Forecasting Realized Volatility with Linear and Nonlinear Univariate Models
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 intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.
|Date of creation:||01 May 2010|
|Date of revision:|
|Contact details of provider:|| Postal: Private Bag 4800, Christchurch, New Zealand|
Phone: 64 3 369 3123 (Administrator)
Fax: 64 3 364 2635
Web page: http://www.econ.canterbury.ac.nz
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:cbt:econwp:10/28. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Albert Yee)
If references are entirely missing, you can add them using this form.