A collection of essays in empirical finance
| dc.contributor.advisor | Lamoureux, Chris | en_US |
| dc.contributor.author | Roskelley, Kenneth | |
| dc.creator | Roskelley, Kenneth | en_US |
| dc.date.accessioned | 2013-04-11T08:45:57Z | |
| dc.date.available | 2013-04-11T08:45:57Z | |
| dc.date.issued | 2002 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/280043 | |
| dc.description.abstract | This dissertation consists of three papers. The first assesses the ability of bivariate distribution models to explain the contemporaneous and autocorrelation between volume and volatility. GMM is used to fit first and second moments of the model to the data and analyze the model's fit. The second paper looks at the uncertainty surrounding cost recovery in regulated utilities. Stock market data is used to ascertain the market's perception about the deregulation of electricity in the United States. The third and final paper looks at the economic evidence for a stochastic opportunity set from an investor's point of view. A Bayesian investor must allocate her wealth between a risky and a risk free asset after observing market data when the model for asset returns is unknown and returns are potentially predictable. | |
| dc.language.iso | en_US | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.rights | Copyright © 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.subject | Economics, Finance. | en_US |
| dc.subject | Energy. | en_US |
| dc.title | A collection of essays in empirical finance | en_US |
| dc.type | text | en_US |
| dc.type | Dissertation-Reproduction (electronic) | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.identifier.proquest | 3053911 | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.discipline | Business Administration | en_US |
| thesis.degree.name | Ph.D. | en_US |
| dc.identifier.bibrecord | .b42819684 | en_US |
| refterms.dateFOA | 2018-06-28T11:26:34Z | |
| html.description.abstract | This dissertation consists of three papers. The first assesses the ability of bivariate distribution models to explain the contemporaneous and autocorrelation between volume and volatility. GMM is used to fit first and second moments of the model to the data and analyze the model's fit. The second paper looks at the uncertainty surrounding cost recovery in regulated utilities. Stock market data is used to ascertain the market's perception about the deregulation of electricity in the United States. The third and final paper looks at the economic evidence for a stochastic opportunity set from an investor's point of view. A Bayesian investor must allocate her wealth between a risky and a risk free asset after observing market data when the model for asset returns is unknown and returns are potentially predictable. |
