Ozun, Alper and Cifter, Atilla and Yilmazer, Sait (2007): Filtered Extreme Value Theory for ValueAtRisk Estimation.

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Abstract
Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional valueatrisk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate valueatrisk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with studentt distribution, GARCH with skewed studentt distribution and FIGARCH by using alternative backtesting algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) hstep ahead forecasting RMSE (70 days), number of exception and hstep ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fattails in the stock returns than parametric valueatrisk models do. Besides increase in conditional quantile decreases hstep ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting.
Item Type:  MPRA Paper 

Institution:  Marmara University 
Original Title:  Filtered Extreme Value Theory for ValueAtRisk Estimation 
Language:  English 
Keywords:  Value atRisk; Filtered Expected shortfall; Extreme value theory; emerging markets 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C32  TimeSeries Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models G  Financial Economics > G0  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection 
Item ID:  3302 
Depositing User:  Atilla Cifter 
Date Deposited:  22. May 2007 
Last Modified:  07. Jan 2014 23:23 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/3302 