Testing for Asymmetric Dependence
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Hans Manner
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 are shown to have higher power than competing tests proposed in the literature. 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 U.S. GNP and unemployment data and we find evidence of asymmetries and nonlinearities.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model
- Testing for Asymmetric Dependence
- Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk
- Estimating the Term Premium by a Markov Switching Model with ARMA-GARCH Errors
- Synchronization and On-Off Intermittency Phenomena in a Market Model with Complementary Goods and Adaptive Expectations
Artikel in diesem Heft
- Article
- Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model
- Testing for Asymmetric Dependence
- Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk
- Estimating the Term Premium by a Markov Switching Model with ARMA-GARCH Errors
- Synchronization and On-Off Intermittency Phenomena in a Market Model with Complementary Goods and Adaptive Expectations
