Randomized quasi-Monte Carlo methods have been shown to offer estimates with smaller variances compared with estimates obtained with
Monte Carlo. This dissertation examines the application of randomized quasi-Monte Carlo methods in the context of value-at-risk and expected
shortfall, two measures of downside risk associated with financial portfolios. It finds that while the randomized quasi-Monte Carlo estimates
have the variance-reduction of estimates property when applied to the aforementioned risk measures of financial portfolios, the reduced standard
errors have a rate of convergence much closer to 1/√M than the potential 1/M described by the theory for the 22-day time horizon of
value-at-risk and expected shortfall. The rate of convergence increased for the 8-day horizon, suggesting that the advantages of randomized
quasi-Monte Carlo estimation in terms of standard error of estimates and accuracy of estimates improve for shorter time horizons of the
aforementioned risk measures and are no worse for longer time horizons. / A Dissertation submitted to the Department of Economics in partial fulfillment of the requirements for the
degree of Doctor of Philosophy. / Fall Semester 2018. / November 1, 2018. / Includes bibliographical references. / Milton Marquis, Professor Directing Dissertation; Giray Okten, University Representative; Paul Beaumont,
Committee Member; Gary Fournier, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_661137 |
| Contributors | Franke, Stephen Robert (author), Marquis, Milton H. (professor directing dissertation), Ökten, Giray (university representative), Beaumont, Paul M. (committee member), Fournier, Gary M. (committee member), Florida State University (degree granting institution), College of Social Sciences and Public Policy (degree granting college), Department of Economics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (202 pages), computer, application/pdf |
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