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Time variation in the correlation structure of exchange rates: high‐frequency analyses

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  • Jayaram Muthuswamy
  • Sudipto Sarkar
  • Aaron Low
  • Eric Terry

Abstract

The correlation structure of asset returns is a crucial parameter in risk management as well as in theoretical finance. In practice, however, the true correlation structure between the returns of assets can easily become obscured by time variation in the observed correlation structure and in the liquidity of the assets. We employed a time‐stamped high‐frequency data set of exchange rates, namely, the US$–deutsche mark and the US$–yen exchange rates, to calibrate the observed time variation in the correlation structure between their returns. We also documented time variation in the liquidity structure of these rates. We then attempted to link the observed correlations with the liquidity via an application of an illiquid trading model first developed by Scholes and Williams (1976). We show that the observed correlation structure is strongly biased by the liquidity and that it is possible to effect at least a partial rectification of the otherwise downward‐biased observed correlation. The rectified sample correlation is, therefore, more appropriate for input into models used for forecasting, option pricing, and other risk management applications. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:127–144, 2001

Suggested Citation

  • Jayaram Muthuswamy & Sudipto Sarkar & Aaron Low & Eric Terry, 2001. "Time variation in the correlation structure of exchange rates: high‐frequency analyses," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(2), pages 127-144, February.
  • Handle: RePEc:wly:jfutmk:v:21:y:2001:i:2:p:127-144
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    Cited by:

    1. Potiron, Yoann & Mykland, Per A., 2017. "Estimation of integrated quadratic covariation with endogenous sampling times," Journal of Econometrics, Elsevier, vol. 197(1), pages 20-41.
    2. Arnab Chakrabarti & Rituparna Sen, 2022. "Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix," Papers 2201.00119, arXiv.org.
    3. Wang, Xi & Yang, Jiao-Hui & Wang, Kai-Li & Fawson, Christopher, 2017. "Dynamic information spillovers in intraregionally-focused spot and forward currency markets," Journal of International Money and Finance, Elsevier, vol. 71(C), pages 78-110.
    4. Christodoulakis, George A., 2007. "Common volatility and correlation clustering in asset returns," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1263-1284, November.
    5. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    6. Arnab Chakrabarti & Rituparna Sen, 2023. "Copula Estimation for Nonsynchronous Financial Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 116-149, May.
    7. Henryk Gurgul & Artur Machno, 2017. "The impact of asynchronous trading on Epps effect on Warsaw Stock Exchange," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 287-301, June.

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