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Econometrics 2019, 7(1), 13; https://doi.org/10.3390/econometrics7010013

Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence

1
Department of Finance, Lingnan (University) College, Sun Yat-sen University, 135 Xingang West Road, Guangzhou 510275, China
2
Department of Economics, School of Arts and Sciences, Rutgers University, 75 Hamilton Street, New Brunswick, NJ 08901, USA
*
Author to whom correspondence should be addressed.
Received: 5 September 2018 / Revised: 19 February 2019 / Accepted: 7 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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PDF [417 KB, uploaded 13 March 2019]
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Abstract

Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter in the data generating process, and which are consistent. In this paper, long span jump tests are compared and contrasted with a variety of fixed span jump tests in a series of Monte Carlo experiments. It is found that both the long time span tests of Corradi et al. (2018) and the fixed span tests of Aït-Sahalia and Jacod (2009) exhibit reasonably good finite sample properties, for time spans both short and long. Various other tests suffer from finite sample distortions, both under sequential testing and under long time spans. The latter finding is new, and confirms the “pitfall” discussed in Huang and Tauchen (2005), of using asymptotic approximations associated with finite time span tests in order to study long time spans of data. An empirical analysis is carried out to investigate the implications of these findings, and “time-span robust” tests indicate that the prevalence of jumps is not as universal as might be expected. View Full-Text
Keywords: jump test; jump intensity; sequential testing bias; fixed time span; long time span; high-frequency data jump test; jump intensity; sequential testing bias; fixed time span; long time span; high-frequency data
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Cheng, M.; Swanson, N.R. Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence. Econometrics 2019, 7, 13.

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