Dynamic price jumps: The performance of high frequency tests and measures, and the robustness of inference

Worapree Maneesoonthorn (), Gael Martin () and Catherine Forbes

No 17/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of using such tests and measures in the estimation of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, most notably in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are correctly identified; iii) the accuracy with which the magnitude and sign of a sequence of jumps, including small clusters of consecutive jumps, are estimated; and iv) the robustness of inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance of alternative methods in certain dimensions, with inference being sensitive to these differences in some cases. Accounting for measurement error when using measures constructed from high frequency data to conduct inference on dynamic jump models is also shown to have an impact. The sensitivity of inference to test and measurement construction is documented using both artificially generated data and empirical data on both the S&P500 stock index and the IBM stock price. The paper concludes by providing guidelines for empirical researchers who wish to exploit high frequency data when drawing conclusions regarding dynamic jump processes.

Keywords: price jump tests; nonparametric jump measures; Hawkes process; discretized jump diffusion model; volatility jumps; Bayesian Markov chain Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C12 C22 C58 (search for similar items in EconPapers)
Pages: 47
Date: 2018
New Economics Papers: this item is included in nep-ets and nep-mst
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