The Gumbel test and jumps in the volatility process

Christian Palmes () and Jeannette H. C. Woerner ()
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Christian Palmes: Technische Universität Dortmund
Jeannette H. C. Woerner: Technische Universität Dortmund

Statistical Inference for Stochastic Processes, 2016, vol. 19, issue 2, No 6, 235-258

Abstract: Abstract In 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. This maximal jump size depends on the relative sampling frequencies involved in the definition of the test statistics. Furthermore, we show that the given bound on the jump size is sharp and investigate the details of the phase transition at this critical bound.

Keywords: Jump test; Stochastic volatility model; Volatility process with jumps; Gumbel distribution; Extreme value theory; High-frequency data; 62G10; 62P05; 62G32 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11203-015-9127-8

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