Testing Distributional Assumptions: A L-Moment Approach

Chu, Ba; Salmon, Mark

doi:10.2139/ssrn.1262610

Cited by 1 publication

(1 citation statement)

References 55 publications

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“…They have also found applications in finance for fitting return distributions (Hosking et al, 2000;Carillo et al, 2006-a;Karvanen, 2006) and rate of profit densities (Wells, 2007), for calibrating extreme returns (Gettinby et al, 2006;French, 2008;Tolikas and Gettinby, 2009), to elaborate a GMM-type Goodness-of-Fit test (Cf. Chu and Salmon, 2008), for risk modelling purposes (Cf. da Silva and de Melo Mendes, 2003; Tolikas and Brown, 2006;Martins-Filho and Yao, 2006;Tolikas et al, 2007;Tolikas, 2008;Gouriéroux and Jasiak, 2008;French, 2008), and, finally, for defining a new set of measures of performance for hedge funds (Cf.…”

Section: Introductionmentioning

confidence: 99%

Extreme Volatilities, Financial Crises and L-Moment Estimations of Tail-Indexes

Maillet

Médecin

2010

SSRN Journal

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Following Bali and Weinbaum (2005) and Maillet et al. (2010), we present several estimates of volatilities computed with high-and low-frequency data and complement their results using additional measures of risk and several alternative methods for Tail-index estimation. The aim here is to confirm previous results regarding the slope of the tail of various risk measure distributions, in order to define the high watermarks of market risks. We also produce synthetic general results concerning the method of estimation of the Tail-indexes related to expressions of the L-moments. Based on estimates of Tail-indexes, retrieved from the high frequency 30' sampled CAC40 French stock Index series from the period 1997-2009, using Non-parametric Generalized Hill, Maximum Likelihood and various kinds of L-moment Methods for the estimation of both a Generalized Extreme Value density and a Generalized Pareto Distribution, we confirm that a heavy-tail density specification of the Log-volatility is not necessary.

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