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Test of independence and randomness based on the empirical copula process

Citations

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Cited by:

  1. Talbi, Marwa & de Peretti, Christian & Belkacem, Lotfi, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Resources Policy, Elsevier, vol. 66(C).
  2. Xia Li, 2024. "RETRACTED ARTICLE: Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10200-10226, September.
  3. Jone Ascorbebeitia & Eva Ferreira & Susan Orbe, 2022. "Correction to: Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1167-1167, December.
  4. M. Mehdi Bateni & Mario L. V. Martina & ·Marcello Arosio, 2022. "Multivariate return period for different types of flooding in city of Monza, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 811-823, October.
  5. Guillou, Armelle & Padoan, Simone A. & Rizzelli, Stefano, 2018. "Inference for asymptotically independent samples of extremes," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 114-135.
  6. García, Jesús E. & González-López, V.A., 2014. "Independence tests for continuous random variables based on the longest increasing subsequence," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 126-146.
  7. Li, Dong & Ling, Shiqing, 2012. "On the least squares estimation of multiple-regime threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 167(1), pages 240-253.
  8. Hudecová, Šárka & Šiman, Miroslav, 2024. "Stochastic hyperplane-based ranks and their use in multivariate portmanteau tests," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
  9. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2018. "Testing for Serial Independence: Beyond the Portmanteau Approach," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 219-238, July.
  10. Sancetta, A. & Nikanrova, A., 2005. "Forecasting and Prequential Validation for Time Varying Meta-Elliptical Distributions with a Study of Commodity Futures Prices," Cambridge Working Papers in Economics 0516, Faculty of Economics, University of Cambridge.
  11. Henry Penikas, 2016. "Copula-Based Univariate Time Series Structural Shift Identification Test," Papers 1609.05056, arXiv.org.
  12. Danna Zhang & Mengyu Xu, 2024. "A High-Dimensional Cramér–von Mises Test," Mathematics, MDPI, vol. 12(22), pages 1-23, November.
  13. Ahmet Akca & Ethem Çanakoğlu, 2021. "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(3), pages 463-504, September.
  14. Boris David & Gilles Zumbach, 2022. "Multivariate backtests and copulas for risk evaluation," Papers 2206.03896, arXiv.org, revised Nov 2023.
  15. Hatem Ben-Ameur & Rim Chérif & Bruno Rémillard, 2016. "American-style options in jump-diffusion models: estimation and evaluation," Quantitative Finance, Taylor & Francis Journals, vol. 16(8), pages 1313-1324, August.
  16. Milan Cisty & Anna Becova & Lubomir Celar, 2016. "Analysis of Irrigation Needs Using an Approach Based on a Bivariate Copula Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 167-182, January.
  17. Henry Penikas, 2010. "Copula-Models in Foreign Exchange Risk-Management of a Bank," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 62-87.
  18. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2017. "A diagram to detect serial dependencies: an application to transport time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 581-594, March.
  19. Pandit, Mahesh & Paudel, Krishna P. & Mishra, Ashok K., 2013. "Do Agricultural Subsidies Affect the Labor Allocation Decision? Comparing Parametric and Semiparametric Methods," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(01), pages 1-18, April.
  20. Ghislain Verdier, 2024. "Goodness-of-fit procedure for gamma processes," Computational Statistics, Springer, vol. 39(5), pages 2623-2650, July.
  21. Florencia Leonardi & Matías Lopez‐Rosenfeld & Daniela Rodriguez & Magno T. F. Severino & Mariela Sued, 2021. "Independent block identification in multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 19-33, January.
  22. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
  23. repec:jss:jstsof:34:i09 is not listed on IDEAS
  24. Fernández-Durán Juan José & Gregorio-Domínguez María Mercedes, 2023. "Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-17, January.
  25. Sergei Aivazian & Mikhail Afanasiev & Victoria Rudenko, 2014. "Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 3-18.
  26. Hao, Jing & He, Feng, 2018. "Univariate dependence among sectors in Chinese stock market and systemic risk implication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 355-364.
  27. Bianchi, Pascal & Elgui, Kevin & Portier, François, 2023. "Conditional independence testing via weighted partial copulas," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
  28. Matthieu Garcin & Maxime L. D. Nicolas, 2024. "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Statistical Papers, Springer, vol. 65(8), pages 4875-4913, October.
  29. Gery Geenens & Arthur Charpentier & Davy Paindaveine, 2014. "Probit Transformation for Nonparametric Kernel Estimation of the Copula Density," Working Papers ECARES ECARES 2014-23, ULB -- Universite Libre de Bruxelles.
  30. Jin, Ze & Matteson, David S., 2018. "Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 304-322.
  31. Mangold, Benedikt, 2017. "A multivariate rank test of independence based on a multiparametric polynomial copula," FAU Discussion Papers in Economics 10/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2017.
  32. Henry Penikas, 2011. "Copula-Based Price Risk Hedging Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 3-21.
  33. Matthieu Garcin & Maxime L. D. Nicolas, 2021. "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Papers 2111.11128, arXiv.org, revised Jul 2023.
  34. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
  35. Kalemkerian, Juan & Fernández, Diego, 2020. "An independence test based on recurrence rates," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  36. Jiří Dvořák & Tomáš Mrkvička, 2022. "Graphical tests of independence for general distributions," Computational Statistics, Springer, vol. 37(2), pages 671-699, April.
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