Volatility Forecast with Long Memory: Evidence from Jordan Stock Market
Various volatility estimators and models have been proposed in the literature to measure volatility of asset returns. The particular emphasis of this paper is on assessing empirical performance of various long memory models (ARFIMA, FIGARCH models, and MF multi-fractal model which has recently been introduced as a new model)in comparison to short memory such as GARCH model, using time-series data from 1987-2004 of 90 stocks traded on the Amman Stock Exchange (ASE). Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of volatility models by one-day, five-day, ten-day, one-moth, two-moth, and three-month ahead. Two different measures are used to evaluate the forecast accuracy, RMSE and RMAE. Our results indicate that conditional volatility (ARFIMA ,FIGARCH and MF models) dominate over GARCH model. However, while FIGARCH and ARFIMA also have a number of cases with dramatic failure of their forecast, the MF model does not suffer from this shortcoming and its performance practically always improves upon the naÃ¯ve forecast provided by historical volatility.
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.
When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:106. 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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.