Investment Risk Prediction Based on Multi-dimensional Tail Dependence Empirical Study

Wang-Xiaoping and Gao-Huimin ()
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Wang-Xiaoping: Jiaxing University
Gao-Huimin: Jiaxing University

A chapter in LISS 2012, 2013, pp 507-512 from Springer

Abstract: Abstract Risk prediction plays a very important role in avoiding capital risk of investors, while tail dependence analysis is vital to risk prediction. Adopting D-vine model, with the data of the weekly-closing-price of China stock market and the stock market of neighboring countries and regions, this paper puts forward empirical distribution fit marginal distribution by using the t-copula, Clayton copula and Joe-Clayton copula to decompose the multivariate density function and analyze the tail dependence in multi-dimensional case. The experiments show that the pair copula model surely can be used to solve the tail dependence in multi-dimensional case efficiently.

Keywords: Risk Prediction; Tail Dependence; D-vine Model; Stock Market (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-32054-5_73

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DOI: 10.1007/978-3-642-32054-5_73

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