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    Empirical analyses of extreme value models for the South African mining index

    Access Status
    Fulltext not available
    Authors
    Chinhamu, K.
    Huang, Chun-Kai
    Huang, C.
    Hammujuddy, J.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Chinhamu, K. and Huang, C. and Huang, C. and Hammujuddy, J. 2015. Empirical analyses of extreme value models for the South African mining index. South African Journal of Economics. 83 (1): pp. 41-55.
    Source Title
    South African Journal of Economics
    DOI
    10.1111/saje.12051
    ISSN
    0038-2280
    URI
    http://hdl.handle.net/20.500.11937/67499
    Collection
    • Curtin Research Publications
    Abstract

    © 2014 Economic Society of South Africa. While the classical normality assumption is simple to implement, it is well known to underestimate the leptokurtic behaviour demonstrated in most financial data. After examining properties of the Johannesburg Stock Exchange Mining Index returns, we propose two extreme value models to fit its negative tail with a higher degree of accuracy. The generalised extreme value distribution (GEVD) is fitted using the block maxima approach, while the generalised Pareto distribution (GPD) is fitted using the peaks-over-threshold method. Numerical assessment of value-at-risk (VaR) estimates indicates that both GEVD and GPD increasingly outperform the normal distribution as we move further into the lower tail. In addition, GEVD produces lower estimates relative to that of the historical VaR, and GPD provides slightly more conservative estimates for adequate capitalisation.

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