Ardia, David (2003): Analysis of dependencies in low frequency financial data sets.

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Abstract
This empirical study proposes a dependency analysis of monthly financial time series. We use the overlapping technique and nonparametric correlation in order to increase both accuracy and consistency. Copulas are used to test extreme comovements between financial securities. Our results indicate that even in a lowfrequency framework, the common practice of assuming independence over time should be taken with caution due to the presence of GARCH effects. In addition, extreme comovements are observed across securities, especially for interest rates.
Item Type:  MPRA Paper 

Original Title:  Analysis of dependencies in low frequency financial data sets 
Language:  English 
Keywords:  dependencies; lowfrequency; monthly; copula; GARCH 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C50  General G  Financial Economics > G2  Financial Institutions and Services > G22  Insurance; Insurance Companies 
Item ID:  12682 
Depositing User:  David Ardia 
Date Deposited:  13. Jan 2009 07:01 
Last Modified:  14. Feb 2013 06:48 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/12682 