Parameter and quantile estimation for the generalized Pareto distribution in peaks over threshold framework

Kang, Seok; Song, Jongwoo

doi:10.1016/j.jkss.2017.02.003

Cited by 43 publications

(28 citation statements)

References 20 publications

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“…Extreme value theory is often used in hydrology and climate sciences (e.g., Lamb & Kay, 2004;Cooley et al, 2007;Tramblay et al, 2013;Kang & Song, 2017). This approach states that peaks over high thresholds, i.e., amounts of rain exceeding a given threshold u, may be approximated by a generalized Pareto distribution, provided the threshold and the number of observations are large enough and some additional mild conditions are satisfied (see section 3.2 for generalized Pareto distribution definition).…”

Section: Intensity Assessmentmentioning

confidence: 99%

A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets

Rivoire

Martius

Naveau

2021

Preprint

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Section: Intensity Assessmentmentioning

confidence: 99%

A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets

Rivoire

Martius

Naveau

2021

Preprint

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“…Nantinya nilai ekstrim inilah yang akan dimodelkan distribusinya. Metode POT menerapkan teorema Pickland Dalkema-DeHann yang menyatakan bahwa semakin tinggi threshold, maka distribusi untuk data di atas threshold akan mengikuti generalized pareto distribution [10]- [11]. Asumsi data diatas threshold yang mengikuti GPD didapat dengan melihat sebaran ekor data yang menjauhi garis hampirannya.…”

Section: Extreme Value Theory (Evt)unclassified

“…Generalized Pareto Distribution (GPD) didefinsikan sebagai limit distribusi dari scaled excesses di atas nilai threshold. Misalkan adalah variabel acak dari kerugian harian dengan 2 parameter GPD, fungsi distribusi GPD dari sebagai berikut [9]- [10].…”

Section: B Generalized Pareto Distribution (Gpd)unclassified

Estimasi Potensi Klaim Maksimal Dalam Risiko Kerugian Kebakaran Rumah Dengan Metode Extreme Value Theory (Evt) Di Kota Bandung

Saputra

Hasbullah²

2020

Kubik

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“…There are many ways to fit a generalized Pareto distribution to data, c.f. [ 46 ] for a comparison of a subset of such methods. Maximum likelihood estimation is notoriously difficult as the likelihood function is undefined for portions of the parameter domain.…”

Section: Applicationsmentioning

confidence: 99%

Clustering Financial Return Distributions Using the Fisher Information Metric

Taylor

2019

Entropy

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Information geometry provides a correspondence between differential geometry and statistics through the Fisher information matrix. In particular, given two models from the same parametric family of distributions, one can define the distance between these models as the length of the geodesic connecting them in a Riemannian manifold whose metric is given by the model's Fisher information matrix. One limitation that has hindered the adoption of this similarity measure in practical applications is that the Fisher distance is typically difficult to compute in a robust manner. We review such complications and provide a general form for the distance function for one parameter model. We next focus on higher dimensional extreme value models including the generalized Pareto and generalized extreme value distributions that will be used in financial risk applications. Specifically, we first develop a technique to identify the nearest neighbors of a target security in the sense that their best fit model distributions have minimal Fisher distance to the target. Second, we develop a hierarchical clustering technique that utilizes the Fisher distance. Specifically, we compare generalized extreme value distributions fit to block maxima of a set of equity loss distributions and group together securities whose worst single day yearly loss distributions exhibit similarities.

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Parameter and quantile estimation for the generalized Pareto distribution in peaks over threshold framework

Cited by 43 publications

References 20 publications

A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets

A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets

Estimasi Potensi Klaim Maksimal Dalam Risiko Kerugian Kebakaran Rumah Dengan Metode Extreme Value Theory (Evt) Di Kota Bandung

Clustering Financial Return Distributions Using the Fisher Information Metric

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