Analysis of dependencies in low frequency financial data sets
AbstractThis empirical study proposes a dependency analysis of monthly financial time series. We use the overlapping technique and non-parametric correlation in order to increase both accuracy and consistency. Copulas are used to test extreme co-movements between financial securities. Our results indicate that even in a low-frequency framework, the common practice of assuming independence over time should be taken with caution due to the presence of GARCH effects. In addition, extreme co-movements are observed across securities, especially for interest rates.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 12682.
Date of creation: 2003
Date of revision:
dependencies; low-frequency; monthly; copula; GARCH;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
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- Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
- H. A. Hauksson & M. Dacorogna & T. Domenig & U. Mller & G. Samorodnitsky, 2001.
"Multivariate extremes, aggregation and risk estimation,"
Taylor & Francis Journals, vol. 1(1), pages 79-95.
- Michel Dacorogna & Höskuldur Ari Hauksson & Thomas Domenig & Ulrich Müller & Gennady Samorodnitsky, 2001. "Multivariate extremes, aggregation and risk estimation," CeNDEF Workshop Papers, January 2001 P2, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Peter Blum & Michel Dacorogna & Lars Jaeger, 2003. "Performance and Risk Measurement Challenges For Hedge Funds: Empirical Considerations," Risk and Insurance 0311001, EconWPA.
- Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
- Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
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