Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/167817
Authors: 
Panahi, Hanieh
Year of Publication: 
2016
Citation: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 4 [Year:] 2016 [Issue:] 4 [Pages:] 1-14
Abstract: 
Numerous heavy-tailed distributions are used for modeling financial data and in problems related to the modeling of economics processes. These distributions have higher peaks and heavier tails than normal distributions. Moreover, in some situations, we cannot observe complete information about the data. Employing the efficient estimation method and then choosing the best model in this situation are very important. Thus, the purpose of this article is to propose a new interval for comparing the two heavy-tailed candidate models and examine its suitability in the financial data under complete and censored samples. This interval is equivalent to encapsulating the results of many hypotheses tests. A maximum likelihood estimator (MLE) is used for evaluating the parameters of the proposed heavy-tailed distribution. A real dataset representing the top 30 companies of the Tehran Stock Exchange indices is used to illustrate the derived results.
Subjects: 
asymptotic distribution
censored sample
heavy-tailed distribution
model selection test
Tehran Stock Exchange
JEL: 
C12
C13
C24
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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