Finance Discipline Group
UTS Business School
University of Technology, Sydney

Working Paper Series

Title:
The Structure and Degree of Dependence - A Quantile Regression Approach
Author(s): Dirk G. Baur
Date of publication: August 2012
Working paper number: 170
Abstract:
The 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.
Paper: Download (Format: PDF, Size: 1.3 Mb)
Comments: 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.
Known citations:

Ben Rejeb, A. and Arfaoul, M., 2014, "Financial Market Interdependencies: A Quantile Regression Analysis of Volatility Spillover", Working Paper.

Cayon, E. and Thorp, S., 2013. "Financial Autarchy as Contagion Prevention: The Case of Colombian Pension Funds", Research Paper Series 323, Quantitative Finance Research Centre, University of Technology, Sydney.

Hmida, M., 2014, "Financial Contagion Crisis Effect of Subprime on G7: Evidence Through the Adjusted Correlation Test and Non-linear Error Correction Models (ECM)", International Journal of Econometrics and Financial Management, 2(5), 180-187.

Jin, X. and De Simone, F. N., 2014, "Banking Systemic Vulnerabilities: A Tail-Risk Dynamic CIMDO Approach", Journal of Financial Stability, 14(C), 81-101.

Jin, X. and De Simone, F. N., 2014, "Tracking Changes in the Intensity of Financial Sector’s Systemic Risk", Working Paper.

Kuck, K., Maderitsch, R. and Schweikert, K., 2015, "Asymmetric Over- and Undershooting of Major Exchange Rates: Evidence from Quantile Regressions", Economics Letters, 126(C), 114-118.

Lien, D. and Shrestha, K., 2015, "Quantile Estimation of Optimal Hedge Ratio", Journal of Futures Markets, forthcoming.

Luchtenberg, K. and Vu, Q. V., 2015, "The 2008 Financial Crisis: Stock Market Contagion and its Determinants", Research in International Business and Finance, 33(C), 178-203.

Maderitsch, R., 2015, "State-Dependent Dynamics and Interdependence of Global Financial Markets", Thesis, Institut für Volkswirtschaftslehre, Fakultät Wirtschafts- und Sozialwissenschaften, Universitat Hohenheim.

Maderitsch, R., 2015, "Spillovers from the USA to Stock Markets in Asia: A Quantile Regression Approach", Applied Economics, 47(44), 4714-4727.

Mensi, W., Hammoudeh, S., Reboredo, J. C. and Nguyen, D. K., 2014, "Do Global Factors Impact BRICS Stock Markets? A Quantile Regression Approach", Emerging Markets Review, 19(C), 1-17.

Stove, B., Tjostheim, D. and Hufthammer, K. O., 2014, "Using Local Gaussian Correlation in a Nonlinear Re-examination of Financial Contagion", Journal of Empirical Finance, 25(C), 62-82.

Ye, W. Lun, K. and Du, S., 2014, "Measuring Contagion of Subprime Crisis Based on MVMQ-CAViaR Method", Discrete Dynamics in Nature and Society, 2014, 1-12.