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Using Copulas to Construct Bivariate Foreign Exchange Distributions with an Application to the Sterling Exchange Rate Index

Author

Listed:
  • Christoph Schleicher
  • Matthew Hurd
  • Mark Salmon

Abstract

We model the joint distribution between the euro-sterling and the dollar-sterling exchange rate using option-implied markginal distributions that are connected via a copula function. We then derive univariate distributions for the simpliefied sterling effective exchange rate index (ERI). Our results indicate that simple parametric copula functions, such as the commonly used Normal and Frank copulas, fail to capture the degree of asymmetry observed in the data. We overcome this problem by using a non-parametric dependence function in the form of a Bernstein copula

Suggested Citation

  • Christoph Schleicher & Matthew Hurd & Mark Salmon, 2005. "Using Copulas to Construct Bivariate Foreign Exchange Distributions with an Application to the Sterling Exchange Rate Index," Computing in Economics and Finance 2005 215, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:215
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    Cited by:

    1. Fabrizio Durante & Erich Klement & Carlo Sempi & Manuel Úbeda-Flores, 2010. "Measures of non-exchangeability for bivariate random vectors," Statistical Papers, Springer, vol. 51(3), pages 687-699, September.
    2. Mendoza-Velázquez, Alfonso & Galvanovskis, Evalds, 2009. "Introducing the GED-Copula with an application to Financial Contagion in Latin America," MPRA Paper 46669, University Library of Munich, Germany, revised 01 Feb 2010.
    3. Zhichao Zhang & Li Ding & Fan Zhang & Zhuang Zhang, 2015. "Optimal Currency Composition for China's Foreign Reserves: A Copula Approach," The World Economy, Wiley Blackwell, vol. 38(12), pages 1947-1965, December.
    4. Constantino, Michel & Candido, Osvaldo & Tabak, Benjamin M. & da Costa, Reginaldo Brito, 2017. "Modeling stochastic frontier based on vine copulas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 595-609.
    5. De Col, Alvise & Gnoatto, Alessandro & Grasselli, Martino, 2013. "Smiles all around: FX joint calibration in a multi-Heston model," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3799-3818.
    6. Tavin, Bertrand, 2015. "Detection of arbitrage in a market with multi-asset derivatives and known risk-neutral marginals," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 158-178.
    7. Mendoza, Alfonso. & Galvanovskis, Evalds., 2014. "La cópula GED bivariada. Una aplicación en entornos de crisis," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(323), pages .721-746, julio-sep.
    8. Fouad Marri & Khouzeima Moutanabbir, 2021. "Risk aggregation and capital allocation using a new generalized Archimedean copula," Working Papers hal-03169291, HAL.
    9. Toan Luu Duc Huynh & Tobias Burggraf, 2020. "If worst comes to worst: Co-movement of global stock markets in the US-China trade war," Economics and Business Letters, Oviedo University Press, vol. 9(1), pages 21-30.
    10. Fouad Marri & Khouzeima Moutanabbir, 2021. "Risk aggregation and capital allocation using a new generalized Archimedean copula," Papers 2103.10989, arXiv.org.
    11. Marri, Fouad & Moutanabbir, Khouzeima, 2022. "Risk aggregation and capital allocation using a new generalized Archimedean copula," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 75-90.
    12. Alessandro Gnoatto & Martino Grasselli, 2013. "An analytic multi-currency model with stochastic volatility and stochastic interest rates," Papers 1302.7246, arXiv.org, revised Mar 2013.

    More about this item

    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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