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Testing for Asymmetric Dependence

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  • Manner, H.

    (Quantitative Economics)

Abstract

We study how to measure and test for differences in dependence for small and large realizations of two variables of interest. We introduce a conditional version of Kendall's tau and provide formulas to evaluate it for any copula of interest. Two tests based on well known copulas are proposed to test the null hypothesis of symmetric dependence and these tests outperform the one proposed by Hong et al. (2007) in a Monte Carlo study. Additionally, we suggest three examples of data generating processes that can lead to asymmetric dependence and study these both analytically and in a Monte Carlo framework. Finally, we illustrate the use of our tests on stock market returns and on quarterly US GNP and Unemployment data and we find evidence of asymmetries and nonlinearities.

Suggested Citation

  • Manner, H., 2008. "Testing for Asymmetric Dependence," Research Memorandum 042, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    • Handle: RePEc:unm:umamet:2008042
    DOI: 10.26481/umamet.2008042
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    References listed on IDEAS

    as
    1. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
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    3. Hans Manner & Bertrand Candelon, 2010. "Testing For Asset Market Linkages: A New Approach Based On Time‐Varying Copulas," Pacific Economic Review, Wiley Blackwell, vol. 15(3), pages 364-384, August.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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