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D-vine copula based quantile regression

Citations

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Cited by:

  1. Yu, Lean & Zha, Rui & Stafylas, Dimitrios & He, Kaijian & Liu, Jia, 2020. "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models," International Review of Financial Analysis, Elsevier, vol. 68(C).
  2. Yu, Peining & Zhou, Luohui & Chen, Zejun & Li, Chujin, 2025. "Risk spillover changes among commodity futures, stock and ESG markets: A study based on multidimensional higher order moment perspective," Finance Research Letters, Elsevier, vol. 71(C).
  3. Kumar, Satish & Tiwari, Aviral Kumar & Chauhan, Yogesh & Ji, Qiang, 2019. "Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 273-284.
  4. Nasri, Bouchra R. & Rémillard, Bruno N. & Bouezmarni, Taoufik, 2019. "Semi-parametric copula-based models under non-stationarity," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 347-365.
  5. Julia Kielmann & Hans Manner & Aleksey Min, 2021. "Stock Market Returns and Oil Price Shocks: A CoVaR Analysis based on Dynamic Vine Copula Models," Graz Economics Papers 2021-01, University of Graz, Department of Economics.
  6. M. Mesfioui & T. Bouezmarni & M. Belalia, 2023. "Copula-based link functions in binary regression models," Statistical Papers, Springer, vol. 64(2), pages 557-585, April.
  7. Zhu, Kailun & Kurowicka, Dorota & Nane, Gabriela F., 2021. "Simplified R-vine based forward regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  8. Yoosoon Chang & Yong-gun Kim & Boreum Kwak & Joon Y. Park, 2024. "Using Density Forecast for Growth-at-Risk to Improve Mean Forecast of GDP Growth in Korea," CAEPR Working Papers 2024-005 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
  9. Sifat, Imtiaz & Ghafoor, Abdul & Ah Mand, Abdollah, 2021. "The COVID-19 pandemic and speculation in energy, precious metals, and agricultural futures," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
  10. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
  11. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
  12. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2020. "Modeling non-normal corporate bond yield spreads by copula," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
  13. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of energy prices on clean energy stock prices. A multivariate quantile dependence approach," Energy Economics, Elsevier, vol. 76(C), pages 136-152.
  14. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
  15. Luca Merlo & Lea Petrella & Valentina Raponi, 2021. "Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation," Papers 2106.06518, arXiv.org.
  16. Mojirsheibani, Majid, 2021. "On classification with nonignorable missing data," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
  17. El Hannoun Wafaa & Zoglat Abdelhak & Ezzahid ElHadj & El Adlouni Salah-Eddine, 2024. "D-vine Copula Quantile Regression for a Multidimensional Water Expenditures Analysis: Social and Regional Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(9), pages 3279-3295, July.
  18. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
  19. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
  20. Lubomira Gertler & Kristina Janovicova-Bognarova & Lukas Majer, 2020. "Explaining Corporate Credit Default Rates with Sector Level Detail," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(2), pages 96-120, August.
  21. Julia Kielmann & Hans Manner & Aleksey Min, 2022. "Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models," Empirical Economics, Springer, vol. 62(4), pages 1543-1574, April.
  22. Wen, Fenghua & Liu, Zhen & Dai, Zhifeng & He, Shaoyi & Liu, Wenhua, 2022. "Multi-scale risk contagion among international oil market, Chinese commodity market and Chinese stock market: A MODWT-Vine quantile regression approach," Energy Economics, Elsevier, vol. 109(C).
  23. Huawei Li & Guohe Huang & Yongping Li & Jie Sun & Pangpang Gao, 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
  24. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.
  25. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
  26. Nadja Klein & Michael Stanley Smith, 2021. "Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach," Biometrics, The International Biometric Society, vol. 77(3), pages 809-823, September.
