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.
|Series||METEOR Research Memorandum|
- c12 - Hypothesis Testing: General
- c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
- asymmetric dependence
- exceedance correlation
- Kendall's tau
- Copula Markov models