Tail Dependence in the US Banking Sector and Measures of Systemic Risk

Author(s):  
Eliana Balla ◽  
Ibrahim Ergen ◽  
Marco Migueis
2021 ◽  
Vol 16 (TNEA) ◽  
pp. 1-23
Author(s):  
Christian Bucio Pacheco ◽  
Luis Villanueva ◽  
Raúl de Jesús Gutiérrez

The objective of this work is to estimate the patterns of dependence between the yields of the stock prices of the main banks of the United States (US) and Mexico. We estimate the patterns of absolute dependence and tail dependence through copulas of the Archimedean family and the use of rolling windows of 245 days. The data employed come from the daily share prices at closing from January 2, 2015, to December 31, 2020, for seven banks. Our results show that: i) there are patterns of high dependence among the main banks in the US, ii) there are patterns of very low dependence among the main banks in the US and Mexico, and iii) there are patterns of low dependence among the main banks in Mexico. These results have several implications, among them that the high-dependency patterns obtained among major US banks limit the joint selection of these US bank equity assets in an investment portfolio. Although this paper focuses on a small sample of banks, they represent an important portion of the banking sector in both countries. Given the limited literature on this subject in Mexico, our paper contributes to expanding this literature with a novel approach.


Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2055
Author(s):  
Faisal Alqahtani ◽  
Nader Trabelsi ◽  
Nahla Samargandi ◽  
Syed Jawad Hussain Shahzad

This study investigates the structure of the tail dependence between the United States (US) and Gulf Cooperation Council (GCC) banking sectors for the period February 2010 to July 2017. Conditional value at risk and conditional diversification benefits are calculated. The GCC banking sectors show lower tail dependence with the US banking sector. This is confirmed by the fact that GCC banking sectors receive higher downside risk spillover from the US banking system during downside market movements compared to upside risk spillover effects. Interestingly, an equally weighted portfolio of US and GCC banking stocks can provide relatively higher diversification benefits. These findings have implications for portfolio diversification, asset allocation and hedging strategies.


2016 ◽  
Vol 76 (4) ◽  
pp. 512-531 ◽  
Author(s):  
Xiaoguang Feng ◽  
Dermot Hayes

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.


2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Zhi-Qiang Jiang ◽  
Boris Podobnik ◽  
Wei-Xing Zhou ◽  
...  

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