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Stochastic Evolution of Distributions - Applications to CDS indices

Author

Listed:
  • Guillaume Bernis

    (Natixis Asset Management, Fixed Income)

  • Nicolas Brunel

    (Université d'Evry Val d'Essonne, ENSIIE)

  • Antoine Kornprobst

    (Centre d'Economie de la Sorbonne)

  • Simone Scotti

    (LPMA, Université Paris Diderot)

Abstract

We use mixture of percentile functions to model credit spread evolution, which allows to obtain a flexible description of credit indices and their components at the same time. We show regularity results in order to extend mixture percentile to the dynamic case. We characterise the stochastic differential equation of the flow of cumulative distribution function and we link it with the ordered list of the components of the credit index. The main application is to introduce a functional version of Bollinger bands. The crossing of bands by the spread is associated with a trading signal. Finally, we show the richness of the signals produced by functional Bollinger bands compared with standard one with a pratical example

Suggested Citation

  • Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Documents de travail du Centre d'Economie de la Sorbonne 17007, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:17007
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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2017/17007.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Mathematical Methods; Model Construction and Validation; Stochastic Analysis; Credit Default Swaps; Dynamic Distributions;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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