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Testing for structural changes in exchange rates' dependence beyond linear correlation

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  • Alexandra Dias
  • Paul Embrechts

Abstract

In this paper, we test for structural changes in the conditional dependence of two-dimensional foreign exchange data. We show that by modeling the conditional dependence structure using copulae, we can detect changes in the dependence beyond linear correlation, such as changes in the tail of the joint distribution. This methodology is relevant for estimating risk-management measures, such as portfolio value-at-risk, pricing multi-name financial instruments, and portfolio asset allocation. Our results include evidence of the existence of changes in the correlation as well as in the fatness of the tail of the dependence between Deutsche mark and Japanese yen.

Suggested Citation

  • Alexandra Dias & Paul Embrechts, 2009. "Testing for structural changes in exchange rates' dependence beyond linear correlation," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 619-637.
    • Handle: RePEc:taf:eurjfi:v:15:y:2009:i:7-8:p:619-637
    DOI: 10.1080/13518470701705579
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    Citations

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    Cited by:

    1. Morettin Pedro A. & Toloi Clelia M.C. & Chiann Chang & de Miranda José C.S., 2011. "Wavelet Estimation of Copulas for Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-31, October.
    2. Vandna Jowaheer & Nafeessah Z. B. Ameerudden, 2012. "Modelling the Dependence Structure of MUR/USD and MUR/INR Exchange Rates using Copula," International Journal of Economics and Financial Issues, Econjournals, vol. 2(1), pages 27-32.
    3. Pircalabu, A. & Benth, F.E., 2017. "A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets," Energy Economics, Elsevier, vol. 68(C), pages 283-302.
    4. Ji, Qiang & Liu, Bing-Yue & Fan, Ying, 2019. "Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model," Energy Economics, Elsevier, vol. 77(C), pages 80-92.
    5. Pedro Alberto Morettin & Clélia Maria de Castro Toloi & Chang Chiann & José Carlos Simon de Miranda, 2010. "Wavelet Smoothed Empirical Copula Estimators," Brazilian Review of Finance, Brazilian Society of Finance, vol. 8(3), pages 263-281.
    6. Markwat, T.D. & Kole, H.J.W.G. & van Dijk, D.J.C., 2009. "Time Variation in Asset Return Dependence: Strength or Structure?," ERIM Report Series Research in Management ERS-2009-052-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Weiß, Gregor N.F. & Neumann, Sascha & Bostandzic, Denefa, 2014. "Systemic risk and bank consolidation: International evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 165-181.
    8. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    9. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
    10. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    11. Liu, Bing-Yue & Ji, Qiang & Fan, Ying, 2017. "Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: A time-varying mixed copula model," Energy Economics, Elsevier, vol. 68(C), pages 53-65.
    12. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    13. Yangguang Zhu & Feng Yang & Wuyi Ye, 2018. "Financial contagion behavior analysis based on complex network approach," Annals of Operations Research, Springer, vol. 268(1), pages 93-111, September.
    14. Ye, Wuyi & Liu, Xiaoquan & Miao, Baiqi, 2012. "Measuring the subprime crisis contagion: Evidence of change point analysis of copula functions," European Journal of Operational Research, Elsevier, vol. 222(1), pages 96-103.
    15. Thijs Markwat, 2014. "The rise of global stock market crash probabilities," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 557-571, April.
    16. Gregor Weiß, 2012. "Analysing contagion and bailout effects with copulae," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(1), pages 1-32, January.
    17. Weiß, Gregor N.F. & Bostandzic, Denefa & Neumann, Sascha, 2014. "What factors drive systemic risk during international financial crises?," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 78-96.
    18. Gregor Wei{ss} & Marcus Scheffer, 2012. "Smooth Nonparametric Bernstein Vine Copulas," Papers 1210.2043, arXiv.org.

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