# Copula estimation for nonsynchronous financial data

## TL;DR

This paper develops new methods for estimating copulas in nonsynchronous intraday financial data, addressing bias issues and demonstrating improved accuracy through simulations and real data applications.

## Contribution

It introduces consistent estimators for copula parameters in nonsynchronous data, including an improved estimator for non-elliptical copulas, with theoretical convergence guarantees.

## Key findings

- The plug-in estimator is uniformly convergent for elliptical copulas.
- The quadratic model reduces bias in copula parameter estimation.
- Proposed methods improve accuracy in real stock price data.

## 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.