A diagnostic for selecting the threshold in extreme value analysis

By Armelle Guillou, Peter Hall. 2001

150 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e691573c899ee23f657d0f8fef83c5989646425c

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Citations in the corpus

A Review of Extreme Value Threshold Estimation and Uncertainty Quantification (2012, background): “However, in their work to find an optimal tail fraction Hall & Welsh (1985) showed that this tends to overestimate the tail fraction in large samples.”
ESTIMATING THE EXTREME VALUE INDEX AND HIGH QUANTILES WITH EXPONENTIAL REGRESSION MODELS (2003, background): “The asymptotic properties of γ̂Pi k and x̂ Pi p,k are discussed by Dekkers and de Haan (1989). Usually, the Pickands estimators γ̂Pi k and x̂ Pi p,k depend heavily on the number k of order statistics used, and their path as a function of k is quite jagged.”
Confidence intervals for the tail index (2001, unknown): “Moreover, some new estimators have been proposed to reduce the bias of Hill estimator (see Beirlant et al. 1999; Feuerverger and Hall 1999; Guillou and Hall 2001).”
A robust prediction error criterion for pareto modelling of upper tails (2005, background): “* Method 1 - Beirlant, Vynckier & Teugels (1996); * Method 2 - Beirlant, Dierckx, Guillou & StAricd (2002); * Method 3 - Danielsson, de Haan, Peng & de Vries (2001), based on Hall (1990); * Method 4 - Drees & Kaufmann (1998); * Method 5 - Guillou & Hall (2001).”
Estimating Extreme Quantiles of Weibull Tail Distributions (2005, background): “Various approaches are proposed in the extreme index estimation context, see Guillou and Hall ( 2001 ), Drees and Kaufmann ( 1998 ), Gomes and Oliveira ( 2001 ), among others, and could be adapted to our problem.”
Threshold Selection in Extreme Value Analysis (2016, methodology): “2 Guillou and Hall’s choice Guillou and Hall ([36]) suggest an interesting approach for choosing the threshold when fitting the Hill estimator of a tail exponent to EV data.”
Diagnostics for dependence within time series extremes (2003, background): “Other application areas are emerging, with financial time series increasingly being analysed to assess the risk from extreme events and to determine the capital that is required to control this risk (Longin, 1996; Embrechts et al., 1997).”
A Horse Race between the Block Maxima Method and the Peak–over–Threshold Approach (2018, background): “For example, one solution is to find the optimal k that minimizes the asymptotic MSE; see, e.g., Danielsson et al. (2001), Drees and Kaufmann (1998) and Guillou and Hall (2001).”
Estimating the First‐ and Second‐Order Parameters of a Heavy‐Tailed Distribution (2004, background): “Since the optimal choice of k depends on the second-order regular variation parameter, which is usually hard to estimate accurately, some new estimators are proposed to reduce the bias term; see Beirlant et al. (1999) and Guillou & Hall (2001).”
The Quantitative Modeling of Operational Risk: Between G-and-H and EVT (2007, methodology): “We confirmed this by implementing the optimal threshold selection method proposed by Guillou and Hall [15] and by applying an ad-hoc selection method, using a fixed percentage of exceedances of 5%.”