A simple nonparametric test for structural change in joint tail probabilities

Krämer, Walter; Kampen, Maarten van

doi:10.1016/j.econlet.2010.12.008

Cited by 14 publications

(5 citation statements)

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“…These papers complement other approaches for related measures of dependence, e.g. for the whole covariance matrix (Aue et al, 2009, Galeano andPeña, 2007), the copula (Na et al, 2012, Krämer andvan Kampen, 2011), Spearman's rho (Gaißler and Schmid, 2010), Kendall's tau (Dehling et al, 2014), autocovariances in a linear process (Lee et al, 2003) and covariance operators in the context of functional data analysis (Fremdt et al, 2012).…”

Section: Introductionmentioning

confidence: 65%

A Nonparametric Test for a Constant Correlation Matrix

Wied

2013

SSRN Journal

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We propose a nonparametric procedure to test for changes in correlation matrices at an unknown point in time. The new test requires constant expectations and variances, but only mild assumptions on the serial dependence structure and has considerable power in finite samples. We derive the asymptotic distribution under the null hypothesis of no change as well as local power results and apply the test to stock returns.

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Section: Introductionmentioning

confidence: 65%

A Nonparametric Test for a Constant Correlation Matrix

Wied

2013

SSRN Journal

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“…By using the copula-based expression for Spearman's rho from Schmid and Schmidt (2007) or Nelsen (2006), we get quite another contribution with our test, i.e. an extension of the copula constancy tests proposed by Busetti and Harvey (2011) and Krämer and van Kampen (2011). Since copula models are frequently used in financial econometrics (see e.g.…”

Section: Introductionmentioning

confidence: 91%

A fluctuation test for constant Spearman's rho with nuisance-free limit distribution

Wied,

Dehling,

van Kampen

et al. 2012

Preprint

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A CUSUM type test for constant correlation that goes beyond a previously suggested correlation constancy test by considering Spearman's rho in arbitrary dimensions is proposed. Since the new test does not require the existence of any moments, the applicability on usually heavy-tailed financial data is greatly improved. The asymptotic null distribution is calculated using an invariance principle for the sequential empirical copula process. The limit distribution is free of nuisance parameters and critical values can be obtained without bootstrap techniques.A local power result and an analysis of the behavior of the test in small samples is provided.

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“…Important issues arising from this problem include detecting the change points, testing the number of changes and measuring the magnitude of changes. For surveys we refer to Csörgö and Horváth (1997), Leybourne and Taylor (2004), Perron (2006), Kramer and Kampen (2011) and Chen and Tian (2014). Recently there has been a growing literature on the estimation and tests of common breaks in panel data models in which there are N individual units and T time series observations for each individual.…”

Section: Introductionmentioning

confidence: 98%