Testing for pure-jump processes for high-frequency data
By Xinbing Kong, Zhi Liu, Bing-Yi Jing. 2015
54 citations recorded by the source corpus.
Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the underlying process of high-frequency data can be modeled by a pure-jump process. The new test is based on the realized characteristic function, and enjoys a much faster convergence rate of order $O(n^{1/2})$ (where $n$ is the sample size) versus the usual $o(n^{1/4})$ available for existing tests; it is applicable much more generally than previous tests; for example, it is robust to jumps of infinite variation and flexible modeling of the diffusion component. Simulation studies justify our findings and the test is also applied to some real high-frequency financial data.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/04687caabacd5a4b380ae3b3a8ef016d3a9cae49
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Citations in the corpus
- On the number of common factors with high‐frequency data (2017, result): “These choices of parameter values are similar to those in Jacod & Todorov (2014) and Kong et al. (2015).”