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A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics

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Cited by:

  1. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, January.
  2. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
  3. Blasques, F. & Francq, Christian & Laurent, Sébastien, 2023. "Quasi score-driven models," Journal of Econometrics, Elsevier, vol. 234(1), pages 251-275.
  4. Francq, Christian & Zakoian, Jean-Michel, 2023. "Local Asymptotic Normality Of General Conditionally Heteroskedastic And Score-Driven Time-Series Models," Econometric Theory, Cambridge University Press, vol. 39(5), pages 1067-1092, October.
  5. Rui Li & Saralees Nadarajah, 2020. "A review of Student’s t distribution and its generalizations," Empirical Economics, Springer, vol. 58(3), pages 1461-1490, March.
  6. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
  7. James Mitchell & Martin Weale, 2023. "Censored density forecasts: Production and evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 714-734, August.
  8. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
  9. Palczewski, Andrzej & Palczewski, Jan, 2019. "Black–Litterman model for continuous distributions," European Journal of Operational Research, Elsevier, vol. 273(2), pages 708-720.
  10. Beddock, Arthur, 2021. "Asset pricing with heterogeneous agents and non-normal return distributions," Other publications TiSEM eeaf2925-4cc0-4fe1-8008-6, Tilburg University, School of Economics and Management.
  11. M. C. Jones, 2015. "On Families of Distributions with Shape Parameters," International Statistical Review, International Statistical Institute, vol. 83(2), pages 175-192, August.
  12. Emmanuel Afuecheta & Idika E. Okorie & Saralees Nadarajah & Geraldine E. Nzeribe, 2024. "Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 271-304, January.
  13. Gao, Chun-Ting & Zhou, Xiao-Hua, 2016. "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution," Economic Modelling, Elsevier, vol. 53(C), pages 216-223.
  14. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.
  15. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
  16. Nadarajah, Saralees & Chan, Stephen & Afuecheta, Emmanuel, 2013. "On the characteristic function for asymmetric Student t distributions," Economics Letters, Elsevier, vol. 121(2), pages 271-274.
  17. Yasutomo Murasawa, 2013. "Measuring Inflation Expectations Using Interval-Coded Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(4), pages 602-623, August.
  18. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
  19. Saswat Patra & Malay Bhattacharyya, 2020. "How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure," Risks, MDPI, vol. 8(3), pages 1-17, July.
  20. Dongming Zhu & John W. Galbraith, 2009. "Forecasting Expected Shortfall with a Generalized Asymmetric Student-t Distribution," CIRANO Working Papers 2009s-24, CIRANO.
  21. Dejan Živkov & Slavica Manić & Jelena Kovačević & Željana Trbović, 2022. "Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(1), pages 67-93, January.
  22. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
  23. Moosup Kim & Sangyeol Lee, 2019. "Test for tail index constancy of GARCH innovations based on conditional volatility," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 947-981, August.
  24. Martin Weale & Paul Labonne, 2022. "Nowcasting in the presence of large measurement errors and revisions," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-05, Economic Statistics Centre of Excellence (ESCoE).
  25. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
  26. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2013. "Log Student’s t -distribution-based option sensitivities: Greeks for the Gosset formulae," Quantitative Finance, Taylor & Francis Journals, vol. 13(8), pages 1289-1302, July.
  27. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
  28. Mark F. J. Steel & Francisco J. Rubio, 2015. "Discussion," International Statistical Review, International Statistical Institute, vol. 83(2), pages 218-222, August.
  29. Rubio, Francisco Javier & Steel, Mark F. J., 2014. "Bayesian modelling of skewness and kurtosis with two-piece scale and shape transformations," MPRA Paper 57102, University Library of Munich, Germany.
  30. Massimiliano Giacalone & Demetrio Panarello, 2022. "A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments," Mathematics, MDPI, vol. 10(5), pages 1-21, February.
  31. José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
  32. Baker, Rose, 2017. "Creating new distributions by blunting cusps," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 55-63.
  33. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
  34. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
  35. Dejan Zivkov & Marina Gajic-Glamoclija & Jelena Kovacevic & Sanja Loncar, 2020. "Inflation Uncertainty and Output Growth - Evidence from the Asia-Pacific Countries Based on the Multiscale Bayesian Quantile Inference," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(5), pages 461-486, November.
  36. Jiang, Chun-Fu & Peng, Hong-Yi & Yang, Yu-Kuan, 2016. "Tail variance of portfolio under generalized Laplace distribution," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 187-203.
