IDEAS home Printed from https://ideas.repec.org/p/mse/cesdoc/b08049.html
   My bibliography  Save this paper

Dynamic analysis of the insurance linked securities index

Author

Abstract

This paper aims to provide a dynamic analysis of the insurance linked securities index. We are discussing the behaviour of the index for three years and pointing out the consequences of some major events like Katrina or the last and current financial crisis. Some stylized facts of the index, like the non-Gaussianity, the asymmetry or the clusters of volatility, are highlighted. We are using some GARCH-type models and the generalized hyperbolic distributions in order to capture these elements. The GARCH in Mean model with a Normal Inverse Gaussian distribution seems to be very efficient to fit the log-returns of the insurance linked securities index.

Suggested Citation

  • Mathieu Gatumel & Dominique Guegan, 2008. "Dynamic analysis of the insurance linked securities index," Documents de travail du Centre d'Economie de la Sorbonne b08049, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Handle: RePEc:mse:cesdoc:b08049
    as

    Download full text from publisher

    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2008/B08049.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ng, Hock Guan & McAleer, Michael, 2004. "Recursive modelling of symmetric and asymmetric volatility in the presence of extreme observations," International Journal of Forecasting, Elsevier, vol. 20(1), pages 115-129.
    2. Verbeek, Marno, 2007. "A Guide to Modern Econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 8(4), pages 125-132.
    3. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Morten B. Jensen & Asger Lunde, 2001. "The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model," Econometrics Journal, Royal Economic Society, vol. 4(2), pages 1-10.
    6. Park, Beum-Jo, 2002. "An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 381-393, August.
    7. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    8. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    9. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Insurance Linked Securities; Garch-type models; normal Inverse Gaussian Distribution.;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mse:cesdoc:b08049. 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: (Lucie Label) or (Joanne Lustig). General contact details of provider: http://edirc.repec.org/data/cenp1fr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.