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Abstract: We propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines quasi maximum likelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model. We use our method to estimate conditional quantiles (VaR) and conditional expected shortfalls (the expected size of a return ... (Update)
Context of citations to this paper: More
.... Straumann [18] Hosking, Bonti, and Siegel [26] Huisman, Koedijk, Kool, and Palm [27] Koedijk, Huisman, and Pownall [33] McNeil and Frey [40], Heyde [25] Using di#erent approaches to the problem and di#erent sets of data, these studies consistently find high kurtosis and...
...[Smi00] suggests using Bayesian techniques to model time varying GPD parameters. In this section, we review a model proposed by McNeil and Frey [MF00] which extends the EVT methodology to models of financial time series that allow for stochastic volatility and apply this model...
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BibTeX entry: (Update)
McNeil, A.J. & Frey, R. (1998). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Preprint, Dept. of Mathematics, ETH Zurich, Switzerland. http://citeseer.ist.psu.edu/article/mcneil00estimation.html More
@misc{ mcneil98estimation,
author = "A. McNeil and R. Frey",
title = "Estimation of tail-related risk measures for heteroscedastic financial
time series: an extreme value approach",
note = "Preprint, Dept. of Mathematics, ETH Z\"urich, Switzerland.",
year = "1998",
url = "citeseer.ist.psu.edu/article/mcneil00estimation.html" }
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