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| Author | : Imre Kondor |
| Publisher | : |
| Total Pages | : 7 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure.
| Author | : Imre Kondor |
| Publisher | : |
| Total Pages | : 5 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given confidence level and a given estimation error.
| Author | : Yasuhiro Yamai |
| Publisher | : |
| Total Pages | : 56 |
| Release | : 2001 |
| Genre | : Financial futures |
| ISBN | : |
Expected shortfall is compared with Value-at-Risk (VaR) in three aspects: estimation errors, decomposition into risk factors, and optimization. Advantages and disadvantages of expected shortfall over VaR are shown, and that expected shortfall is easily decomposed (needing a larger size of sample than VaR for the same level of accuracy) and optimized, while VaR is not.
| Author | : Carl Lönnbark |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
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| ISBN | : |
| Author | : |
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| Total Pages | : |
| Release | : 2015 |
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| Author | : Simona Roccioletti |
| Publisher | : Springer Gabler |
| Total Pages | : 0 |
| Release | : 2015-12-11 |
| Genre | : Business & Economics |
| ISBN | : 9783658119072 |
In this book Simona Roccioletti reviews several valuable studies about risk measures and their properties; in particular she studies the new (and heavily discussed) property of "Elicitability" of a risk measure. More important, she investigates the issue related to the backtesting of Expected Shortfall. The main contribution of the work is the application of "Test 1" and "Test 2" developed by Acerbi and Szekely (2014) on different models and for five global market indexes.
| Author | : Sander Barendse |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions for the additional terms in the asymptotic covariance matrix that result from estimation error, and propose robust tests that account for it. Monte Carlo experiments show that the tests that ignore these terms suffer from size distortions, which are more pronounced for higher ratios of outof-sample to in-sample observations. Robust versions of the backtests perform well, although this also depends on the choice of conditioning variables. In an application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models, we find that estimation error substantially impacts the outcome of the backtests.
| Author | : Daniel Giamouridis |
| Publisher | : |
| Total Pages | : |
| Release | : 2008 |
| Genre | : |
| ISBN | : |
Christoffersen and Goncalves (2005) study the effect of parameter estimation error in computing Value at Risk and Expected Shortfall through commonly used methods including the Cornish-Fisher/Gram-Charlier approximations approach. We provide a correction to the expression used for the computation of the Expected Shortfall under the Cornish-Fisher/Gram-Charlier approximations and illustrate the effect of the error found in assessing the accuracy of Expected Shortfall point forecasts.
| Author | : Song Xi Chen |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
The expected shortfall is an increasingly popular risk measure in financial risk management and it possesses the desired sub-additivity property, which is lacking for the value at risk (VaR). We consider two nonparametric expected shortfall estimators for dependent financial losses. One is a sample average of excessive losses larger than a VaR. The other is a kernel smoothed version of the first estimator (Scaillet, 2004 Mathematical Finance), hoping that more accurate estimation can be achieved by smoothing. Our analysis reveals that the extra kernel smoothing does not produce more accurate estimation of the shortfall. This is different from the estimation of the VaR where smoothing has been shown to produce reduction in both the variance and the mean square error of estimation. Therefore, the simpler ES estimator based on the sample average of excessive losses is attractive for the shortfall estimation.
| Author | : M. R. Leadbetter |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 344 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461254493 |
Classical Extreme Value Theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity. During this period of time it has found significant application-exemplified best perhaps by the book Statistics of Extremes by E. J. Gumbel-as well as a rather complete theoretical development. More recently, beginning with the work of G. S. Watson, S. M. Berman, R. M. Loynes, and H. Cramer, there has been a developing interest in the extension of the theory to include, first, dependent sequences and then continuous parameter stationary processes. The early activity proceeded in two directions-the extension of general theory to certain dependent sequences (e.g., Watson and Loynes), and the beginning of a detailed theory for stationary sequences (Berman) and continuous parameter processes (Cramer) in the normal case. In recent years both lines of development have been actively pursued.
