Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
The Markov-switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood (ML) and Generalized Method of Moments (GMM) and volatility forecasting can be performed via Bayesian updating (ML) or best linear forecasts (GMM). Monte Carlo simulations show that using GMM plus linear forecasts leads to minor losses in efficiency compared to optimal Bayesian forecasts based on ML estimates. The forecasting capability of the MSM-t model is evaluated empirically in a comprehensive panel forecasting analysis with three different cross-sections of assets at the country level (all-share equity indices, bond indices and real estate security indices). Empirical forecasts of the MSM-t model are compared to those obtained from its Gaussian counterparts and other volatility models of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family. In terms of mean absolute errors (mean squared errors), the MSM-t (Gaussian MSM) dominates all other models at most forecasting horizons for the various asset classes considered. Furthermore, forecast combinations obtained from the MSM and (Fractionally Integrated) GARCH models provide an improvement upon forecasts from single models.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2676-2692. 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: (Shamier, Wendy)
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.