IDEAS home Printed from https://ideas.repec.org/r/bla/jorssb/v65y2003i2p545-556.html
   My bibliography  Save this item

Inference for clusters of extreme values

Citations

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


Cited by:

  1. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
  2. J. Sebastião & A. Martins & H. Ferreira & L. Pereira, 2013. "Estimating the upcrossings index," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 549-579, November.
  3. 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.
  4. A. P. Martins & J. R. Sebastião, 2019. "Methods for estimating the upcrossings index: improvements and comparison," Statistical Papers, Springer, vol. 60(4), pages 1317-1347, August.
  5. 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.
  6. Vera Melinda Gálfi & Tamás Bódai & Valerio Lucarini, 2017. "Convergence of Extreme Value Statistics in a Two-Layer Quasi-Geostrophic Atmospheric Model," Complexity, Hindawi, vol. 2017, pages 1-20, September.
  7. Zhao, Xin & Scarrott, Carl John & Oxley, Les & Reale, Marco, 2011. "GARCH dependence in extreme value models with Bayesian inference," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1430-1440.
  8. Fries, Sébastien, 2018. "Conditional moments of noncausal alpha-stable processes and the prediction of bubble crash odds," MPRA Paper 97353, University Library of Munich, Germany, revised Nov 2019.
  9. Natalia Markovich, 2024. "Extremal properties of evolving networks: local dependence and heavy tails," Annals of Operations Research, Springer, vol. 339(3), pages 1839-1870, August.
  10. 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.
  11. Marta Ferreira, 2024. "Extremal index: estimation and resampling," Computational Statistics, Springer, vol. 39(5), pages 2703-2720, July.
  12. Jose Olmo, 2015. "A New Family of Consistent and Asymptotically-Normal Estimators for the Extremal Index," Econometrics, MDPI, vol. 3(3), pages 1-21, August.
  13. Caston Sigauke & Rosinah Mukhodobwane & Wilbert Chagwiza & Winston Garira, 2022. "Asymptotic Dependence Modelling of the BRICS Stock Markets," IJFS, MDPI, vol. 10(3), pages 1-32, July.
  14. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
  15. Beirlant, J. & Schoutens, W. & Segers, J.J.J., 2004. "Mandelbrot's Extremism," Other publications TiSEM 34027632-2f60-44af-a2ed-e, Tilburg University, School of Economics and Management.
  16. Amir AghaKouchak & Nasrin Nasrollahi, 2010. "Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1229-1249, April.
  17. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
  18. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
  19. 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).
  20. Fries, Sébastien & Zakoian, Jean-Michel, 2019. "Mixed Causal-Noncausal Ar Processes And The Modelling Of Explosive Bubbles," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1234-1270, December.
  21. Juan Ignacio Pe~na & Rosa Rodriguez & Silvia Mayoral, 2022. "Tail Risk of Electricity Futures," Papers 2202.01732, arXiv.org.
  22. Natalia Markovich & Marijus Vaičiulis, 2023. "Extreme Value Statistics for Evolving Random Networks," Mathematics, MDPI, vol. 11(9), pages 1-35, May.
  23. Tian, Shuairu & Hamori, Shigeyuki, 2015. "Modeling interest rate volatility: A Realized GARCH approach," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 158-171.
  24. Sigauke, Caston & Bere, Alphonce, 2017. "Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand," Energy, Elsevier, vol. 119(C), pages 152-166.
  25. Hees, Katharina & Nayak, Smarak & Straka, Peter, 2021. "Statistical inference for inter-arrival times of extreme events in bursty time series," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  26. Anna Kiriliouk & Chen Zhou, 2024. "Tail Risk Analysis for Financial Time Series," Papers 2409.18643, arXiv.org.
  27. 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.
  28. 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.
  29. Segers, J.J.J., 2006. "Rare Events, Temporal Dependence and the Extremal Index," Discussion Paper 2006-7, Tilburg University, Center for Economic Research.
  30. Ross Towe & Jonathan Tawn & Emma Eastoe & Rob Lamb, 2020. "Modelling the Clustering of Extreme Events for Short-Term Risk Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 32-53, March.
  31. Sara Ali Alokley & Mansour Saleh Albarrak, 2020. "Clustering of Extremes in Financial Returns: A Study of Developed and Emerging Markets," JRFM, MDPI, vol. 13(7), pages 1-11, July.
  32. Tadele Akeba Diriba & Legesse Kassa Debusho, 2020. "Modelling dependency effect to extreme value distributions with application to extreme wind speed at Port Elizabeth, South Africa: a frequentist and Bayesian approaches," Computational Statistics, Springer, vol. 35(3), pages 1449-1479, September.
  33. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2016. "US stock returns: are there seasons of excesses?," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1453-1464, September.
  34. Joerg Osterrieder & Julian Lorenz, 2017. "A Statistical Risk Assessment Of Bitcoin And Its Extreme Tail Behavior," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-19, March.
  35. Xin Zhao & Carl John Scarrott & Marco Reale & Les Oxley, 2009. "Bayesian Extreme Value Mixture Modelling for Estimating VaR," Working Papers in Economics 09/15, University of Canterbury, Department of Economics and Finance.
  36. Omey, Edward & Mallor, Fermin & Nualart, Eulalia, 2009. "An introduction to statistical modelling of extreme values. Application to calculate extreme wind speeds," Working Papers 2009/36, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
  37. Segers, J.J.J., 2006. "Rare Events, Temporal Dependence and the Extremal Index," Other publications TiSEM 04952d0f-2b24-44ad-bf07-f, Tilburg University, School of Economics and Management.
  38. Alexandre Mornet & Thomas Opitz & Michel Luzi & Stéphane Loisel, 2016. "Wind Storm Risk Management," Working Papers hal-01299692, HAL.
  39. Paola Bortot & Carlo Gaetan, 2016. "Latent Process Modelling of Threshold Exceedances in Hourly Rainfall Series," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 531-547, September.
  40. Beirlant, J. & Schoutens, W. & Segers, J.J.J., 2004. "Mandelbrot's Extremism," Discussion Paper 2004-125, Tilburg University, Center for Economic Research.
  41. John G. Galbraith & Serguei Zernov, 2006. "Extreme Dependence In The Nasdaq And S&P Composite Indexes," Departmental Working Papers 2006-14, McGill University, Department of Economics.
  42. 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.
  43. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
  44. John Galbraith & Serguei Zernov, 2009. "Extreme dependence in the NASDAQ and S&P 500 composite indexes," Applied Financial Economics, Taylor & Francis Journals, vol. 19(13), pages 1019-1028.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.