Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Engle, Robert F. | - |
dc.contributor.author | Patton, Andrew J. | - |
dc.date.accessioned | 2008-05-29T12:02:30Z | - |
dc.date.available | 2008-05-29T12:02:30Z | - |
dc.date.issued | 2001-01-29 | - |
dc.identifier.uri | http://hdl.handle.net/2451/26881 | - |
dc.description.abstract | A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylised facts about volatility that should be incorporated in a model; pronounced persistence and meanreversion, asymmetry such that the sign of an innovation also affects volatility and the possibility of exogenous or pre-determined variables influencing volatility. We use data on the Dow Jones Industrial index to illustrate these stylised facts, and the ability of GARCH-type models to capture these features. We conclude with some challenges for future research in this area. | en |
dc.language.iso | en_US | en |
dc.relation.ispartofseries | S-DRP-01-03 | en |
dc.subject | volatility modelling | en |
dc.subject | ARCH | en |
dc.subject | GARCH | en |
dc.subject | volatility forecasting | en |
dc.title | WHAT GOOD IS A VOLATILITY MODEL? | en |
dc.type | Working Paper | en |
Appears in Collections: | Derivatives Research |
Files in This Item:
File | Description | Size | Format | |
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S-DRP-01-03.pdf | 353 kB | Adobe PDF | View/Open |
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