Using approximate results for validating value-at-risk

Hong, J; Knight, J; Satchell, SE; Scherer, B

Using approximate results for validating value-at-risk

Hong, J Knight, J Satchell, SE Scherer, B

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Publisher:: Incisive Financial Publishing Limited
Publication Type:: Journal Article
Citation:: Journal of Risk Model Validation, 2010, 4 (3), pp. 69 - 81
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Hong, J	en_US
dc.contributor.author	Knight, J	en_US
dc.contributor.author	Satchell, SE	en_US
dc.contributor.author	Scherer, B	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Journal of Risk Model Validation, 2010, 4 (3), pp. 69 - 81	en_US
dc.identifier.issn	1753-9579	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28641
dc.description.abstract	The failure of value-at-risk (VaR) methods in recent times has reawakened interest in its statistical properties. It is clear that we need quite general methods to assess the ef?cacy of a VaR forecast. One such procedure that can be useful is the construction of con?dence intervals. Failure of the forecasts to lie within their con?dence intervals in an ex-post sense is an indication of model failure. Under general assumptions, we derive asymptotic results for VaR which can be used to construct con?dence intervals. Our results indicate why VaR may be more accurately measured if the data is positively skewed and less accurately measured if the data is leptokurtotic. Previous approaches have been based on the assumption of normality and so the approximate approach defended by Jorion (1996, 2001) and Chappell and Dowd (1999) may need re?nement. We also carry out some exact and Monte Carlo analysis to ?nd the degree of approximation involved.	en_US
dc.publisher	Incisive Financial Publishing Limited	en_US
dc.relation.ispartof	Journal of Risk Model Validation	en_US
dc.title	Using approximate results for validating value-at-risk	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	4	en_US
utslib.for	150201 Finance	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	3	en_US
pubs.volume	4	en_US

Abstract:

The failure of value-at-risk (VaR) methods in recent times has reawakened interest in its statistical properties. It is clear that we need quite general methods to assess the ef?cacy of a VaR forecast. One such procedure that can be useful is the construction of con?dence intervals. Failure of the forecasts to lie within their con?dence intervals in an ex-post sense is an indication of model failure. Under general assumptions, we derive asymptotic results for VaR which can be used to construct con?dence intervals. Our results indicate why VaR may be more accurately measured if the data is positively skewed and less accurately measured if the data is leptokurtotic. Previous approaches have been based on the assumption of normality and so the approximate approach defended by Jorion (1996, 2001) and Chappell and Dowd (1999) may need re?nement. We also carry out some exact and Monte Carlo analysis to ?nd the degree of approximation involved.

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http://hdl.handle.net/10453/28641