The Structure and Degree of Dependence - A Quantile Regression Approach
AbstractThe copula function defines the degree of dependence and the structure of dependence. This paper proposes an alternative framework to decompose the dependence using quantile regression. It is demonstrated that the methodology provides a detailed picture of dependence including asymmetric and non-linear relationships. In addition, changes in the degree or structure of dependence can be modelled and tested for each quantile of the distribution. The empirical part applies the framework to three different sets of financial time-series and demonstrates substantial differences in dependence patterns among asset classes and through time. The analysis of 54 global equity markets shows that detailed information about the structure of dependence is crucial to adequately assess the benefits of diversification in normal times and crisis times.
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Bibliographic InfoPaper provided by Finance Discipline Group, UTS Business School, University of Technology, Sydney in its series Working Paper Series with number 170.
Date of creation: 01 Aug 2012
Date of revision:
Publication status: Published as: Baur, D. G., 2013, "The Structure and Degree of Dependence - A Quantile Regression Approach", Journal of Banking and Finance, 37(3), 786-798.
quantile regression; copula; dependence modelling; tail dependence; contagion; financial crises;
Other versions of this item:
- Baur, Dirk G., 2013. "The structure and degree of dependence: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 786-798.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies
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