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Copula estimation for nonsynchronous financial data

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  • Arnab Chakrabarti
  • Rituparna Sen

Abstract

Copula is a powerful tool to model multivariate data. We propose the modelling of intraday financial returns of multiple assets through copula. The problem originates due to the asynchronous nature of intraday financial data. We propose a consistent estimator of the correlation coefficient in case of Elliptical copula and show that the plug-in copula estimator is uniformly convergent. For non-elliptical copulas, we capture the dependence through Kendall's Tau. We demonstrate underestimation of the copula parameter and use a quadratic model to propose an improved estimator. In simulations, the proposed estimator reduces the bias significantly for a general class of copulas. We apply the proposed methods to real data of several stock prices.

Suggested Citation

  • Arnab Chakrabarti & Rituparna Sen, 2019. "Copula estimation for nonsynchronous financial data," Papers 1904.10182, arXiv.org, revised Sep 2020.
    • Handle: RePEc:arx:papers:1904.10182
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    References listed on IDEAS

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    1. Grossmass Lidan & Poon Ser-Huang, 2015. "Estimating dynamic copula dependence using intraday data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 501-529, September.
    2. Roberto Renò, 2003. "A Closer Look At The Epps Effect," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 87-102.
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