Copula is a powerful tool to model multivariate data. Due to its several merits Copula modelling has become one of the most widely used methods to model financial data. We discuss the problem of modelling intraday financial data through Copula . The problem originates due to the nonsynchronous nature of intraday financial data whereas to estimate the Copula, we need synchronous observations. We show that this problem may lead to serious underestimation of the Copula parameter . We propose a modification to obtain a consistent estimatorin case of Elliptical Copula or to reduce the bias significantly in case of general copulas .
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 .
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Due to its several merits Copula modelling has become one of the most widely used methods to model financial data. We discuss the problem of modelling intraday financial data through Copula
We propose the modelling of intraday financial returns of multiple assets through copula
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nonsynchronous
asynchronous
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whereas to estimate the Copula, we need synchronous observations. We show that this problem may lead to serious
. 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
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Copula parameter . We propose a modification to obtain a consistent estimatorin case of Elliptical Copula or to reduce the bias significantly in case of general copulas
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