Using High Frequency Data to Calculate, Model and Forecast Realized Volatility
The objective of this paper is to calculate, model, and forecast realized volatility using high-frequency stock-market index data. The approach differs from existing ones in several ways. First, it is shown that the decay of the serial dependence of high-frequency returns on the sampling frequency is consistent with an ARMA process under temporal aggregation. This is important in modelling high-frequency returns and chosing the optimal sampling frequency when calculating realized volatility. Second, as a result of several test statistics for long memory in realized volatility, it is found that the realized volatility series can be modelled as an ARFIMA process. The ARFIMA's forecasting performance is assessed in a simulation study, and, although it outperforms representative GARCH models, it does so with greater complexity and data intensiveness that may not be worthwhile relative to GARCH's simplicity and flexibility.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||01 Apr 2001|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecf1:75. 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: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.