Modeling volatility’s long-range persistence and asymmetry effect of Bradesco bank stock prices using garchmodels
The main aim of this paper is to evaluate and model both leverage effect and persistent volatility of Bradesco Bank stock shares (BBDC3). The leverage effect was measured through the generalized autoregressive conditional heteroscedasticity (GARCH) model and some of its extensions, such as EGARCH, N...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Brasil |
| Institución: | Universidade Federal de Lavras (UFLA) |
| Repositorio: | Repositório Institucional da UFLA |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufla.br:1/50662 |
| Acceso en línea: | https://repositorio.ufla.br/handle/1/50662 |
| Access Level: | acceso abierto |
| Palabra clave: | Time series Forecasting Financial data GARCH extensions Séries temporais Dados financeiros |
| Sumario: | The main aim of this paper is to evaluate and model both leverage effect and persistent volatility of Bradesco Bank stock shares (BBDC3). The leverage effect was measured through the generalized autoregressive conditional heteroscedasticity (GARCH) model and some of its extensions, such as EGARCH, NGARCH, APGARCH, ALLGARCH, IGARCH, CGARCH and FIGARCH. The orical basis: These are mainly asymmetrical extensions that were chosen since they can overcome the possible limitation that negative returns might have a bigger impact in volatility than positive ones in this type of data. Results indicated that the most common used goodness-of-fit information criteria might not be a sufficient measurement for comparing different kinds of GARCH models for forecasting and, for this reason, it might be necessary to consider other important volatility characteristics, such as long-range persistence. In BBDC3 returns, despite CGARCH model slightly overestimated the central tendency, it still significantly outperformed the asymmetric extensions of GARCH model. |
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