EconPapers    
Economics at your fingertips  
 

New fat-tail normality test based on conditional second moments with applications to finance

Damian Jelito and Marcin Pitera

Papers from arXiv.org

Abstract: In this paper we introduce an efficient fat-tail measurement framework that is based on conditional second moments. We construct goodness-of-fit statistic that has a direct financial interpretation and can be used to assess the impact of fat-tails on central data normality assumption. Next, we show how to use our framework to construct a powerful statistical normality test. In particular, we compare our methodology to various popular normality statistical tests, including the Jarque--Bera test that is based on third and fourth moments, and show that in most considered cases our framework outperforms all others, both on simulated and market-stock data. Finally, we derive asymptotic distributions for conditional mean and variance estimators, and use this to show asymptotic normality of the proposed test statistic.

New Economics Papers: this item is included in nep-ecm
Date: 2018-11, Revised 2019-03
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://arxiv.org/pdf/1811.05464 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1811.05464

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2019-03-05
Handle: RePEc:arx:papers:1811.05464