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Detection of changes in a random financial sequence with a stable distribution

Author

Listed:
  • Dong Han
  • Fugee Tsung
  • Yanting Li
  • Jinguo Xian

Abstract

Quick detection of unanticipated changes in a financial sequence is a critical problem for practitioners in the finance industry. Based on refined logarithmic moment estimators for the four parameters of a stable distribution, this article presents a stable-distribution-based multi-CUSUM chart that consists of several CUSUM charts and detects changes in the four parameters in an independent and identically distributed random sequence with the stable distribution. Numerical results of the average run lengths show that the multi-CUSUM chart is superior (robust and quick) on the whole to a single CUSUM chart in detecting the shift change of the four parameters. A real example that monitors changes in IBM's stock returns is used to demonstrate the performance of the proposed method.

Suggested Citation

  • Dong Han & Fugee Tsung & Yanting Li & Jinguo Xian, 2010. "Detection of changes in a random financial sequence with a stable distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1089-1111.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1089-1111
    DOI: 10.1080/02664760902914433
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    Cited by:

    1. Qu, Liang & Wu, Zhang & Khoo, Michael B.C. & Castagliola, Philippe, 2013. "A CUSUM scheme for event monitoring," International Journal of Production Economics, Elsevier, vol. 145(1), pages 268-280.
    2. Josep Lluís Carrion-i-Silvestre & Andreu Sansó, 2023. ""Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series"," IREA Working Papers 202309, University of Barcelona, Research Institute of Applied Economics, revised Jul 2023.

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