IDEAS home Printed from https://ideas.repec.org/p/aiz/louvad/2013017.html
   My bibliography  Save this paper

Markov Tail Chains

Author

Listed:
  • janssen, Anja
  • Segers, Johan

Abstract

No abstract is available for this item.

Suggested Citation

  • janssen, Anja & Segers, Johan, 2013. "Markov Tail Chains," LIDAM Discussion Papers ISBA 2013017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2013017
    as

    Download full text from publisher

    File URL: https://cdn.uclouvain.be/public/Exports%20reddot/stat/documents/DP2013_17_segers_markov.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 2002. "Regular variation of GARCH processes," Stochastic Processes and their Applications, Elsevier, vol. 99(1), pages 95-115, May.
    2. Meinguet, Thomas & Segers, Johan, 2010. "Regularly varying time series in Banach spaces," LIDAM Discussion Papers ISBA 2010002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Collamore, Jeffrey F. & Vidyashankar, Anand N., 2013. "Tail estimates for stochastic fixed point equations via nonlinear renewal theory," Stochastic Processes and their Applications, Elsevier, vol. 123(9), pages 3378-3429.
    4. Basrak, Bojan & Segers, Johan, 2009. "Regularly varying multivariate time series," Stochastic Processes and their Applications, Elsevier, vol. 119(4), pages 1055-1080, April.
    5. Paola Bortot & Stuart Coles, 2003. "Extremes of Markov chains with tail switching potential," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 851-867, November.
    6. de Haan, Laurens & Resnick, Sidney I. & Rootzén, Holger & de Vries, Casper G., 1989. "Extremal behaviour of solutions to a stochastic difference equation with applications to arch processes," Stochastic Processes and their Applications, Elsevier, vol. 32(2), pages 213-224, August.
    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. Rafal Kulik & Philippe Soulier, 2013. "Heavy tailed time series with extremal independence," Papers 1307.1501, arXiv.org, revised Oct 2014.

    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. Davis, Richard A. & Mikosch, Thomas & Zhao, Yuwei, 2013. "Measures of serial extremal dependence and their estimation," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2575-2602.
    2. Basrak, Bojan & Segers, Johan, 2009. "Regularly varying multivariate time series," Stochastic Processes and their Applications, Elsevier, vol. 119(4), pages 1055-1080, April.
    3. Kokoszka, Piotr & Kulik, Rafał, 2023. "Principal component analysis of infinite variance functional data," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    4. Drees, Holger & Janßen, Anja & Neblung, Sebastian, 2021. "Cluster based inference for extremes of time series," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 1-33.
    5. Phornchanok Cumperayot & Casper G. de Vries, 2006. "Large Swings in Currencies driven by Fundamentals," Tinbergen Institute Discussion Papers 06-086/2, Tinbergen Institute.
    6. Janßen, Anja, 2019. "Spectral tail processes and max-stable approximations of multivariate regularly varying time series," Stochastic Processes and their Applications, Elsevier, vol. 129(6), pages 1993-2009.
    7. de Valk, Cees Fouad & Segers, Johan, 2018. "Stability and tail limits of transport-based quantile contours," LIDAM Discussion Papers ISBA 2018031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Liu, Ji-Chun, 2006. "On the tail behaviors of Box-Cox transformed threshold GARCH(1,1) process," Statistics & Probability Letters, Elsevier, vol. 76(13), pages 1323-1330, July.
    9. Bücher, Axel & Jennessen, Tobias, 2022. "Statistical analysis for stationary time series at extreme levels: New estimators for the limiting cluster size distribution," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 75-106.
    10. Hay, Diana & Rastegar, Reza & Roitershtein, Alexander, 2011. "Multivariate linear recursions with Markov-dependent coefficients," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 521-527, March.
    11. Bojan Basrak & Danijel Krizmanić, 2015. "A Multivariate Functional Limit Theorem in Weak $$M_{1}$$ M 1 Topology," Journal of Theoretical Probability, Springer, vol. 28(1), pages 119-136, March.
    12. Krizmanić, Danijel, 2017. "Weak convergence of multivariate partial maxima processes," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 1-11.
    13. Rasmus Pedersen & Olivier Wintenberger, 2017. "On the tail behavior of a class of multivariate conditionally heteroskedastic processes," Papers 1701.05091, arXiv.org, revised Dec 2017.
    14. Klüppelberg, Claudia & Pergamenchtchikov, Serguei, 2007. "Extremal behaviour of models with multivariate random recurrence representation," Stochastic Processes and their Applications, Elsevier, vol. 117(4), pages 432-456, April.
    15. Durieu, Olivier & Wang, Yizao, 2022. "Phase transition for extremes of a stochastic model with long-range dependence and multiplicative noise," Stochastic Processes and their Applications, Elsevier, vol. 143(C), pages 55-88.
    16. F. Laurini & J. A. Tawn, 2006. "The extremal index for GARCH(1,1) processes with t-distributed innovations," Economics Department Working Papers 2006-SE01, Department of Economics, Parma University (Italy).
    17. Rasmus Pedersen & Olivier Wintenberger, 2017. "On the tail behavior of a class of multivariate conditionally heteroskedastic processes," Working Papers hal-01436267, HAL.
    18. Zhang, Rong-Mao & Sin, Chor-yiu (CY) & Ling, Shiqing, 2015. "On functional limits of short- and long-memory linear processes with GARCH(1,1) noises," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 482-512.
    19. Pedersen, Rasmus Søndergaard, 2016. "Targeting Estimation Of Ccc-Garch Models With Infinite Fourth Moments," Econometric Theory, Cambridge University Press, vol. 32(2), pages 498-531, April.
    20. Mika Meitz & Pentti Saikkonen, 2008. "Stability of nonlinear AR‐GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 453-475, May.

    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:aiz:louvad:2013017. 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: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.html .

    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.