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 |
| Summary: | 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. |
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