説明
In this paper, we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high-frequency intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.
収録刊行物
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- SSRN Electronic Journal
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SSRN Electronic Journal 2009-01-01
Elsevier BV
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キーワード
- Financial econometrics, volatility forecasting, neural networks, nonlinear models, realized volatility, bagging.
- ddc:330
- volatility forecasting
- neural networks
- Volatilität
- Nichtlineare Regression
- realized volatility
- Algorithmus
- bagging.
- Financial econometrics
- Prognoseverfahren
- nonlinear models
- Neuronale Netze
詳細情報 詳細情報について
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- CRID
- 1870865117535089920
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- ISSN
- 15565068
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- HANDLE
- 10419/176051
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- データソース種別
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- OpenAIRE