Herding, trend chasing and market volatility

Di Guilmi, C; He, XZ; Li, K

Herding, trend chasing and market volatility

Di Guilmi, C

He, XZ

Li, K

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Publication Type:: Journal Article
Citation:: Journal of Economic Dynamics and Control, 2014, 48 pp. 349 - 373
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Di Guilmi, C https://orcid.org/0000-0001-7786-039X	en_US
dc.contributor.author	He, XZ https://orcid.org/0000-0003-1446-9996	en_US
dc.contributor.author	Li, K https://orcid.org/0000-0003-1620-8829	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Economic Dynamics and Control, 2014, 48 pp. 349 - 373	en_US
dc.identifier.issn	0165-1889	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121969
dc.description.abstract	© 2014 Elsevier B.V. We introduce a heterogeneous agent asset pricing model in continuous-time to show that, although trend chasing, switching and herding all contribute to market volatility in price and return and to volatility clustering, their impacts are different. The fluctuations of the market price and return and the level of the significant autocorrelations (ACs) of the absolute and squared returns increase with the intensities of herding and trend chasing based on long time horizon. However an increase in switching intensity reduces the return volatility and in particular a low switching intensity reduces the price volatility and increases the level of the significant ACs, but the effect becomes opposite when the switching intensity is high. We also show that market noise plays a more important role than fundamental noise on the power-law behavior of returns.	en_US
dc.relation.ispartof	Journal of Economic Dynamics and Control	en_US
dc.relation.isbasedon	10.1016/j.jedc.2014.07.008	en_US
dc.subject.classification	Economics	en_US
dc.title	Herding, trend chasing and market volatility	en_US
dc.type	Journal Article
utslib.citation.volume	48	en_US
utslib.for	1401 Economic Theory	en_US
utslib.for	1402 Applied Economics	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2014 Elsevier B.V. We introduce a heterogeneous agent asset pricing model in continuous-time to show that, although trend chasing, switching and herding all contribute to market volatility in price and return and to volatility clustering, their impacts are different. The fluctuations of the market price and return and the level of the significant autocorrelations (ACs) of the absolute and squared returns increase with the intensities of herding and trend chasing based on long time horizon. However an increase in switching intensity reduces the return volatility and in particular a low switching intensity reduces the price volatility and increases the level of the significant ACs, but the effect becomes opposite when the switching intensity is high. We also show that market noise plays a more important role than fundamental noise on the power-law behavior of returns.

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http://hdl.handle.net/10453/121969