Modelling heavy tails and double long memory in North African stock market returns

2012 ◽  
Vol 17 (2) ◽  
pp. 195-214 ◽  
Author(s):  
Adel Boubaker ◽  
Beljid Makram
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Heavy Tails ◽  
North African ◽  
Market Returns ◽  
Stock Market Returns

scholarly journals A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

2017 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
John Oden ◽  
Kevin Hurt ◽  
Susan Gentry
Keyword(s):  
Stock Market ◽  
Global Economy ◽  
Heavy Tails ◽  
Financial Sector ◽  
Superior Performance ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering ◽  
Empirical Performance ◽  
Heavy Tailed

As the fourth largest economy over the world, Germany’s financial sector plays a key role in the global economy. As one of the most important components of the financial sector, the equity market played a more and more important role. Thus, risk management of its stock market is crucial for welfare of its market participants. To account for the two stylized facts, volatility clustering and conditional heavy tails, we take advantage of the framework in Guo (2016) and consider empirical performance of the GARCH model with normal reciprocal inverse Gaussian distribution in fitting the German stock return series. Our results indicate the NRIG distribution has superior performance in fitting the stock market returns.

A Long Memory Property of Stock Market Returns and a New Model

2010 ◽  
pp. 349-372
Author(s):  
Zhuanxin Ding ◽  
Robert F. Engle ◽  
Clive W. J. Granger
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Market Returns ◽  
Stock Market Returns ◽  
New Model ◽  
Memory Property
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