A new Forecasting combination system for predicting Volatility
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| Format: | Artículo científico |
| Language: | en |
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Universidad Nacional de Colombia
2013
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| _version_ | 1876479899144814592 |
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| author | Johanna M. Orozco |
| author_facet | Johanna M. Orozco |
| contents | A new Forecasting combination system for predicting Volatility Johanna M. Orozco Juan D. Velásquez Administración y Contabilidad Volatility forecast combinations Forecast volatility models Forecast combination models have been broadly studied and often used to improve forecast accuracy. this article presents a new non-linear composite model to forecast the volatility of asset returns. our model is composed of a set of GaRCH models fitted to a time series dataset using different loss functions, with the aim of capturing different features of volatility dynamics. individual forecasts are combined by using either the simple arithmetical average method or an artificial neural network. the proposed model is used to forecast the monthly excess returns of s&P500 time series, finding that this new approach is able to forecast volatility with more accuracy than each individual GaRCH model considered. 2013 artículo científico 0121-5051 https://www.redalyc.org/articulo.oa?id=81828692002 en http://www.redalyc.org/revista.oa?id=818 INNOVAR. Revista de Ciencias Administrativas y Sociales application/pdf Universidad Nacional de Colombia INNOVAR. Revista de Ciencias Administrativas y Sociales (Colombia) Num.50 Vol.23 |
| format | Artículo científico |
| id | redalyc_81828692002 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | A new Forecasting combination system for predicting Volatility Johanna M. Orozco Administración y Contabilidad Volatility forecast combinations Forecast volatility models A new Forecasting combination system for predicting Volatility Johanna M. Orozco Juan D. Velásquez Administración y Contabilidad Volatility forecast combinations Forecast volatility models Forecast combination models have been broadly studied and often used to improve forecast accuracy. this article presents a new non-linear composite model to forecast the volatility of asset returns. our model is composed of a set of GaRCH models fitted to a time series dataset using different loss functions, with the aim of capturing different features of volatility dynamics. individual forecasts are combined by using either the simple arithmetical average method or an artificial neural network. the proposed model is used to forecast the monthly excess returns of s&P500 time series, finding that this new approach is able to forecast volatility with more accuracy than each individual GaRCH model considered. 2013 artículo científico 0121-5051 https://www.redalyc.org/articulo.oa?id=81828692002 en http://www.redalyc.org/revista.oa?id=818 INNOVAR. Revista de Ciencias Administrativas y Sociales application/pdf Universidad Nacional de Colombia INNOVAR. Revista de Ciencias Administrativas y Sociales (Colombia) Num.50 Vol.23 |
| title | A new Forecasting combination system for predicting Volatility |
| topic | Administración y Contabilidad Volatility forecast combinations Forecast volatility models |
| url | https://www.redalyc.org/articulo.oa?id=81828692002 |