  27. Wang, Qunwei & Liu, Mengmeng & Xiao, Ling & Dai, Xingyu & Li, Matthew C. & Wu, Fei, 2022. "Conditional sovereign CDS in market basket risk scenario: A dynamic vine-copula analysis," International Review of Financial Analysis, Elsevier, vol. 80(C).
  28. Jiang, Rong & Yu, Keming, 2020. "Single-index composite quantile regression for massive data," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
  29. Pan, Shenyi & Joe, Harry, 2022. "Predicting times to event based on vine copula models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
  30. Yunquan Song & Zitong Li & Minglu Fang, 2022. "Robust Variable Selection Based on Penalized Composite Quantile Regression for High-Dimensional Single-Index Models," Mathematics, MDPI, vol. 10(12), pages 1-17, June.
  31. Genest Christian & Scherer Matthias, 2019. "The world of vines: An interview with Claudia Czado," Dependence Modeling, De Gruyter, vol. 7(1), pages 169-180, January.
  32. Changsheng Liu & Hanying Liang & Yongmei Li, 2025. "Bayesian quantile regression for partially linear single-index model with longitudinal data," Statistical Papers, Springer, vol. 66(1), pages 1-51, January.
  33. Marius Lux & Wolfgang Karl Hardle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Papers 2009.06910, arXiv.org.
  34. Nam Gang Lee, 2020. "Vulnerable Growth: A Revisit," Working Papers 2020-22, Economic Research Institute, Bank of Korea.
  35. Marius Lux & Wolfgang Karl Härdle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Computational Statistics, Springer, vol. 35(3), pages 947-981, September.
  36. Czado, Claudia & Ivanov, Eugen & Okhrin, Yarema, 2019. "Modelling temporal dependence of realized variances with vines," Econometrics and Statistics, Elsevier, vol. 12(C), pages 198-216.
  37. Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
  38. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.
  39. Chen, Yaling & Zhu, Huiming & Liu, Yinpeng, 2025. "Measuring multi-scale risk contagion between crude oil, clean energy, and stock market: A MODWT-Vine-copula method," Research in International Business and Finance, Elsevier, vol. 75(C).
  40. Md Erfanul Hoque & Elif F. Acar & Mahmoud Torabi, 2023. "A time‐heterogeneous D‐vine copula model for unbalanced and unequally spaced longitudinal data," Biometrics, The International Biometric Society, vol. 79(2), pages 734-746, June.
  41. Kahkashan Afrin & Ashif S Iquebal & Mostafa Karimi & Allyson Souris & Se Yoon Lee & Bani K Mallick, 2020. "Directionally dependent multi-view clustering using copula model," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
  42. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  43. Jonathan Rathjens & Arthur Kolbe & Jürgen Hölzer & Katja Ickstadt & Nadja Klein, 2024. "Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 290-317, April.
  44. Czado, Claudia, 2025. "Vine copula based structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
  45. Rong Jiang & Mengxian Sun, 2022. "Single-index composite quantile regression for ultra-high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 443-460, June.
  46. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.
  47. Merlo, Luca & Petrella, Lea & Raponi, Valentina, 2021. "Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 133(C).
  48. Matthias Killiches & Claudia Czado, 2018. "A D‐vine copula‐based model for repeated measurements extending linear mixed models with homogeneous correlation structure," Biometrics, The International Biometric Society, vol. 74(3), pages 997-1005, September.
  49. Xue, Jianhao & Dai, Xingyu & Zhang, Dongna & Nghiem, Xuan-Hoa & Wang, Qunwei, 2024. "Tail risk spillover network among green bond, energy and agricultural markets under extreme weather scenarios," International Review of Economics & Finance, Elsevier, vol. 96(PC).
  50. Rémillard, Bruno & Nasri, Bouchra & Bouezmarni, Taoufik, 2017. "On copula-based conditional quantile estimators," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 14-20.
  51. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
  52. Okhrin, Yarema & Uddin, Gazi Salah & Yahya, Muhammad, 2023. "Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets," Energy Economics, Elsevier, vol. 125(C).
  53. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
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