Tail Dependence Measure for Examining Financial Extreme Co-movements

Asimit, A.V., Gerrard, R. J. G., Yanxi, H. & Peng, L. (2016). Tail Dependence Measure for Examining Financial Extreme Co-movements. Journal of Econometrics, 194(2), pp. 330-348. doi: 10.1016/j.jeconom.2016.05.011

[img] Text - Accepted Version
Restricted to Repository staff only until 8 June 2018.
Available under License : See the attached licence file.

Download (1MB) | Request a copy
[img]
Preview
Text (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence) - Other
Download (201kB) | Preview

Abstract

Modeling and forecasting extreme co-movements in financial market is important for conducting stress test in risk management. Asymptotic independence and asymptotic dependence behave drastically different in modeling such co-movements. For example, the impact of extreme events is usually overestimated whenever asymptotic dependence is wrongly assumed. On the other hand, the impact is seriously underestimated whenever the data is misspecified as asymptotic independent. Therefore, distinguishing between asymptotic independence/dependence scenarios is very informative for any decision-making and especially in risk management. We investigate the properties of the limiting conditional Kendall’s tau which can be used to detect the presence of asymptotic independence/dependence. We also propose nonparametric estimation for this new measure and derive its asymptotic limit. A simulation study shows good performances of the new measure and its combination with the coefficient of tail dependence proposed by Ledford and Tawn (1996, 1997). Finally, applications to financial and insurance data are provided.

Item Type: Article
Additional Information: © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Asymptotic dependence and independence; Copula; Extreme co-movement; Kendall’s tau; Measure of association
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
Related URLs:
URI: http://openaccess.city.ac.uk/id/eprint/13142

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics