An Lppl Algorithm For Estimating The Critical Time Of A Stock Market Bubble
LPPL models have been widely used to describe the behaviour of stock prices during an endogenous bubble and to predict the most probable time of the regime switching. Although their utility has been proved in many papers, there is still a lack of consensus on the statistical robustness, as the estimators are obtained through a nonlinear optimization algorithm and they are sensitive to the initial values. In this paper we propose an extension of the approach from Liberatore (2011), using a time series peak detection algorithm.
Volume (Year): 1 (2012)
Issue (Month): 2 (DECEMBER)
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- Petr Geraskin & Dean Fantazzini, 2013.
"Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask,"
The European Journal of Finance,
Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
- Fantazzini, Dean & Geraskin, Petr, 2011. "Everything You Always Wanted to Know about Log Periodic Power Laws for Bubble Modelling but Were Afraid to Ask," MPRA Paper 47869, University Library of Munich, Germany.
- Cajueiro, Daniel O. & Tabak, Benjamin M. & Werneck, Filipe K., 2009. "Can we predict crashes? The case of the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1603-1609. Full references (including those not matched with items on IDEAS)
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