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

In: Liss 2012

Author

Listed:
  • Wang-Xiaoping

    (Jiaxing University)

  • Gao-Huimin

    (Jiaxing University)

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.

Suggested Citation

  • Wang-Xiaoping & Gao-Huimin, 2013. "Investment Risk Prediction Based on Multi-dimensional Tail Dependence Empirical Study," Springer Books, in: Zhenji Zhang & Runtong Zhang & Juliang Zhang (ed.), Liss 2012, edition 127, pages 507-512, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-32054-5_73
    DOI: 10.1007/978-3-642-32054-5_73
    as

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