Test of fit for a Laplace distribution against heavier tailed alternatives
Over the last decade there has been a marked interest in a Laplace distribution and its properties and generalizations, especially in the framework of financial applications. Such an interest has led to a revision and discussion of available goodness-of-fit procedures for a Laplace distribution. Indeed, since most of the studies which employ the Laplace distribution are concerned with modelling heavy tailed patterns, the modern class of possible alternatives is way broader than just testing the Laplace vs. normal distribution. In this paper we propose a new test of fit for a Laplace distribution against deviations with heavier tails than that of the reference Laplace distribution. The proposed goodness-of-fit procedure is based on sample skewness and kurtosis and a robust L1 estimator of scale about a sample median. The developed test statistic is shown to asymptotically follow a [chi]2-distribution with two degrees of freedom. Performance of the new goodness-of-fit test is illustrated by simulations and a case study.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Stefan Mittnik & Marc Paolella & Svetlozar Rachev, 1998. "Unconditional and Conditional Distributional Models for the Nikkei Index," Asia-Pacific Financial Markets, Springer, vol. 5(2), pages 99-128, May.
- Ole E. Barndorff-Nielsen, 1997. "Processes of normal inverse Gaussian type," Finance and Stochastics, Springer, vol. 2(1), pages 41-68.
- Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2005.
"Modeling and predicting market risk with Laplace-Gaussian mixture distributions,"
CFS Working Paper Series
2005/11, Center for Financial Studies (CFS).
- Markus Haas & Stefan Mittnik & Marc Paolella, 2006. "Modelling and predicting market risk with Laplace-Gaussian mixture distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 16(15), pages 1145-1162.
- Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
- Linden, Mikael, 2001. "A Model for Stock Return Distribution," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(2), pages 159-69, April.
- Thorsten Thadewald & Herbert Buning, 2007. "Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 87-105.
- Best, D.J. & Rayner, J.C.W. & Thas, O., 2008. "Comparison of some tests of fit for the Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5338-5343, August.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:958-965. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.