Application ofgarchmethodologyto the closing priceon thelimasto ck exchange
The article presents a methodology that uses the time series, for forecasting indices closing prices, which made the stock market centers. The behavior response to a current generated on the expectation value of change in the preceding moment, ie an expected value conditioned by the variance of prev...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Perú |
| Institución: | Universidad Nacional Mayor de San Marcos |
| Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
| Idioma: | español |
| OAI Identifier: | oai:revistasinvestigacion.unmsm.edu.pe:article/6377 |
| Acceso en línea: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/6377 |
| Access Level: | acceso abierto |
| Palabra clave: | Forecasting time series heteroscedasticity autoregressive models GARCH methodology. Predicción series de tiempo heterocedasticidad modelos autorregresivos metodología GARCH. |
| Sumario: | The article presents a methodology that uses the time series, for forecasting indices closing prices, which made the stock market centers. The behavior response to a current generated on the expectation value of change in the preceding moment, ie an expected value conditioned by the variance of previous period. The GARCH model is the key part of the investigation. It presents a clear and detailed each of the activities undertaken to quantify market risk. ARIMA methodology is applied to predict the yields of the series, which generally have a variance is not constant over time, ie the existence of heteroscedasticity present and should be used generalized autoregressive conditional heteroskedasticity, for the company under study. |
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