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Dynamic quantile models

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Cited by:

  1. Charle Augusto Llondono, 2011. "Regresión del cuantil aplicada al modelo de redes neuronales artificiales. Una aproximación de la estructura CAVIAR para el mercado de valores colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 29(64), pages 62-109, July.
  2. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
  3. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
  4. Christian Gourieroux & Wei Liu, 2009. "Control and Out‐of‐Sample Validation of Dependent Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 683-707, September.
  5. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
  6. Ayoub Ammy-Driss & Matthieu Garcin, 2021. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Working Papers hal-02903655, HAL.
  7. Li, Jingyu & Yao, Yanzhen & Li, Jianping & Zhu, Xiaoqian, 2019. "Network-based estimation of systematic and idiosyncratic contagion: The case of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 40(C), pages 1-1.
  8. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
  9. Ayoub Ammy-Driss & Matthieu Garcin, 2020. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Papers 2007.10727, arXiv.org, revised Nov 2021.
  10. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
  11. Benjamin Hamidi & Bertrand Maillet & Jean-Luc Prigent, 2009. "A Risk Management Approach for Portfolio Insurance Strategies," Documents de travail du Centre d'Economie de la Sorbonne 09034, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  12. Bonaccolto, Giovanni & Caporin, Massimiliano & Maillet, Bertrand B., 2022. "Dynamic large financial networks via conditional expected shortfalls," European Journal of Operational Research, Elsevier, vol. 298(1), pages 322-336.
  13. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
  14. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
  15. Zongwu Cai & Xiyuan Liu, 2020. "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202017, University of Kansas, Department of Economics, revised Oct 2020.
  16. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
  17. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
  18. Darolles, Serge & Gourieroux, Christian & Jasiak, Joann, 2009. "L-performance with an application to hedge funds," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 671-685, September.
  19. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
  20. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Post-Print halshs-01467736, HAL.
  21. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
  22. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
  23. Chen, Cathy W.S. & Gerlach, Richard & Wei, D.C.M., 2009. "Bayesian causal effects in quantiles: Accounting for heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1993-2007, April.
  24. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
  25. Bernard, Carole & Czado, Claudia, 2015. "Conditional quantiles and tail dependence," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 104-126.
  26. Alex Maynard & Katsumi Shimotsu & Nina Kuriyama, 2023. "Inference in Predictive Quantile Regressions," Papers 2306.00296, arXiv.org.
  27. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk Abstract: One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as t," Caepr Working Papers 2007-005, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  28. CORONEO, Laura & VEREDAS, David, 2006. "Intradaily seasonality of returns distribution. A quantile regression approach and intradaily VaR estimation," LIDAM Discussion Papers CORE 2006077, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  29. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723.
  30. Yuta Kurose & Yasuhiro Omori, 2012. "Bayesian Analysis of Time-Varying Quantiles Using a Smoothing Spline," CIRJE F-Series CIRJE-F-845, CIRJE, Faculty of Economics, University of Tokyo.
  31. Luis Melo Velandia & Luis Fernando Melo Velandia, 2019. "Regresión cuantílica dinámica para la medición del valor en riesgo: Una aplicación a datos colombianos," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 38(76), pages 23-50, January.
  32. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.
  33. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
  34. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
  35. Nieto, María Rosa & Ruiz Ortega, Esther, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
  36. Christian Francq & Jean-Michel Zakoian, 2014. "Multi-level Conditional VaR Estimation in Dynamic Models," Working Papers 2014-01, Center for Research in Economics and Statistics.
  37. Ammy-Driss, Ayoub & Garcin, Matthieu, 2023. "Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  38. Cheng Peng & Stanislav Uryasev, 2023. "Factor Model of Mixtures," Papers 2301.13843, arXiv.org, revised Mar 2023.
  39. Georges Tsafack & James Cataldo, 2021. "Backtesting and estimation error: value-at-risk overviolation rate," Empirical Economics, Springer, vol. 61(3), pages 1351-1396, September.
  40. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
  41. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Documents de travail du Centre d'Economie de la Sorbonne 17007, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  42. Zhijie Xiao & Roger Koenker, 2009. "Conditional Quantile Estimation for GARCH Models," Boston College Working Papers in Economics 725, Boston College Department of Economics.
  43. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01467736, HAL.
  44. Harvey, Andrew & Oryshchenko, Vitaliy, 2012. "Kernel density estimation for time series data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 3-14.
  45. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
  46. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
  47. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
  48. Semeyutin, Artur & O’Neill, Robert, 2019. "A brief survey on the choice of parameters for: “Kernel density estimation for time series data”," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
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