This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of using such testsand 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 .
We conduct an extensive evaluation of price jump tests based on high-frequency financial data. After providing a concise review of multiple alternative tests, we document the size and power of all tests in a range of empirically relevant scenarios. Particular focus is given to the robustness of test performance to the presence of jumps in volatility and microstructure noise, and to the impact of sampling frequency . The paper concludes by providing guidelines for empirical researchers about which test to choose in any given setting .
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This paper provides
We conduct
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high frequency jump tests and measures, in the context of using such testsand 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)
price jump tests based on high-frequency financial data. After providing a concise review of multiple alternative tests, we document the size and power of all tests in a range of empirically relevant scenarios. Particular focus is given to
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inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance
test performance to the presence of jumps in volatility and microstructure noise, and to the impact
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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
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P500 stock index and the IBM stock price
sampling frequency
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who wish to exploit high frequency data when drawing conclusions regarding dynamic jump processes
about which test to choose in any given setting