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Forecasting Financial Crashes: Revisit to Log-Periodic Power Law

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  • Bingcun Dai
  • Fan Zhang
  • Domenico Tarzia
  • Kwangwon Ahn

Abstract

We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.

Suggested Citation

  • Bingcun Dai & Fan Zhang & Domenico Tarzia & Kwangwon Ahn, 2018. "Forecasting Financial Crashes: Revisit to Log-Periodic Power Law," Complexity, Hindawi, vol. 2018, pages 1-12, August.
    • Handle: RePEc:hin:complx:4237471
    DOI: 10.1155/2018/4237471
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    References listed on IDEAS

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    1. Vladimir Filimonov & Didier Sornette, "undated". "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Working Papers ETH-RC-11-002, ETH Zurich, Chair of Systems Design.
    2. Daniel T. Pele, 2012. "An Lppl Algorithm For Estimating The Critical Time Of A Stock Market Bubble," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 1(2), pages 14-22, DECEMBER.
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    Cited by:

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    2. Jang, Hanwool & Song, Yena & Ahn, Kwangwon, 2020. "Can government stabilize the housing market? The evidence from South Korea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    3. Ryu, Inug & Jang, Hanwool & Kim, Dongshin & Ahn, Kwangwon, 2021. "Market Efficiency of US REITs: A Revisit," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).

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