A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model

By Vladimir Filimonov, Didier Sornette. 2011

151 citations recorded by the source corpus.

We present a simple transformation of the formulation of the log-periodic power law formula of the Johansen–Ledoit–Sornette (JLS) model of financial bubbles that reduces it to a function of only three nonlinear parameters. The transformation significantly decreases the complexity of the fitting procedure and improves its stability tremendously because the modified cost function is now characterized by good smooth properties with in general a single minimum in the case where the model is appropriate to the empirical data. We complement the approach with an additional subordination procedure that slaves two of the nonlinear parameters to the most crucial nonlinear parameter, the critical time tc, defined in the JLS model as the end of the bubble and the most probable time for a crash to occur. This further decreases the complexity of the search and provides an intuitive representation of the results of the calibration. With our proposed methodology, metaheuristic searches are not longer necessary and one can resort solely to rigorous controlled local search algorithms, leading to a dramatic increase in efficiency. Empirical tests on the Shanghai Composite index (SSE) from January 2007 to March 2008 illustrate our findings.

Subfields: Computer Networks and Communications, Condensed Matter Physics, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/6134e99031d418d1d1b181005455fed3f97af86f

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In Computer Networks and Communications

  1. Didier Sornette
  2. Wanfeng Yan
  3. Ryan Woodard
  4. Wei‐Xing Zhou

In Condensed Matter Physics

  1. Wei‐Xing Zhou
  2. Ryan Woodard
  3. Wanfeng Yan
  4. Didier Sornette

In Economics and Econometrics

  1. Didier Sornette
  2. Wanfeng Yan
  3. Wei‐Xing Zhou
  4. Ryan Woodard
  5. Ling-Yun He
  6. Shu-Peng Chen
  7. Guangxi Cao
  8. Ladislav Kristoufek

Citations in the corpus