Modelling stock returns with AR-GARCH processes
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time s...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3744 |
| Acceso en línea: | https://hdl.handle.net/2099/3744 |
| Access Level: | acceso abierto |
| Palabra clave: | Inference Mathematical economics Inferència Processos estocàstics Matemàtica financera Classificació AMS::62 Statistics::62M Inference from stochastic processes Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics |
| Sumario: | Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions. |
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