Parameter Estimation for Stable Distributions with Application to Commodity Futures Log-Returns

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dc.contributor.author Kateregga, Michael
dc.contributor.author Mataramvura, Sure
dc.contributor.author Taylor, David
dc.date.accessioned 2017-09-13T07:13:03Z
dc.date.available 2017-05-02
dc.date.available 2017-09-13T07:13:03Z
dc.date.issued 2017-05-02
dc.identifier.issn 2332-2039 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/25147
dc.description.abstract This paper explores the theory behind the rich and robust family of α-stable distributions to estimate parameters from financial asset log-returns data. We discuss four-parameter estimation methods including the quantiles, logarithmic moments method, maximum likelihood (ML), and the empirical characteristics function (ECF) method. The contribution of the paper is two-fold: first, we discuss the above parametric approaches and investigate their performance through error analysis. Moreover, we argue that the ECF performs better than the ML over a wide range of shape parameter values, α including values closest to 0 and 2 and that the ECF has a better convergence rate than the ML. Secondly, we compare the t location-scale distribution to the general stable distribution and show that the former fails to capture skewness which might exist in the data. This is observed through applying the ECF to commodity futures log-returns data to obtain the skewness parameter. en_ZA
dc.language eng en_ZA
dc.publisher Taylor and Francis en_ZA
dc.rights Creative Commons Attribution 4.0 International (CC BY 4.0) *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en_ZA
dc.source Cogent Economics and Finance en_ZA
dc.source.uri http://www.tandfonline.com/toc/oaef20/current
dc.title Parameter Estimation for Stable Distributions with Application to Commodity Futures Log-Returns en_ZA
dc.type Articles en_ZA
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
dc.publisher.institution University of Cape Town
uct.type.filetype Text
uct.type.filetype Image


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