A Nonparametric Test for Granger-causality in Distribution with Application to Financial Contagion
AbstractThis paper introduces a kernel-based nonparametric inferential procedure to test for Granger-causality in distribution. This test is a multivariate extension of the kernel-based Granger-causality test in tail-event introduced by Hong et al. (2009) and hence shares its main advantage, by checking a large number of lags with higher order lags discounted. Besides, our test is highly exible as it can be used to check for Granger-causality in specific regions on the distribution supports, like the center or the tails. We prove that it converges asymptotically to a standard Gaussian distribution under the null hypothesis and thus it is free of parameter estimation uncertainty. Monte Carlo simulations illustrate the excellent small sample size and power properties of the test. This new test is applied for a set of European stock markets in order to analyse the spill-overs during the recent European crisis and to distinguish contagion from interdependence effect.
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Bibliographic InfoPaper provided by University of Paris West - Nanterre la Défense, EconomiX in its series EconomiX Working Papers with number 2014-18.
Length: 42 pages
Date of creation: 2014
Date of revision:
Granger-causality; Distribution; Tails; Kernel-based test; Financial Spill-over.;
Other versions of this item:
- Bertrand Candelon & Sessi Tokpavi, 2014. "A Nonparametric Test for Grangercausality in Distribution with Application to Financial Contagion," Working Papers 2014-162, Department of Research, Ipag Business School.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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