Non-linear versus non-gaussian volatility models in application to different financial markets
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Summary
This work used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications, and found that for all markets there was found no improvement in the forecast by non-linear models over linear ones.
- Type
- article
- Published
- 2003-01-01
- Cited by
- 1
- References
- 23
- Access
- Open access
- OpenAlex
- https://openalex.org/W42078474
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6271254
Keywords
Econometrics, Volatility (finance), Linear model, Gaussian, Financial market
References
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- Modelling market volatilities: the neural network perspective
- Neural networks for the analysis and forecasting of advertising and promotion impact
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- Regularities in the Variation of Skewness in Asset Returns
- On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
- Autoregressive Conditional Density Estimation
- Neural Networks and the Bias/Variance Dilemma
- Multilayer feedforward networks are universal approximators
- Neural Networks: Tricks of the Trade
- Multilayer feedforward networks are universal approximators
- Early Stopping-But When?
- Generalized autoregressive conditional heteroskedasticity
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