
Mathematical Statistics
Time Series Analysis
LAP Lambert Academic Publishing
Published on 29. May 2014
Book
Paperback/Softback
60 pages
978-3-659-54380-7 (ISBN)
Description
Financial data have, among others, a particular feature: large values of such series cluster, we are concerned with estimation of clustering probabilities for univariate heavy tailed time series. We describe regular variation as a tool to model heavy tails. We summarize some results on the central limit theorem (CLT) and tightness of stochastic processes. These tools are needed to prove asymptotic normality of our estimator. We employ functional convergence of a bivariate tail empirical process,regular variation property and Lindeberg's CLT and the ¿¿mixing property with geometric rates to conclude asymptotic normality of an estimator of the clustering probabilities. Theoretical results are illustrated by simulation studies.
More details
Language
English
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 5 mm
Weight
107 gr
ISBN-13
978-3-659-54380-7 (9783659543807)
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Schweitzer Classification
Persons
M. Sc. in Mathematics and Statistics, University of Ottawa, Canada (2013). Postgraduate Diploma in Statistics, African Institute for Mathematical Sciences, South Africa (2011). Honours B. Sc. in Mathematics and Statistics, University Marien Ngouabi, Congo Brazzaville (2009). Research Interests: Time Series Analysis