Modelling volatility with mixture density networks
Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH an...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/40416 |
| _version_ | 1848755865344016384 |
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| author | Mostafa, Fahed Dillon, Tharam S. |
| author2 | X.T. Hu |
| author_facet | X.T. Hu Mostafa, Fahed Dillon, Tharam S. |
| author_sort | Mostafa, Fahed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models. |
| first_indexed | 2025-11-14T09:03:06Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40416 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:06Z |
| publishDate | 2008 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-404162022-12-07T06:50:49Z Modelling volatility with mixture density networks Mostafa, Fahed Dillon, Tharam S. X.T. Hu Q. Liu Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models. 2008 Conference Paper http://hdl.handle.net/20.500.11937/40416 10.1109/GRC.2008.4664673 Institute of Electrical and Electronics Engineers (IEEE) fulltext |
| spellingShingle | Mostafa, Fahed Dillon, Tharam S. Modelling volatility with mixture density networks |
| title | Modelling volatility with mixture density networks |
| title_full | Modelling volatility with mixture density networks |
| title_fullStr | Modelling volatility with mixture density networks |
| title_full_unstemmed | Modelling volatility with mixture density networks |
| title_short | Modelling volatility with mixture density networks |
| title_sort | modelling volatility with mixture density networks |
| url | http://hdl.handle.net/20.500.11937/40416 |