Testing for cojumps in high-frequency financial data: an approach based on first-high-low-last prices

This paper proposes a new test for simultaneous intraday jumps (cojumps) in a panel of high frequency financial data. We utilize intraday first-high-low-last values of asset prices to construct estimates for the cross-variation of returns in a large panel of high frequency financial data, which we then use to form a test statistic that can detect cojumps. Simulations show that a bias corrected version of the test performs well when microstructure noise is present. Applied to a panel of high frequency Chinese equity data, our test identifies cojumps that coincide with announcements relating to monetary policy and stock market regulations.