Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/176051 
Year of Publication: 
2010
Series/Report no.: 
Texto para discussão No. 568
Publisher: 
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, Rio de Janeiro
Abstract: 
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 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in this paper.
Subjects: 
Financial econometrics
volatility forecasting
neural networks
nonlinear models
realized volatility
bagging.
Document Type: 
Working Paper

Files in This Item:
File
Size
200 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.