Juan-Juan Cai

Personal Details

First Name:	Juan-Juan
Middle Name:
Last Name:	Cai
Suffix:
RePEc Short-ID:	pca1603
	[This author has chosen not to make the email address public]
	https://research.vu.nl/en/persons/juan-juan-cai

Affiliation

School of Business and Economics
Vrije Universiteit Amsterdam

Amsterdam, Netherlands
http://sbe.vu.nl/
RePEc:edi:fewvunl (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. de Valk, Cees Fouad & Cai, Juan-Juan, 2018. "A high quantile estimator based on the log-generalized Weibull tail limit," LIDAM Reprints ISBA 2018030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
   • de Valk, Cees & Cai, Juan-Juan, 2018. "A high quantile estimator based on the log-generalized Weibull tail limit," Econometrics and Statistics, Elsevier, vol. 6(C), pages 107-128.

   Cited by:

   1. Matheus Henrique Junqueira Saldanha & Adriano Kamimura Suzuki, 2023. "On dealing with the unknown population minimum in parametric inference," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 509-535, September.
   2. Albert, Clément & Dutfoy, Anne & Gardes, Laurent & Girard, Stéphane, 2020. "An extreme quantile estimator for the log-generalized Weibull-tail model," Econometrics and Statistics, Elsevier, vol. 13(C), pages 137-174.

2. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Discussion Paper 2012-080, Tilburg University, Center for Economic Research.
   • Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
   • Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Other publications TiSEM e96e039f-cb6b-4cd5-805b-5, Tilburg University, School of Economics and Management.

   Cited by:

   1. Chen, Yu & Gao, Yu & Shu, Lei & Zhu, Xiaonan, 2023. "Network effects on risk co-movements: A network quantile autoregression-based analysis," Finance Research Letters, Elsevier, vol. 56(C).
   2. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2017. "Extreme M-quantiles as risk measures: From L1 to Lp optimization," TSE Working Papers 17-841, Toulouse School of Economics (TSE).
   3. S. Tavolaro & F. Visnovsky, 2014. "What is the information content of the SRISK measure as a supervisory tool?," Débats économiques et financiers 10, Banque de France.
   4. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2018. "Tail expectile process and risk assessment," TSE Working Papers 18-944, Toulouse School of Economics (TSE).
   5. Li, Jinzhu, 2022. "Asymptotic results on marginal expected shortfalls for dependent risks," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 146-168.
   6. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
   7. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2016. "Multivariate tail conditional expectation for elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 216-223.
   8. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
   9. Mao, Tiantian & Stupfler, Gilles & Yang, Fan, 2023. "Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 173-192.
   10. Popescu, Alexandra & Turcu, Camelia, 2017. "Sovereign debt and systemic risk in the eurozone," Economic Modelling, Elsevier, vol. 67(C), pages 275-284.
       • Alexandra Popescu & Camélia Turcu, 2017. "Sovereign debt and systemic risk in the eurozone," Post-Print hal-02521449, HAL.
   11. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
   12. Das, Bikramjit & Fasen-Hartmann, Vicky, 2018. "Risk contagion under regular variation and asymptotic tail independence," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 194-215.
   13. Goegebeur, Yuri & Guillou, Armelle & Ho, Nguyen Khanh Le & Qin, Jing, 2023. "Nonparametric estimation of conditional marginal excess moments," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
   14. Laurent Gardes & Stéphane Girard & Gilles Stupfler, 2020. "Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp‐optimization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 922-949, September.
   15. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.
       • Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2018. "ExpectHill estimation, extreme risk and heavy tails," TSE Working Papers 18-953, Toulouse School of Economics (TSE).
   16. Qin, Xiao & Zhou, Chunyang, 2019. "Financial structure and determinants of systemic risk contribution," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
   17. Das Bikramjit & Fasen-Hartmann Vicky, 2019. "Conditional excess risk measures and multivariate regular variation," Statistics & Risk Modeling, De Gruyter, vol. 36(1-4), pages 1-23, December.
   18. Hua, Lei & Joe, Harry, 2014. "Strength of tail dependence based on conditional tail expectation," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 143-159.
   19. Cristina Zeldea, 2020. "Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions," Administrative Sciences, MDPI, vol. 10(3), pages 1-14, August.
   20. Takashi Isogai, 2014. "Benchmarking of Unconditional VaR and ES Calculation Methods: A Comparative Simulation Analysis with Truncated Stable Distribution," Bank of Japan Working Paper Series 14-E-1, Bank of Japan.
   21. Ji, Liuyan & Tan, Ken Seng & Yang, Fan, 2021. "Tail dependence and heavy tailedness in extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 282-293.
   22. Qin, Xiao & Zhou, Chen, 2021. "Systemic risk allocation using the asymptotic marginal expected shortfall," Journal of Banking & Finance, Elsevier, vol. 126(C).
   23. Hou, Yanxi & Wang, Xing, 2019. "Nonparametric inference for distortion risk measures on tail regions," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 92-110.
   24. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
       • Rodrigo S. Targino & Gareth W. Peters & Pavel V. Shevchenko, 2014. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Papers 1410.1101, arXiv.org, revised Feb 2015.
   25. Gribkova, N.V. & Su, J. & Zitikis, R., 2022. "Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 199-222.
   26. Andrea Teruzzi, 2023. "Tail Risk and Systemic Risk Estimation of Cryptocurrencies: an Expectiles and Marginal Expected Shortfall based approach," Papers 2311.17239, arXiv.org.
   27. Sun, Hongfang & Chen, Yu & Hu, Taizhong, 2022. "Statistical inference for tail-based cumulative residual entropy," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 66-95.
   28. Ling, Chengxiu, 2019. "Asymptotics of multivariate conditional risk measures for Gaussian risks," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 205-215.
   29. Beck, Nicholas & Di Bernardino, Elena & Mailhot, Mélina, 2021. "Semi-parametric estimation of multivariate extreme expectiles," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
   30. Asimit, Alexandru V. & Li, Jinzhu, 2016. "Extremes for coherent risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 332-341.
   31. Goegebeur, Yuri & Guillou, Armelle & Ho, Nguyen Khanh Le & Qin, Jing, 2023. "A Weissman-type estimator of the conditional marginal expected shortfall," Econometrics and Statistics, Elsevier, vol. 27(C), pages 173-196.
   32. Liu, Ruicheng & Pun, Chi Seng, 2022. "Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach," Journal of Banking & Finance, Elsevier, vol. 136(C).
   33. Beatriz de la Flor & Javier Ojea-Ferreiro & Eva Ferreira, 2022. "The Hedging Cost of Forgetting the Exchange Rate," Documentos de Trabajo del ICAE 2022-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
   34. Areski Cousin & Elena Di Bernardino, 2013. "On Multivariate Extensions of Conditional-Tail-Expectation," Working Papers hal-00877386, HAL.
   35. Bikramjit Das & Vicky Fasen, 2016. "Risk contagion under regular variation and asymptotic tail independence," Papers 1603.09406, arXiv.org, revised Apr 2017.

3. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M., 2011. "Estimation of extreme risk regions under multivariate regular variation," Other publications TiSEM b7a72a8d-f9bc-4129-ae9b-a, Tilburg University, School of Economics and Management.

   Cited by:

   1. Berthet, Philippe & Einmahl, John, 2020. "Cube Root Weak Convergence of Empirical Estimators of a Density Level Set," Discussion Paper 2020-015, Tilburg University, Center for Economic Research.
      • Berthet, Philippe & Einmahl, John, 2020. "Cube Root Weak Convergence of Empirical Estimators of a Density Level Set," Other publications TiSEM 69103be2-c944-4ca1-b9e1-2, Tilburg University, School of Economics and Management.
   2. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Other publications TiSEM dd3c4dd0-7181-40f3-af44-f, Tilburg University, School of Economics and Management.
      • Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
      • John H. J. Einmahl & Fan Yang & Chen Zhou, 2021. "Testing the Multivariate Regular Variation Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 907-919, October.
   3. 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.
   4. Einmahl, J.H.J. & Li, Jun & Liu, Regina, 2015. "Bridging Centrality and Extremity : Refining Empirical Data Depth using Extreme Value Statistics," Other publications TiSEM bcd9783a-e07e-4da2-bc47-b, Tilburg University, School of Economics and Management.
      • Einmahl, J.H.J. & Li, Jun & Liu, Regina, 2015. "Bridging Centrality and Extremity : Refining Empirical Data Depth using Extreme Value Statistics," Discussion Paper 2015-020, Tilburg University, Center for Economic Research.
   5. Yi He & John H. J. Einmahl, 2017. "Estimation of extreme depth-based quantile regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 449-461, March.
      • He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Other publications TiSEM d6529c8a-8865-4c03-a064-a, Tilburg University, School of Economics and Management.
      • He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Discussion Paper 2014-035, Tilburg University, Center for Economic Research.
   6. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.
   7. Yves Dominicy & Pauliina Ilmonen & David Veredas, 2017. "Multivariate Hill Estimators," International Statistical Review, International Statistical Institute, vol. 85(1), pages 108-142, April.
   8. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.

Articles

1. Juan‐Juan Cai & Eni Musta, 2020. "Estimation of the marginal expected shortfall under asymptotic independence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(1), pages 56-83, March.

   Cited by:

   1. Li, Jinzhu, 2022. "Asymptotic results on marginal expected shortfalls for dependent risks," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 146-168.
   2. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
   3. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
   4. Gribkova, N.V. & Su, J. & Zitikis, R., 2022. "Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 199-222.
   5. Sun, Hongfang & Chen, Yu & Hu, Taizhong, 2022. "Statistical inference for tail-based cumulative residual entropy," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 66-95.

2. de Valk, Cees & Cai, Juan-Juan, 2018. "A high quantile estimator based on the log-generalized Weibull tail limit," Econometrics and Statistics, Elsevier, vol. 6(C), pages 107-128.
   • de Valk, Cees Fouad & Cai, Juan-Juan, 2018. "A high quantile estimator based on the log-generalized Weibull tail limit," LIDAM Reprints ISBA 2018030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
   See citations under working paper version above.
3. Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
   • Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Discussion Paper 2012-080, Tilburg University, Center for Economic Research.
   • Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Other publications TiSEM e96e039f-cb6b-4cd5-805b-5, Tilburg University, School of Economics and Management.
   See citations under working paper version above.

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