...this application that Kupiec’s method understates risk ...overstates risk exposure we report a version of the test statistics S n and S e n without absolute ... See full document
40
Firm Specific Risk and Return: Quantile Regression Application
... return. Quantile regression is a powerful tool which describes the whole distribution of dependent ...regression, quantile regression is able to indicate the effect of FSR on each of probable values of ... See full document
18
The Uncertainty Reduction for the Refined Sample Mean of Combined Quantities
... applying quantile-based maximum likelihood estimation (QMLE) to mean value estimation of normal distribution in sparse data condition was ... See full document
6
Boosted Classification Trees and Class Probability/Quantile Estimation
... As mentioned earlier, the realization that boosting often overfits the CCPF but not the median value of the CCPF has many important implications. It brings into question whether it is right to attribute the ... See full document
31
Optimal risk transfer under quantile-based risk measurers
... robust estimation of the risk measure will make the methodology more robust, and hence the goal of this section is to robustly estimate the p-quantiles of a data set (for different values of 0 < p < ... See full document
27
Filtered Extreme Value Theory for Value At Risk Estimation
... conditional quantile is the best ...conditional quantile decreases h-step ahead forecasting of number of exceptions and this shows that filtered expected shortfall with 40 days conditional quantile ... See full document
12
Looking for efficient qml estimation of conditional value at risk at multiple risk levels
... joint estimation of conditional Value-at-Risk (VaR) at several levels, in the framework of general GARCH-type ...of risk levels, a two-step procedure based on a generalized ...and ... See full document
21
Conditional Value at Risk and Average Value at Risk: Estimation and Asymptotics
... Estimators obtained from di erent methods are omputed; quantile based estimator referred to as \QVaR" and LSR estimator referred to as \RVaR" for the onditional VaR, mixed quantile estim[r] ... See full document
36
Combined Estimation for Quantile Regression
... Extreme Value Theory (EVT), which estimate the tail parameter of the return distribution, and quantile regression, which model the conditional quantiles ...via quantile regression is a natural ... See full document
103
On line evolution of Takagi Sugeno fuzzy models
... the recursive clustering and modified recursive least squares (RLS) estimation, is studied in ...evolution, recursive clustering, RLS ... See full document
6
Efficient Estimation of an Additive Quantile Regression Model
... conditional quantile functions for α = ...both quantile functions is similar to those of the median for both distance and ...that quantile estimates of the semi-parametric approach are functions of ... See full document
36
M Quantile Models for Small Area Estimation
... Finally, in Table 8 we show the coverage performances of confidence intervals for the regional means based on M-quantile and Expectile estimates and the MSE estimator (10). Here we see that the larger ... See full document
33
Innovation and market value: A quantile regression analysis
... case, estimation of linear models by quantile regression may be preferable to the usual regression methods for a number of ...normality, quantile regression results are characteristically robust to ... See full document
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