Journal articles on the topic 'Beta-t-EGARCH'
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Blazsek, Szabolcs, Helmuth Chavez, and Carlos Mendez. "Model stability and forecast performance of Beta-t-EGARCH." Applied Economics Letters 23, no. 17 (February 29, 2016): 1219–23. http://dx.doi.org/10.1080/13504851.2016.1145343.
Full textBlazsek, Szabolcs, and Marco Villatoro. "Is Beta-t-EGARCH(1,1) superior to GARCH(1,1)?" Applied Economics 47, no. 17 (January 19, 2015): 1764–74. http://dx.doi.org/10.1080/00036846.2014.1000536.
Full textMuller, Fernanda Maria, and Fábio Mariano Bayer. "Avaliações numéricas das inferências no modelo Beta-Skew-t-EGARCH." Brazilian Review of Finance 13, no. 1 (November 5, 2015): 40. http://dx.doi.org/10.12660/rbfin.v13n1.2015.41464.
Full textYao, Yanyun, Xiutian Zheng, and Huimin Wang. "Predictability of China’s Stock Market Returns Based on Combination of Distribution Forecasting Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 4 (July 20, 2020): 477–87. http://dx.doi.org/10.20965/jaciii.2020.p0477.
Full textSucarrat, Genaro. "betategarch: Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models." R Journal 5, no. 2 (2013): 137. http://dx.doi.org/10.32614/rj-2013-034.
Full textLiao, Ruofan, Woraphon Yamaka, and Songsak Sriboonchitta. "Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH." IEEE Access 8 (2020): 207563–74. http://dx.doi.org/10.1109/access.2020.3038564.
Full textBlazsek, Szabolcs, and Vicente Mendoza. "QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500." Applied Economics 48, no. 12 (September 30, 2015): 1119–29. http://dx.doi.org/10.1080/00036846.2015.1093086.
Full textBlazsek, Szabolcs, Daniela Carrizo, Ricardo Eskildsen, and Humberto Gonzalez. "Forecasting rate of return after extreme values when using AR- t -GARCH and QAR-Beta- t -EGARCH." Finance Research Letters 24 (March 2018): 193–98. http://dx.doi.org/10.1016/j.frl.2017.09.006.
Full textBlazsek, Szabolcs, Alvaro Escribano, and Adrian Licht. "Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility." Studies in Nonlinear Dynamics & Econometrics, March 7, 2022. http://dx.doi.org/10.1515/snde-2021-0083.
Full textBlazsek, Szabolcs, and Han-Chiang Ho. "Markov regime-switching Beta-t-EGARCH." Applied Economics, February 20, 2017, 1–13. http://dx.doi.org/10.1080/00036846.2017.1293794.
Full textBlazsek 1, Szabolcs, and Adrian Licht 1. "Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market." Financial Statistical Journal 1, no. 2 (August 27, 2018). http://dx.doi.org/10.24294/fsj.v1i2.699.
Full textBlazsek, Szabolcs, and Michel Ferreira Cardia Haddad. "Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution." Studies in Nonlinear Dynamics & Econometrics, July 21, 2022. http://dx.doi.org/10.1515/snde-2021-0101.
Full textAyala, Astrid, Szabolcs Blazsek, and Alvaro Escribano. "Anticipating extreme losses using score-driven shape filters." Studies in Nonlinear Dynamics & Econometrics, October 10, 2022. http://dx.doi.org/10.1515/snde-2021-0102.
Full textChen, Jiusheng. "Asymmetric risk spillovers between oil and the Chinese stock market: a Beta-skew-t-EGARCH-EVT-copula approach." Journal of Risk, 2023. http://dx.doi.org/10.21314/jor.2022.047.
Full textGeng, Wenjing, Hongyang Zhao, and Xiaoxiao Zhou. "Research on extreme risk measurement in the international carbon emission futures market, based on a two-component Beta-Skew-t-EGARCH-POT model." Applied Economics, October 24, 2022, 1–10. http://dx.doi.org/10.1080/00036846.2022.2128176.
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