Choosing the best volatility models: the model confidence set approach

Peter Hansen, Asger Lunde () and James Nason

No 2003-28, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

Abstract: This paper applies the model confidence sets (MCS) procedure to a set of volatility models. A MSC is analogous to a confidence interval of parameter in the sense that the former contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is based on fifty-five volatility models, and the MCS includes about a third of these when evaluated by mean square error, whereas the MCS contains only a VGARCH model when mean absolute deviation criterion is used. We conduct a simulation study that shows the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, this bound delivers inferior performance.

Date: 2003
New Economics Papers: this item is included in nep-ets, nep-fin and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (121)

Downloads: (external link)
https://www.frbatlanta.org/-/media/documents/resea ... s/wp/2003/wp0328.pdf (application/pdf)

Related works:
Journal Article: Choosing the Best Volatility Models: The Model Confidence Set Approach* (2003)
Working Paper: Choosing the Best Volatility Models:The Model Confidence Set Approach (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedawp:2003-28

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta Contact information at EDIRC.
Bibliographic data for series maintained by Rob Sarwark ().