Jump-robust testing of volatility functions in continuous time models

Chen, Q; Xu, W; Gong, Y

Jump-robust testing of volatility functions in continuous time models

Chen, Q Xu, W Gong, Y

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Canadian Journal of Statistics, 2022, 50, (3), pp. 1071-1095
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Chen, Q
dc.contributor.author	Xu, W
dc.contributor.author	Gong, Y
dc.date.accessioned	2023-06-22T21:41:09Z
dc.date.available	2023-06-22T21:41:09Z
dc.date.issued	2022-09-01
dc.identifier.citation	Canadian Journal of Statistics, 2022, 50, (3), pp. 1071-1095
dc.identifier.issn	0319-5724
dc.identifier.issn	1708-945X
dc.identifier.uri	http://hdl.handle.net/10453/170833
dc.description.abstract	In this article we develop new tests for the parametric volatility function of jump diffusion processes Our tests are of the Kolmogorov Smirnov and Cramer von Mises types and are based on marked empirical processes coupled with a threshold bipower approach The tests impose no restrictions on the functional form of the drift function and are robust to jumps Monte Carlo simulations show that the tests have satisfactory size and good power under processes with or without jumps Application to a real dataset shows that our jump robust volatility tests give more reasonable results than the existing non robust volatility tests 2021 Statistical Society of Canada
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Canadian Journal of Statistics
dc.relation.isbasedon	10.1002/cjs.11640
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	Jump-robust testing of volatility functions in continuous time models
dc.type	Journal Article
utslib.citation.volume	50
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-06-22T21:41:08Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	50
utslib.citation.issue	3

Abstract:

In this article we develop new tests for the parametric volatility function of jump diffusion processes Our tests are of the Kolmogorov Smirnov and Cramer von Mises types and are based on marked empirical processes coupled with a threshold bipower approach The tests impose no restrictions on the functional form of the drift function and are robust to jumps Monte Carlo simulations show that the tests have satisfactory size and good power under processes with or without jumps Application to a real dataset shows that our jump robust volatility tests give more reasonable results than the existing non robust volatility tests 2021 Statistical Society of Canada

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http://hdl.handle.net/10453/170833