The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

L. Gazola, C. Fernandes, A. Pizzinga and R. Riera ()

The European Physical Journal B: Condensed Matter and Complex Systems, 2008, vol. 61, issue 3, 355-362

Abstract: This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2008

Keywords: 89.65.Gh Economics; econophysics, financial markets, business and management, 02.50.Tt Inference methods, 05.10.-a Computational methods in statistical physics and nonlinear dynamics, (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:eurphb:v:61:y:2008:i:3:p:355-362

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DOI: 10.1140/epjb/e2008-00085-1

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