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Financial statements based bank risk aggregation

Published on Apr 1, 2018in Review of Quantitative Finance and Accounting
· DOI :10.1007/s11156-017-0642-0
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Lu Wei2
Estimated H-index: 2
(CAS: Chinese Academy of Sciences)
+ 2 AuthorsDengsheng Wu10
Estimated H-index: 10
(CAS: Chinese Academy of Sciences)
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Abstract
Abstract One of the major challenges involved in risk aggregation is the lack of risk data. Recently, researchers have found that mapping financial statements into risk types is a satisfactory way to resolve the problem of data shortage and inconsistency. Nevertheless, ignoring off-balance sheet (OBS) items has so far been regarded as the usual practice in risk aggregation, which may lead to deviations in conclusions. Hence, we improve the financial statements based risk aggregation framework by mapping OBS items into risk types. Based on 487 quarterly financial statements from all 16 listed Chinese commercial banks over the period 2007–2014, we empirically study whether the overall impact of OBS activities and the individual impact of each of the OBS risk types on total risk depend on bank size. Moreover, this research divides the sample into two subsets, during and after the subprime crisis, to find out how the subprime crisis affects risks of Chinese banks. Our empirical results show that although OBS credit risk is positively linked to total risk while OBS operational risk is negatively linked to total risk for both large and small banks, the overall impact of OBS activities on total risk depends on bank size. The overall OBS activities are positively related to the large bank’s total risk while they are negatively related to the small bank’s total risk. Besides, we also found that it is the increase of liquidity risk and market risk that leads to the larger total risk of Chinese banks during the subprime crisis.
  • References (44)
  • Citations (3)
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References44
Newest
#1Xiaoqian Zhu (CAS: Chinese Academy of Sciences)H-Index: 7
#2Yongjia Xie (CAS: Chinese Academy of Sciences)H-Index: 4
Last.Dengsheng Wu (CAS: Chinese Academy of Sciences)H-Index: 10
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#1Xiaohui Hou (Xi'an Jiaotong University)H-Index: 2
#2Qing Wang ('PBOC': People's Bank of China)H-Index: 2
Last.Cheng Li (Xi'an Jiaotong University)H-Index: 2
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#1Gandjar Mustika (Bank Indonesia)H-Index: 1
#2Enny Suryatinc (Bank Indonesia)H-Index: 1
Last.Richard Simper (University of Nottingham)H-Index: 14
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#1Jianping LiXiaolei (CAS: Chinese Academy of Sciences)H-Index: 19
#2Xiaoqian Zhu (CAS: Chinese Academy of Sciences)H-Index: 7
Last.Yong Shi (CAS: Chinese Academy of Sciences)H-Index: 39
view all 6 authors...
#1José G. Dias (ISCTE-IUL: ISCTE – University Institute of Lisbon)H-Index: 13
#2Sofia Ramos (ISCTE-IUL: ISCTE – University Institute of Lisbon)H-Index: 13
#1Dilruba Karim (Brunel University London)H-Index: 8
#2Iana Liadze (National Institute of Economic and Social Research)H-Index: 9
Last.E. Philip Davis (National Institute of Economic and Social Research)H-Index: 24
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Cited By3
Newest
#1Lu Wei (CAS: Chinese Academy of Sciences)H-Index: 2
#2Guowen Li (CAS: Chinese Academy of Sciences)H-Index: 2
Last.Xiaoqian Zhu (CAS: Chinese Academy of Sciences)H-Index: 7
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#1Lu Wei (CAS: Chinese Academy of Sciences)H-Index: 2
#2Guowen Li (CAS: Chinese Academy of Sciences)H-Index: 2
Last.Jianping LiXiaolei (CAS: Chinese Academy of Sciences)H-Index: 19
view all 4 authors...
#1Yinghui Wang (CAS: Chinese Academy of Sciences)H-Index: 1
#2Guowen Li (CAS: Chinese Academy of Sciences)H-Index: 2
Last.Xiaoqian Zhu (CAS: Chinese Academy of Sciences)H-Index: 7
view all 4 authors...
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