On the power of generalized extreme value (GEV) and generalized Pareto distribution (GPD) estimators for empirical distributions of stock returns
AbstractUsing synthetic tests performed on time series with time dependence in the volatility with both Pareto and Stretched-Exponential distributions, it is shown that for samples of moderate sizes the standard generalized extreme value (GEV) estimator is quite inefficient due to the possibly slow convergence toward the asymptotic theoretical distribution and the existence of biases in the presence of dependence between data. Thus, it cannot distinguish reliably between rapidly and regularly varying classes of distributions. The Generalized Pareto distribution (GPD) estimator works better, but still lacks power in the presence of strong dependence. Applied to 100 years of daily returns of the Dow Jones Industrial Average and over one years of five-minutes returns of the Nasdaq Composite index, the GEV and GDP estimators are found insufficient to prove that the distributions of empirical returns of financial time series are regularly varying, because the rapidly varying exponential or stretched exponential distributions are equally acceptable.
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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics.
Volume (Year): 16 (2006)
Issue (Month): 3 ()
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- V. F. Pisarenko & D. Sornette, 2004. "New statistic for financial return distributions: power-law or exponential?," Papers physics/0403075, arXiv.org.
- Fernandes, José L. B. & Hasman, Augusto & Peña Sánchez de Rivera, Juan Ignacio, 2008.
"Risk premium: insights over the threshold,"
Open Access publications from Universidad Carlos III de Madrid
info:hdl:10016/7070, Universidad Carlos III de Madrid.
- José L. B. Fernandes & Augusto Hasman & Juan Ignacio Peña, 2006. "Risk Premium: Insights Over The Threshold," Working Papers Series 126, Central Bank of Brazil, Research Department.
- Jose L. B. Fernandes & Augusto Hasman & Juan Ignacio Peña, 2006. "Risk Premium: Insights Over The Threshold," Business Economics Working Papers wb062808, Universidad Carlos III, Departamento de Economía de la Empresa.
- Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical distributions of Chinese stock returns at different microscopic timescales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 495-502.
- Bertrand B. Maillet & Jean-Philippe R. Médecin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
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