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A latent trawl process model for extreme values

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Standard

A latent trawl process model for extreme values. / Noven, Ragnhild C.; Veraart, Almut E.D.; Gandy, Axel.
In: Journal of Energy Markets, Vol. 11, No. 3, 09.2018, p. 1-24.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Noven, RC, Veraart, AED & Gandy, A 2018, 'A latent trawl process model for extreme values', Journal of Energy Markets, vol. 11, no. 3, pp. 1-24. https://doi.org/10.21314/JEM.2018.179

APA

Noven, R. C., Veraart, A. E. D., & Gandy, A. (2018). A latent trawl process model for extreme values. Journal of Energy Markets, 11(3), 1-24. https://doi.org/10.21314/JEM.2018.179

CBE

Noven RC, Veraart AED, Gandy A. 2018. A latent trawl process model for extreme values. Journal of Energy Markets. 11(3):1-24. https://doi.org/10.21314/JEM.2018.179

MLA

Noven, Ragnhild C., Almut E.D. Veraart and Axel Gandy. "A latent trawl process model for extreme values". Journal of Energy Markets. 2018, 11(3). 1-24. https://doi.org/10.21314/JEM.2018.179

Vancouver

Noven RC, Veraart AED, Gandy A. A latent trawl process model for extreme values. Journal of Energy Markets. 2018 Sept;11(3):1-24. doi: 10.21314/JEM.2018.179

Author

Noven, Ragnhild C. ; Veraart, Almut E.D. ; Gandy, Axel. / A latent trawl process model for extreme values. In: Journal of Energy Markets. 2018 ; Vol. 11, No. 3. pp. 1-24.

Bibtex

@article{1554a1d9affc4618adb3b13bbfe078f4,
title = "A latent trawl process model for extreme values",
abstract = "This paper presents a new model for characterizing temporal dependence in exceedances above a given threshold. Our model is based on a class of stationary, infinitely divisible stochastic processes known as trawl processes. For use with extreme values, our model is constructed by embedding a trawl process in a hierarchical framework. This ensures that the marginal distribution is a generalized Pareto, as expected from classical extreme value theory. We also consider a modified version of this model that works with a wider class of generalized Pareto distributions (GPDs) and has the advantage of separating marginal and temporal dependence properties. The model is illustrated via various applications to environmental time series; thus, we show that the model offers considerable flexibility in capturing the dependence structure of extreme value data.",
keywords = "Conditional tail dependence coefficient, Generalized pareto distribution (GPD), Marginal transformation model, Pairwise likelihood estimation, Peaks over threshold, Trawl process",
author = "Noven, {Ragnhild C.} and Veraart, {Almut E.D.} and Axel Gandy",
year = "2018",
month = sep,
doi = "10.21314/JEM.2018.179",
language = "English",
volume = "11",
pages = "1--24",
journal = "Journal of Energy Markets",
issn = "1756-3607",
publisher = "Incisive Media Investments Ltd.",
number = "3",

}

RIS

TY - JOUR

T1 - A latent trawl process model for extreme values

AU - Noven, Ragnhild C.

AU - Veraart, Almut E.D.

AU - Gandy, Axel

PY - 2018/9

Y1 - 2018/9

N2 - This paper presents a new model for characterizing temporal dependence in exceedances above a given threshold. Our model is based on a class of stationary, infinitely divisible stochastic processes known as trawl processes. For use with extreme values, our model is constructed by embedding a trawl process in a hierarchical framework. This ensures that the marginal distribution is a generalized Pareto, as expected from classical extreme value theory. We also consider a modified version of this model that works with a wider class of generalized Pareto distributions (GPDs) and has the advantage of separating marginal and temporal dependence properties. The model is illustrated via various applications to environmental time series; thus, we show that the model offers considerable flexibility in capturing the dependence structure of extreme value data.

AB - This paper presents a new model for characterizing temporal dependence in exceedances above a given threshold. Our model is based on a class of stationary, infinitely divisible stochastic processes known as trawl processes. For use with extreme values, our model is constructed by embedding a trawl process in a hierarchical framework. This ensures that the marginal distribution is a generalized Pareto, as expected from classical extreme value theory. We also consider a modified version of this model that works with a wider class of generalized Pareto distributions (GPDs) and has the advantage of separating marginal and temporal dependence properties. The model is illustrated via various applications to environmental time series; thus, we show that the model offers considerable flexibility in capturing the dependence structure of extreme value data.

KW - Conditional tail dependence coefficient

KW - Generalized pareto distribution (GPD)

KW - Marginal transformation model

KW - Pairwise likelihood estimation

KW - Peaks over threshold

KW - Trawl process

UR - http://www.scopus.com/inward/record.url?scp=85059812926&partnerID=8YFLogxK

U2 - 10.21314/JEM.2018.179

DO - 10.21314/JEM.2018.179

M3 - Journal article

AN - SCOPUS:85059812926

VL - 11

SP - 1

EP - 24

JO - Journal of Energy Markets

JF - Journal of Energy Markets

SN - 1756-3607

IS - 3

ER -