We study how co-jumps influence covariance and correlation in currency markets. We propose a new wavelet-based estimator of quadratic covariation that is able to disentangle the continuous part of quadratic covariation from co-jumps  . The proposed estimator is able to  identify the statistically significant co-jumps that impact covariance structures by using bootstrapped test statistics. Empirical findings reveal the behavior of co-jumps during Asian, European and U.S. trading sessions. Our results show that the impact of co-jumps on correlations increased during the years 2012 - 2015. Hence appropriately estimating co-jumps is becoming a crucial step in understanding dependence in currency markets.
We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps , and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator . The proposed estimation framework is able to localize the co-jumps very precisely through wavelet coefficients and identify the statistically significant co-jumps using bootstrapped test statistics. Empirical findings reveal the different behaviors of co-jumps during Asian, European and U.S. trading sessions. Importantly, we document that co-jumps significantly inflate correlation in currency markets.
R
study
quantify
R
influence covariance and correlation
impact correlations
R
We propose a new wavelet-based estimator of quadratic covariation that is able to
To
A
, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator
R
estimator
estimation framework
A
localize the co-jumps very precisely through wavelet coefficients and
D
that impact covariance structures by
R
behavior
different behaviors
R
Our results show that the impact of
Importantly, we document that
R
on correlations increased during the years 2012 - 2015. Hence appropriately estimating co-jumps is becoming a crucial step in understanding dependence
significantly inflate correlation