In this thesis we consider estimation of the tail index for heavy tailed stochastic volatility models with long memory. We prove a central limit theorem for a Hill estimator. In particular, it is shown that neither the rate of convergence nor the asymptotic variance is affected by long memory. The theoretical findings are verified by simulation studies.

Thesis (Ed.D.)--Teachers College, Columbia University, 1974. / Typescript; issued also on microfilm. Sponsor: Michael Brick. Dissertation Committee: Richard Whittemore. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 236-258).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1956. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 225-231).

Thesis (M.S.)--University of Wisconsin, 1963. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 117-125).

Thesis--Wisconsin. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 228-241). Also available in electronic version.

No description available.

Search engines have revolutionised the access to information to the general public. Today search engines are the most important promotional method on the Internet. Sponsored search dominates the revenue model behind this growth. The rise in popularity and the auction pricing mechanism of sponsored advertising have increased the average cost-per-click. Marketing managers need tools to enable them to increase return on investment in this medium. The application of Anderson’s (2004) long tail distribution holds great promise to solve this dilemma. The current study used causal research in a two by two factorial design. Here data from an online property portal in a developing market was collected in order to examine the effect of a long tail (LT) distribution in keyword selection on return on investment (ROI) with sponsored search. Sponsored search allows for individualised targeting of the users behaviour. The application of the long tail (LT) enables further matching the advert text to the users search query. The results provide strong support for the significant impact on cost-per-click and by implication the return on investment that keyword selection and targeted advert text have when used in conjunction with the principles of the long tail. The interaction of the independent variables of long tail and sponsored search is significant, contributing to a 430% increase in click-through (CTR) rates and 61% reduction in cost-per-click, translating into a 61% increase in return on investment. / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

In this thesis we consider estimation of the tail index for heavy tailed stochastic volatility models with long memory. We prove a central limit theorem for a Hill estimator. In particular, it is shown that neither the rate of convergence nor the asymptotic variance is affected by long memory. The theoretical findings are verified by simulation studies.