IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v32y2021i8ne2697.html
   My bibliography  Save this article

A self‐exciting marked point process model for drought analysis

Author

Listed:
  • Xiaoting Li
  • Christian Genest
  • Jonathan Jalbert

Abstract

A self‐exciting marked point process approach is proposed to model clustered low‐flow events. It combines a self‐exciting ground process designed to capture the temporal clustering behavior of extreme values and an extended Generalized Pareto mark distribution for the exceedances over a subasymptotic threshold. The model takes into account the dependence between the magnitude and occurrence time of exceedances and allows for closed‐form inference on tail probabilities and large quantiles. It is used to analyze daily water levels from the Rivière des Mille Îles (Québec, Canada) and to characterize drought patterns in the Montréal area. The model is useful to generate short‐term probability forecasts and to estimate the return period of major droughts. This information on the drought events is critical to water resource professionals in planning, designing, building, and managing more efficient water resource systems to hedge against the water shortage in case of extreme droughts.

Suggested Citation

  • Xiaoting Li & Christian Genest & Jonathan Jalbert, 2021. "A self‐exciting marked point process model for drought analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    • Handle: RePEc:wly:envmet:v:32:y:2021:i:8:n:e2697
    DOI: 10.1002/env.2697
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2697
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2697?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jakob Gulddahl Rasmussen, 2013. "Bayesian Inference for Hawkes Processes," Methodology and Computing in Applied Probability, Springer, vol. 15(3), pages 623-642, September.
    2. P. Tencaliec & A.‐C. Favre & P. Naveau & C. Prieur & G. Nicolet, 2020. "Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount," Environmetrics, John Wiley & Sons, Ltd., vol. 31(2), March.
    3. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    4. P. A. W Lewis & G. S. Shedler, 1979. "Simulation of nonhomogeneous poisson processes by thinning," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 26(3), pages 403-413, September.
    5. Emma F. Eastoe & Jonathan A. Tawn, 2012. "Modelling the distribution of the cluster maxima of exceedances of subasymptotic thresholds," Biometrika, Biometrika Trust, vol. 99(1), pages 43-55.
    6. Karl Sigman & Ward Whitt, 2019. "Marked point processes in discrete time," Queueing Systems: Theory and Applications, Springer, vol. 92(1), pages 47-81, June.
    7. Christopher A. T. Ferro & Johan Segers, 2003. "Inference for clusters of extreme values," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 545-556, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alice B. V. Mello & Maria C. S. Lima & Abraão D. C. Nascimento, 2022. "A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gloria Buriticá & Philippe Naveau, 2023. "Stable sums to infer high return levels of multivariate rainfall time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.
    2. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    3. Cavaliere, Giuseppe & Lu, Ye & Rahbek, Anders & Stærk-Østergaard, Jacob, 2023. "Bootstrap inference for Hawkes and general point processes," Journal of Econometrics, Elsevier, vol. 235(1), pages 133-165.
    4. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    5. Andreia Monteiro & Raquel Menezes & Maria Eduarda Silva, 2021. "Modelling informative time points: an evolutionary process approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 364-382, June.
    6. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    7. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    8. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Time reversal and last passage time of diffusions with applications to credit risk management," Finance and Stochastics, Springer, vol. 24(3), pages 795-825, July.
    9. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 9-24.
    10. Pfeifer Dietmar & Mändle Andreas & Ragulina Olena, 2017. "New copulas based on general partitions-of-unity and their applications to risk management (part II)," Dependence Modeling, De Gruyter, vol. 5(1), pages 246-255, October.
    11. Diba Daraei & Kristina Sendova, 2024. "Determining Safe Withdrawal Rates for Post-Retirement via a Ruin-Theory Approach," Risks, MDPI, vol. 12(4), pages 1-21, April.
    12. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    13. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    14. Nevrla, Matěj, 2020. "Systemic risk in European financial and energy sectors: Dynamic factor copula approach," Economic Systems, Elsevier, vol. 44(4).
    15. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
    16. Battulga Gankhuu, 2022. "Merton's Default Risk Model for Private Company," Papers 2208.01974, arXiv.org.
    17. Saminger-Platz Susanne & Kolesárová Anna & Šeliga Adam & Mesiar Radko & Klement Erich Peter, 2024. "On comprehensive families of copulas involving the three basic copulas and transformations thereof," Dependence Modeling, De Gruyter, vol. 12(1), pages 1-36.
    18. Stephan Schlüter & Fabian Menz & Milena Kojić & Petar Mitić & Aida Hanić, 2022. "A Novel Approach to Generate Hourly Photovoltaic Power Scenarios," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    19. Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020. "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, vol. 217(2), pages 335-355.
    20. Dietmar Pfeifer & Olena Ragulina, 2018. "Generating VaR Scenarios under Solvency II with Product Beta Distributions," Risks, MDPI, vol. 6(4), pages 1-15, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:32:y:2021:i:8:n:e2697. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.