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dc.contributor.authorChinhamu, K.
dc.contributor.authorHuang, Chun-Kai
dc.contributor.authorChikobvu, D.
dc.date.accessioned2018-05-18T07:58:54Z
dc.date.available2018-05-18T07:58:54Z
dc.date.created2018-05-18T00:23:15Z
dc.date.issued2017
dc.identifier.citationChinhamu, K. and Huang, C. and Chikobvu, D. 2017. Evaluating risk in precious metal prices with generalised lambda, generalised pareto and generalised extreme value distributions. South African Statistical Journal. 51 (1): pp. 159-182.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/67534
dc.description.abstract

In this study we investigate the performance of the generalised lambda distribution (GLD), the generalised Pareto distribution (GPD) and the generalised extreme value distribution (GEVD) in modelling daily platinum, gold and silver price log-returns. Our primary goal is to compare GLD against GPD, and GEVD, in the estimation of Value-at-Risk (VaR) and expected shortfall (ES) as per the international Basel regulatory framework. Our analyses show that GPD and GLD generally outperform GEVD for VaR and ES estimation for negative precious metal returns. For gold, the GPD stands out as the most suitable model. For platinum, GPD and GLD are equally adequate,especially at the 1% VaR level. For silver, GLD is the most suitable at 1% VaR level, whereas GPD is the best model at 0.1%. This study has shown that GLD is a suitable model for extreme risk in precious metal prices and can be used for the estimation of VaR and ES values.

dc.titleEvaluating risk in precious metal prices with generalised lambda, generalised pareto and generalised extreme value distributions
dc.typeJournal Article
dcterms.source.volume51
dcterms.source.number1
dcterms.source.startPage159
dcterms.source.endPage182
dcterms.source.issn0038-271X
dcterms.source.titleSouth African Statistical Journal
curtin.accessStatusFulltext not available


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