Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach

By Alexander J. McNeil, Rüdiger Frey. 2000

1,807 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ffc8464348708fac31d21559da3681bf973729f8

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Citations in the corpus

On the use of archetypes as benchmarks (2008, positive): “To assess the predictive accuracy of our estimators, we follow McNeil and Frey [15] who propose a formal test of the hypothesis that a particular method provides an unbiased predictor of the expected shortfall.”
NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY (2016, methodology): “To our knowledge, this two stage estimation ap- proach was first proposed by McNeil and Frey (2000) in the case where m and h are indexed by a finite dimensional parameter.”
Testing the Gaussian copula hypothesis for financial assets dependences (2001, neutral): “…by a power law with the tail index ranging between 2 and 4 (de Vries 1994, Lux 1996, Pagan 1996, Guillaume et al 1997, Gopikrishnan et al 1998, McNeil and Frey 2000), by stretched exponentials (Laherrère and Sornette 1999, Gouriéroux and Jasiak 1999, Sornette et al 2000a, 2000b), or by…”
Using extreme value theory to measure value-at-risk for daily electricity spot prices (2006, methodology): “In each case, the resulting exceedences Ny total approximately 10% of the sample, which is consistent with percentages reported by McNeil and Frey (2000).”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, methodology): “22 Note that the violation frequencies for the AR(1)-GARCH(1,1) filter with either the normal or t innova- tion assumption are considerably worse than for the S&P and DAX series reported in McNeil and Frey (2000).”
On the power of generalized extreme value (GEV) and generalized Pareto distribution (GPD) estimators for empirical distributions of stock returns (2006, background): “In order to bypass these problems, some authors (Longin, 2000 ; McNiel and Frey, 2000 ; Bali, 2003 , among many others) have proposed to estimate the extreme quantiles of the distributions in a semi-parametric way, which allows (i) to avoid the model errors and (ii) to limit the sampling errors w”
Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data (2010, methodology): “One advantage of the threshold methods over the annual maxima is that they can deal with asymmetries in the tails of distributions (McNeil and Frey 2000).”
Extreme value theory and Value-at-Risk: Relative performance in emerging markets (2004, background): “McNeil and Frey (2000) study the estimation of tail-related risk measures for heteroskedastic financial time series.”