AN APPLICATION TO FORECAST VOLATILITY IN THE LIMA STOCK MARKET
A method is proposed to analyze data generated by a family of stochastic processes called autoregressive conditional heteroscedastic processes (ARCH), which are widely used to predict volatility of financial time series. An ARCE model is used to predict the volatility of the Atacocha mining company...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2004 |
| 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/9318 |
| Acceso en línea: | https://revistasinvestigacion.unmsm.edu.pe/index.php/matema/article/view/9318 |
| Access Level: | acceso abierto |
| Palabra clave: | Modelos ARCH series financieras volatilidad heterocedasticidad condicional retornos financieros riesgo. ARCH models financial time series volatility conditional heteroscedasticity financial returns risk |
| Sumario: | A method is proposed to analyze data generated by a family of stochastic processes called autoregressive conditional heteroscedastic processes (ARCH), which are widely used to predict volatility of financial time series. An ARCE model is used to predict the volatility of the Atacocha mining company stock price based on the data from 1992 to 2003. |
|---|