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An algorithm of generating random number by wavelet denoising method and its application

Author

Listed:
  • Zhou Xiaohui

    (Zhejiang University of Finance and Economics Dongfang College
    Shanghai University of Finance and Economics)

  • Gu Guiding

    (Shanghai University of Finance and Economics)

Abstract

According to the wavelet deniosing method, a new algorithm for generating standard normal random numbers is proposed in this paper. For the standard normal random number generated by randn function, a comparative study is done to discuss the influence of different threshold rules on the mean and variance of random number, the influence of different decomposition levels on random number. Then the correlation among the components of high-dimensional random number is discussed in different space scales. For 1000 groups of normal random number, the distributions of p value of J-B test, mean, variance and correlation are shown by their boxplot. WMC method is presented and applied in numerical integration. For 1000 groups of approximation values computed by WMC method, the mean and variance are given for discussing its accuracy and stability by the boxplot. Finally, an example is given for numerical simulation of financial model.

Suggested Citation

  • Zhou Xiaohui & Gu Guiding, 2022. "An algorithm of generating random number by wavelet denoising method and its application," Computational Statistics, Springer, vol. 37(1), pages 107-124, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01117-z
    DOI: 10.1007/s00180-021-01117-z
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    References listed on IDEAS

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    1. Xiaohui Zhou & Gang Wang & Basil K. Papadopoulos, 2021. "Biorthogonal Wavelet on a Logarithm Curve â„‚," Journal of Mathematics, Hindawi, vol. 2021, pages 1-14, March.
    2. Mark J. Jensen, 1997. "Using Wavelets to Obtain a Consistent Ordinary Least Squares Estimator of the Long Memory Parameter," Econometrics 9710002, University Library of Munich, Germany.
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