  37. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2010. "Student's t-Distribution Based Option Sensitivities: Greeks for the Gosset Formulae," Papers 1003.1344, arXiv.org, revised Jul 2010.
  38. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
  39. Francisco J. Rubio Alvarez, 2020. "Letter to the Editor: ‘On Quantile‐based Asymmetric Family of Distributions: Properties and Inference’," International Statistical Review, International Statistical Institute, vol. 88(3), pages 793-796, December.
  40. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
  41. Ibrahim Ergen, 2015. "Two-step methods in VaR prediction and the importance of fat tails," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1013-1030, June.
  42. Robina Iqbal & Ghulam Sorwar & Rose Baker & Taufiq Choudhry, 2020. "Multiday expected shortfall under generalized t distributions: evidence from global stock market," Review of Quantitative Finance and Accounting, Springer, vol. 55(3), pages 803-825, October.
  43. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
  44. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
  45. Colletaz, Gilbert & Hurlin, Christophe & Pérignon, Christophe, 2013. "The Risk Map: A new tool for validating risk models," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3843-3854.
  46. Victor Korolev & Alexander Zeifman, 2023. "Mixture Representations for Generalized Burr, Snedecor–Fisher and Generalized Student Distributions with Related Results," Mathematics, MDPI, vol. 11(18), pages 1-25, September.
  47. Victor Korolev, 2023. "Analytic and Asymptotic Properties of the Generalized Student and Generalized Lomax Distributions," Mathematics, MDPI, vol. 11(13), pages 1-27, June.
  48. Yuancheng Si & Saralees Nadarajah, 2023. "A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting," Annals of Data Science, Springer, vol. 10(1), pages 73-88, February.
  49. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
  50. Owusu Junior, Peterson & Alagidede, Imhotep, 2020. "Risks in emerging markets equities: Time-varying versus spatial risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
  51. J. Miguel Marin & Genaro Sucarrat, 2015. "Financial density selection," The European Journal of Finance, Taylor & Francis Journals, vol. 21(13-14), pages 1195-1213, November.
  52. Stephen Thiele, 2020. "Modeling the conditional distribution of financial returns with asymmetric tails," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 46-60, January.
  53. Mazur Błażej & Pipień Mateusz, 2018. "Time-varying asymmetry and tail thickness in long series of daily financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-21, December.
  54. Patra, Saswat, 2021. "Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions," Energy Economics, Elsevier, vol. 101(C).
  55. Ruijie Guan & Xu Zhao & Weihu Cheng & Yaohua Rong, 2021. "A New Generalized t Distribution Based on a Distribution Construction Method," Mathematics, MDPI, vol. 9(19), pages 1-36, September.
  56. Trottier, Denis-Alexandre & Ardia, David, 2016. "Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models," Finance Research Letters, Elsevier, vol. 18(C), pages 311-316.
  57. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
  58. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
  59. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
  60. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
  61. Samet Gunay & Audil Rashid Khaki, 2018. "Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models," JRFM, MDPI, vol. 11(2), pages 1-19, June.
  62. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
  63. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
  64. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
  65. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
  66. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
  67. Acereda, Beatriz & Leon, Angel & Mora, Juan, 2020. "Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting," Finance Research Letters, Elsevier, vol. 33(C).
  68. Mitchell, James & Weale, Martin, 2019. "Forecasting with Unknown Unknowns: Censoring and Fat Tails on the Bank of England's Monetary Policy Committee," EMF Research Papers 27, Economic Modelling and Forecasting Group.
  69. Beatriz Vaz de Melo Mendes & André Fluminense Carneiro, 2020. "A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020," JRFM, MDPI, vol. 13(9), pages 1-21, August.
  70. Yang, Lu & Hamori, Shigeyuki, 2021. "The role of the carbon market in relation to the cryptocurrency market: Only diversification or more?," International Review of Financial Analysis, Elsevier, vol. 77(C).
  71. Tianyang Wang & James Dyer & Warren Hahn, 2015. "A copula-based approach for generating lattices," Review of Derivatives Research, Springer, vol. 18(3), pages 263-289, October.
  72. Irène Gijbels & Rezaul Karim & Anneleen Verhasselt, 2020. "Response to the Letter to the Editor on ‘On Quantile‐based Asymmetric Family of Distributions: Properties and Inference’," International Statistical Review, International Statistical Institute, vol. 88(3), pages 797-801, December.
  73. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
  74. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
  75. Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
  76. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "A note on "Modelling exchange rate returns: which flexible distribution to use?"," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1777-1785, November.
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