Testing for simultaneous jumps in case of asynchronous observations

TL;DR

This paper introduces a new statistical test for detecting simultaneous jumps in two processes observed asynchronously, using a correlation-based approach and bootstrap inference, with strong simulation results.

Contribution

It develops a novel test for simultaneous jumps that operates effectively on irregular, asynchronous data without requiring synchronization.

Findings

Test performs well compared to regular observation cases

Bootstrap method accurately assesses asymptotic distribution

Effective in irregular, asynchronous observation settings

Abstract

This paper proposes a novel test for simultaneous jumps in a bivariate It\^o semimartingale when observation times are asynchronous and irregular. Inference is built on a realized correlation coefficient for the jumps of the two processes which is estimated using bivariate power variations of Hayashi-Yoshida type without an additional synchronization step. An associated central limit theorem is shown whose asymptotic distribution is assessed using a bootstrap procedure. Simulations show that the test works remarkably well in comparison with the much simpler case of regular observations.