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Journal articles on the topic 'Beta-t-EGARCH'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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.

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2

Blazsek, 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.

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3

Muller, 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.

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The Beta-Skew-t-EGARCH model was recently proposed in literature to model the volatility of financial returns. The inferences over the parameters of the model are based on maximum likelihood method. These estimators have good asymptotic properties, however in finite sample sizes their performance can be poor. With the purpose of evaluating the finite sample performance of point estimators and of the likelihood ratio test proposed to the presence of two components of volatility, we present a Monte Carlo simulation study. Numerical results indicate that the maximum likelihood estimators of some parameters of the model are considerably biased in sample sizes smaller than 3000. The evaluation results of the proposed two-component test, in terms of size and power of the test, showed its practical usefulness in sample sizes greater than 3000. At the end of the work we present an application in a real data of the proposed two-component test and the model Beta-Skew-t-EGARCH.

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4

Yao, 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.

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No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is “qualified” in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China’s stock market.

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5

Sucarrat, 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.

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6

Liao, 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.

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7

Blazsek, 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.

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8

Blazsek, 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.

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9

Blazsek, 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.

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Abstract We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.

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10

Blazsek, 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.

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11

Blazsek 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.

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Recently, the use of dynamic conditional score (DCS) time series models are suggested in the body of literature on time series econometrics. DCS models are robust to extreme observations because those observations are discounted by the score function that updates each dynamic equation. Examples of the DCS models are the quasi-autoregressive (QAR) model and the Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, which measure the dynamics of location and scale, respectively, of the dependent variable. Both QAR and Beta-t-EGARCH discount extreme observations according to a smooth form of trimming. Classical dynamic location and scale models (for example, the AR and the GARCH models) are sensitive to extreme observations. Thus, the AR and the GARCH modelsmay provide imprecise estimates of location and scale dynamics. In the application presented in this paper, we use data from the Shanghai Stock Exchange A-Share Index and the Shenzhen Stock Exchange A-Share Index for the period of 5th January 1998 to 29th December 2017. For the corresponding stock index return time series, a relatively high number of extreme values are observed during the sample period. We find that the statistical performance of QAR plus Beta-t-EGARCH is superior to that of AR plus t-GARCH, due to the robustness of QAR plus Beta-t-EGARCH to extreme unexpected returns.

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12

Blazsek, 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.

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Abstract In this paper, statistical and volatility forecasting performances of the non-path-dependent score-driven multi-regime Markov-switching (MS) exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models are explored. Three contributions to the existing literature are provided. First, we use all relevant score-driven distributions from the literature - namely, the Student’s t-distribution, general error distribution (GED), skewed generalized t-distribution (Skew-Gen-t), exponential generalized beta distribution of the second kind (EGB2), and normal-inverse Gaussian (NIG) distribution. We then introduce the score-driven Meixner (MXN) distribution-based EGARCH model to the literature on score-driven models. Second, proving the sufficient conditions of the asymptotic properties of the maximum likelihood (ML) estimator for non-path-dependent score-driven MS-EGARCH models is an unsolved problem. We provide a partial solution to that problem by proving necessary conditions for the asymptotic theory of the ML estimator. Third, to the best of our knowledge, this work includes the largest number of international stock indices from the G20 countries in the literature, covering the period of 2000–2022. We provide a discussion on the major events which caused common or non-common switching to the high-volatility regime for the G20 countries. The statistical performance and volatility forecasting results support the adoption of score-driven MS-EGARCH for the G20 countries.

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13

Ayala, 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.

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Abstract We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor’s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.

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14

Chen, 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.

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15

Geng, 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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