Stochastic Variance Models in Discrete Time with Feedforward Neural Networks. Neural Computations 21
| dc.contributor.author | Andoh, C. | |
| dc.date.accessioned | 2015-07-24T16:07:53Z | |
| dc.date.accessioned | 2017-10-16T11:10:38Z | |
| dc.date.available | 2015-07-24T16:07:53Z | |
| dc.date.available | 2017-10-16T11:10:38Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | The study overcomes the estimation difficulty in stochastic variance models for discrete financial time series with feedforward neural networks. The volatility function is estimated semiparametrically. The model is used to estimate market risk, taking into account not only the time series of interest but extra information on the market. As an application, some stock prices series are studied and compared with the nonlinear ARX-ARCHX model. | en_US |
| dc.identifier.uri | http://197.255.68.203/handle/123456789/6679 | |
| dc.language.iso | en | en_US |
| dc.title | Stochastic Variance Models in Discrete Time with Feedforward Neural Networks. Neural Computations 21 | en_US |
| dc.type | Article | en_US |