Palmes, ChristianWoerner, Jeannette H.C.2013-12-102013-12-102013-12-10http://hdl.handle.net/2003/3129410.17877/DE290R-13114In the framework of jump detection in stochastic volatility models the Gumbel test based on extreme value theory has recently been introduced. Compared to other jump tests it possesses the advantages that the direction and location of jumps may also be detected. Furthermore, compared to the Barndorff-Nielsen and Shephard test based on bipower variation the Gumbel test possesses a larger power. However, so far one assumption was that the volatility process is Hölder continuous, though there is empirical evidence for jumps in the volatility as well. In this paper we derive that the Gumbel test still works under the setting of finitely many jumps not exceeding a certain size. Furthermore, we show that the given bound on the jump size is sharp.enjump teststochastic volatility modelvolatility process with jumpsGumbel distributionextreme value theoryhigh-frequency data610preprint