IDEAS home Printed from https://ideas.repec.org/r/eee/jmvana/v138y2015icp53-73.html
   My bibliography  Save this item

Structured factor copula models: Theory, inference and computation

Citations

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


Cited by:

  1. Wang, Pan & Lu, Zhenzhou & Zhang, Kaichao & Xiao, Sinan & Yue, Zhufeng, 2018. "Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 437-450.
  2. Nguyen, Hoang & Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2024. "Structured factor copulas for modeling the systemic risk of European and United States banks," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  3. Marbac, Matthieu & Sedki, Mohammed, 2017. "A family of block-wise one-factor distributions for modeling high-dimensional binary data," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 130-145.
  4. Zhang, Xi & Li, Jian, 2018. "Credit and market risks measurement in carbon financing for Chinese banks," Energy Economics, Elsevier, vol. 76(C), pages 549-557.
  5. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
  6. Damien Ackerer & Thibault Vatter, 2016. "Dependent Defaults and Losses with Factor Copula Models," Papers 1610.03050, arXiv.org, revised Jan 2018.
  7. Minoru Tachibana, 2020. "Flight-to-quality in the stock–bond return relation: a regime-switching copula approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(4), pages 429-470, December.
  8. Ackerer Damien & Vatter Thibault, 2017. "Dependent defaults and losses with factor copula models," Dependence Modeling, De Gruyter, vol. 5(1), pages 375-399, December.
  9. Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
  10. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
  11. Nguyen, Hoang & Ausín, M. Concepción & Galeano, Pedro, 2020. "Variational inference for high dimensional structured factor copulas," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
  12. Zheng Wei & Seongyong Kim & Boseung Choi & Daeyoung Kim, 2019. "Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 365-387, January.
  13. Li, Xiaoting & Joe, Harry, 2024. "Multivariate directional tail-weighted dependence measures," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
  14. Jonas Moss & Steffen Grønneberg, 2023. "Partial Identification of Latent Correlations with Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 241-252, March.
  15. Tong, Chen & Hansen, Peter Reinhard, 2023. "Characterizing correlation matrices that admit a clustered factor representation," Economics Letters, Elsevier, vol. 233(C).
  16. Sayed H. Kadhem & Aristidis K. Nikoloulopoulos, 2023. "Bi-factor and Second-Order Copula Models for Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 132-157, March.
  17. Krupskii, Pavel & Genton, Marc G., 2019. "A copula model for non-Gaussian multivariate spatial data," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 264-277.
  18. Mohamed Belalia & Jean-François Quessy, 2024. "Generalized simulated method-of-moments estimators for multivariate copulas," Statistical Papers, Springer, vol. 65(8), pages 4811-4841, October.
  19. Perreault, Samuel & Duchesne, Thierry & Nešlehová, Johanna G., 2019. "Detection of block-exchangeable structure in large-scale correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 400-422.
  20. Joe, Harry & Sang, Peijun, 2016. "Multivariate models for dependent clusters of variables with conditional independence given aggregation variables," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 114-132.
  21. Marius Hofert & Johanna F. Ziegel, 2021. "Matrix-Tilted Archimedean Copulas," Risks, MDPI, vol. 9(4), pages 1-24, April.
  22. Mazo, Gildas & Uyttendaele, Nathan, 2016. "Building conditionally dependent parametric one-factor copulas," LIDAM Discussion Papers ISBA 2016004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  23. Oh, Rosy & Jeong, Himchan & Ahn, Jae Youn & Valdez, Emiliano A., 2021. "A multi-year microlevel collective risk model," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 309-328.
  24. Benedikt Schamberger & Lutz F. Gruber & Claudia Czado, 2017. "Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting," Econometrics, MDPI, vol. 5(2), pages 1-23, May.
  25. Krupskii, Pavel & Joe, Harry, 2020. "Flexible copula models with dynamic dependence and application to financial data," Econometrics and Statistics, Elsevier, vol. 16(C), pages 148-167.
  26. Verhoijsen Alex & Krupskiy Pavel, 2022. "Fast inference methods for high-dimensional factor copulas," Dependence Modeling, De Gruyter, vol. 10(1), pages 270-289, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.