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Computational LPPL Fit to Financial Bubbles

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  • Vincenzo Liberatore

Abstract

The log-periodic power law (LPPL) is a model of asset prices during endogenous bubbles. If the on-going development of a bubble is suspected, asset prices can be fit numerically to the LPPL law. The best solutions can then indicate whether a bubble is in progress and, if so, the bubble critical time (i.e., when the bubble is expected to burst). Consequently, the LPPL model is useful only if the data can be fit to the model with algorithms that are accurate and computationally efficient. In this paper, we address primarily the computational efficiency and secondarily the precision of the LPPL non-linear least-square fit. Specifically, we present a parallel Levenberg-Marquardt algorithm (LMA) for LPPL least-square fit that sped up computation of more than a factor of four over a sequential LMA on historical and synthetic price series. Additionally, we isolate a linear sub-structure of the LPPL least-square fit that can be paired with an exact computation of the Jacobian, give new settings for the Levenberg-Marquardt damping factor, and describe a heuristic method to choose initial solutions.

Suggested Citation

  • Vincenzo Liberatore, 2010. "Computational LPPL Fit to Financial Bubbles," Papers 1003.2920, arXiv.org, revised Jan 2011.
    • Handle: RePEc:arx:papers:1003.2920
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    References listed on IDEAS

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    1. Olivier Blanchard & Giovanni Dell’Ariccia & Paolo Mauro, 2010. "Rethinking Macroeconomic Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 199-215, September.
    2. Jiang, Zhi-Qiang & Zhou, Wei-Xing & Sornette, Didier & Woodard, Ryan & Bastiaensen, Ken & Cauwels, Peter, 2010. "Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 149-162, June.
    3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    4. Didier Sornette & Ryan Woodard & Maxim Fedorovsky & Stefan Reimann & Hilary Woodard & Wei-Xing Zhou, 2009. "The Financial Bubble Experiment: advanced diagnostics and forecasts of bubble terminations," Papers 0911.0454, arXiv.org, revised May 2010.
    5. Olivier Blanchard & Giovanni Dell'Ariccia & Paolo Mauro, 2010. "Rethinking Macroeconomic Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 199-215, September.
    6. David S. Bree & Nathan Lael Joseph, 2010. "Testing for financial crashes using the Log Periodic Power Law mode," Papers 1002.1010, arXiv.org, revised Apr 2013.
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    Cited by:

    1. 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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