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dc.contributor.advisorsmith, Vernon L.en_US
dc.contributor.authorAttiyeh, Gregory, 1966-
dc.creatorAttiyeh, Gregory, 1966-en_US
dc.date.accessioned2013-05-09T09:53:08Z
dc.date.available2013-05-09T09:53:08Z
dc.date.issued1997en_US
dc.identifier.urihttp://hdl.handle.net/10150/289367
dc.description.abstractThis paper explores the relationship between strategic trading and the clustering of volatility commonly found in financial asset returns. The aim is to provide a theoretical foundation for the volatility clustering which has been documented in the econometric literature of ARCH and stochastic volatility models. The analysis builds on the notion that, while exogenously occurring events generate new information, investor activity ultimately determines how quickly information becomes incorporated into prices. In the model developed, a single investor obtains superior information about the implications of news events for an asset's value and is able to exploit his informational advantage over time by trading with a market maker. The model extends the market microstructure literature by allowing the trader to receive a flow of information through time and by making uncertain the length of time the trader's information remains private. Results show how an investor's trading behavior can influence the arrival of information into the market and cause above average volatility to cluster and persist, even when the arrival of events does not. A key finding is that, while the level of volatility is sensitive to the rate at which private information flows, its persistence is driven by the nature of the trader's uncertainty. The model and its extensions are tested in a computer laboratory setting with human subjects, which permits control of the underlying distributions of values and events. In testing the model's hypotheses experimentally, two underlying issues are addressed: whether trading will tend toward the theoretical dynamic equilibrium and whether agents' preferences are suitably modeled as risk neutral. Analysis of the experimental data reveals that market makers' pricing was more sensitive, informed trading was more cautious, and price volatility was higher than the risk neutral model predicts. Estimation of an alternative model specification reveals that behavior was consistent with risk aversion.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectEconomics, Finance.en_US
dc.titleA theoretical and experimental examination of volatility persistence in financial marketsen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9729540en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineEconomicsen_US
thesis.degree.namePh.D.en_US
dc.description.noteThis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.identifier.bibrecord.b34841362en_US
dc.description.admin-noteOriginal file replaced with corrected file October 2023.
refterms.dateFOA2018-08-29T08:51:57Z
html.description.abstractThis paper explores the relationship between strategic trading and the clustering of volatility commonly found in financial asset returns. The aim is to provide a theoretical foundation for the volatility clustering which has been documented in the econometric literature of ARCH and stochastic volatility models. The analysis builds on the notion that, while exogenously occurring events generate new information, investor activity ultimately determines how quickly information becomes incorporated into prices. In the model developed, a single investor obtains superior information about the implications of news events for an asset's value and is able to exploit his informational advantage over time by trading with a market maker. The model extends the market microstructure literature by allowing the trader to receive a flow of information through time and by making uncertain the length of time the trader's information remains private. Results show how an investor's trading behavior can influence the arrival of information into the market and cause above average volatility to cluster and persist, even when the arrival of events does not. A key finding is that, while the level of volatility is sensitive to the rate at which private information flows, its persistence is driven by the nature of the trader's uncertainty. The model and its extensions are tested in a computer laboratory setting with human subjects, which permits control of the underlying distributions of values and events. In testing the model's hypotheses experimentally, two underlying issues are addressed: whether trading will tend toward the theoretical dynamic equilibrium and whether agents' preferences are suitably modeled as risk neutral. Analysis of the experimental data reveals that market makers' pricing was more sensitive, informed trading was more cautious, and price volatility was higher than the risk neutral model predicts. Estimation of an alternative model specification reveals that behavior was consistent with risk aversion.


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