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Jump detection with wavelets for high-frequency financial time series

Author

Listed:
  • Yi Xue
  • Ramazan Gen�ay
  • Stephen Fagan

Abstract

This paper introduces a new nonparametric test to identify jump arrival times in high frequency financial time series data. The asymptotic distribution of the test is derived. We demonstrate that the test is robust for different specifications of price processes and the presence of the microstructure noise. A Monte Carlo simulation is conducted which shows that the test has good size and power. Further, we examine the multi-scale jump dynamics in US equity markets. The main findings are as follows. First, the jump dynamics of equities are sensitive to data sampling frequency with significant underestimation of jump intensities at lower frequencies. Second, although arrival densities of positive jumps and negative jumps are symmetric across different time scales, the magnitude of jumps is distributed asymmetrically at high frequencies. Third, only 20% of jumps occur in the trading session from 9:30 AM to 4:00 PM, suggesting that illiquidity during after-hours trading is a strong determinant of jumps.

Suggested Citation

  • Yi Xue & Ramazan Gen�ay & Stephen Fagan, 2013. "Jump detection with wavelets for high-frequency financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1427-1444, July.
    • Handle: RePEc:taf:quantf:v:14:y:2013:i:8:p:1427-1444
    DOI: 10.1080/14697688.2013.830320
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    Citations

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    Cited by:

    1. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    2. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    3. Kilponen, Juha & Verona, Fabio, 2016. "Testing the Q theory of investment in the frequency domain," Bank of Finland Research Discussion Papers 32/2016, Bank of Finland.
    4. Michis, Antonis A., 2014. "Investing in gold: Individual asset risk in the long run," Finance Research Letters, Elsevier, vol. 11(4), pages 369-374.
    5. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
    6. repec:zbw:bofrdp:2016_032 is not listed on IDEAS
    7. Gonçalo Faria & Fabio Verona, 2021. "Time-frequency forecast of the equity premium," Quantitative Finance, Taylor & Francis Journals, vol. 21(12), pages 2119-2135, December.
    8. repec:zbw:bofrdp:2020_006 is not listed on IDEAS
    9. Bruzda Joanna, 2015. "Amplitude and phase synchronization of European business cycles: a wavelet approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 625-655, December.
    10. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
    11. Kilponen, Juha & Verona, Fabio, 2016. "Testing the Q theory of investment in the frequency domain," Research Discussion Papers 32/2016, Bank of Finland.
    12. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    13. repec:zbw:bofrdp:2018_007 is not listed on IDEAS

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