Characterizing Asymptotic Dependence via Conditional Kendall’s tau
Alexandru V. Asimit & Russell Gerrard – Cass Business School,
Yanxi Hou – Georgia Institute of Technology and Liang Peng – Georgia State University

Analysing the concomitant extreme events has become of vast interest in many applications. The first step in statistical extreme inference is to make a binary decision for the presence of asymptotic dependence or independence. Therefore, distinguishing between (only) these possible scenarios represents a crucial piece of information before starting any forecasting. Since the strength of dependence can be characterised via some measures of association, the Kendall’s tau is a good candidate to simply identify asymptotic dependence or independence. The main advantage of using this measure is due to its simplicity and ease of communication beyond the statistical academic community and practitioners. Convincing applications are detailed within the environmental statistics, insurance and finance fields. At a quantitative level, a non-parametric estimator is proposed in order to make the appropriate decisions before making any predictions.

Alexandru V. Asimit

Vali Asimit joined Cass Business in January 2011 as a Lecturer in Actuarial Science. Previously, he had been a Lecturer in Actuarial Science at the University of Manchester for two years. Vali had studied Economics at the Academy of Economic Studies, Bucharest, Romania. He has an MSc in Statistics from the University of Western Ontario, Canada. He pursued his doctoral research on “Dependence Modelling with Applications in Finance and Insurance” at the University of Western Ontario. As part of his academic work he has published and acted as referee for international statistical and actuarial journals. Vali received the 2010 Fortis Award for the best Insurance: Mathematics and Economics (IME) journal paper presented at the 14th International Congress of IME. To know more…


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