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High Quantile Estimation for some Stochastic Volatility Models

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.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOU.#10393/20295
Date05 October 2011
CreatorsLuo, Ling
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThèse / Thesis

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