A diagnostic for selecting the threshold in extreme value analysis

Citations

Cited by:

1. Małgorzata Just & Krzysztof Echaust, 2021. "An Optimal Tail Selection in Risk Measurement," Risks, MDPI, vol. 9(4), pages 1-16, April.
2. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
3. Wang, Yulong & Xiao, Zhijie, 2022. "Estimation and inference about tail features with tail censored data," Journal of Econometrics, Elsevier, vol. 230(2), pages 363-387.
   • Yulong Wang & Zhijie Xiao, 2020. "Estimation and Inference about Tail Features with Tail Censored Data," Papers 2002.09982, arXiv.org.
   • Yulong Wang & Zhijie Xiao, 2020. "Estimation and Inference about Tail Features with Tail Censored Data," Boston College Working Papers in Economics 994, Boston College Department of Economics.
4. Stéphane Guerrier & Samuel Orso & Maria-Pia Victoria-Feser, 2018. "Parametric Inference for Index Functionals," Econometrics, MDPI, vol. 6(2), pages 1-11, April.
5. Yuya Sasaki & Yulong Wang, 2024. "Extreme Changes in Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 812-824, April.
   • Yuya Sasaki & Yulong Wang, 2022. "Extreme Changes in Changes," Papers 2211.14870, arXiv.org, revised May 2023.
6. Cuntz, A. & Haeusler, E. & Segers, J.J.J., 2003. "Edgeworth Expansions for the Distribution Function of the Hill Estimator," Other publications TiSEM 345501c7-c622-4b04-8d27-9, Tilburg University, School of Economics and Management.
7. Chan, Ngai-Hang & Lee, Thomas C.M. & Peng, Liang, 2010. "On nonparametric local inference for density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 509-515, February.
8. Wager, Stefan, 2014. "Subsampling extremes: From block maxima to smooth tail estimation," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 335-353.
9. Silvia Sarpietro & Yuya Sasaki & Yulong Wang, 2022. "Non-Existent Moments of Earnings Growth," Papers 2203.08014, arXiv.org, revised Feb 2024.
10. Goegebeur, Yuri & Guillou, Armelle & Pedersen, Tine & Qin, Jing, 2022. "Extreme-value based estimation of the conditional tail moment with application to reinsurance rating," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 102-122.
11. Laurens Haan & Cécile Mercadier & Chen Zhou, 2016. "Adapting extreme value statistics to financial time series: dealing with bias and serial dependence," Finance and Stochastics, Springer, vol. 20(2), pages 321-354, April.
12. Tsourti, Zoi & Panaretos, John, 2003. "Extreme Value Index Estimators and Smoothing Alternatives: A Critical Review," MPRA Paper 6390, University Library of Munich, Germany.
13. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
14. Koning, A.J. & Peng, L., 2005. "Goodness-of-fit tests for a heavy tailed distribution," Econometric Institute Research Papers EI 2005-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
15. Haeusler, E. & Segers, J., 2005. "Assessing Confidence Intervals for the Tail Index by Edgeworth Expansions for the Hill Estimator," Other publications TiSEM e635c476-8fa8-4f16-8760-2, Tilburg University, School of Economics and Management.
16. Yuri Goegebeur & Tertius de Wet, 2012. "Estimation of the third-order parameter in extreme value statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 330-354, June.
17. Łuczak, Aleksandra & Just, Małgorzata, 2021. "Sustainable development of territorial units: MCDM approach with optimal tail selection," Ecological Modelling, Elsevier, vol. 457(C).
18. Herrera, Rodrigo & Schipp, Bernhard, 2014. "Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 218-238.
19. Wang, Yinzhi & Hobæk Haff, Ingrid & Huseby, Arne, 2020. "Modelling extreme claims via composite models and threshold selection methods," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 257-268.
20. Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
21. Haeusler, E. & Segers, J., 2005. "Assessing Confidence Intervals for the Tail Index by Edgeworth Expansions for the Hill Estimator," Discussion Paper 2005-129, Tilburg University, Center for Economic Research.
22. Sasaki, Yuya & Wang, Yulong, 2024. "On uniform confidence intervals for the tail index and the extreme quantile," Journal of Econometrics, Elsevier, vol. 244(1).
23. Himadri Ghosh & Prajneshu, 2011. "Statistical learning theory for fitting multimodal distribution to rainfall data: an application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2533-2545, January.
24. Juan Gonzalez & Daniela Rodriguez & Mariela Sued, 2013. "Threshold selection for extremes under a semiparametric model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 481-500, November.
25. Chen, Song X. & Delaigle, Aurore & Hall, Peter, 2010. "Nonparametric estimation for a class of Lévy processes," Journal of Econometrics, Elsevier, vol. 157(2), pages 257-271, August.
26. Cuntz, A. & Haeusler, E. & Segers, J.J.J., 2003. "Edgeworth Expansions for the Distribution Function of the Hill Estimator," Discussion Paper 2003-8, Tilburg University, Center for Economic Research.