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Inference of Jumps Using Wavelet Variance

Author

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  • Heng Chen

    (Currency Department, The Bank of Canada)

  • Mototsugu Shintani

    (Faculty of Economics, The University of Tokyo)

Abstract

We consider the statistical inference of jumps in nonparametric regression models with long memory noise. A test statistic is proposed for the presence of jumps based on a robust estimator of the variance of the wavelet coefficients. The sequential applications of tests allow us to estimate the number of jumps and their locations. In comparison with the existing inference procedure, in which test statistic converges very slowly to the extreme value distribution, ours processes a more accurate finite sample performance derived from the asymptotic normality of our test statistic.

Suggested Citation

  • Heng Chen & Mototsugu Shintani, 2021. "Inference of Jumps Using Wavelet Variance," CARF F-Series CARF-F-527, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf527
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