Academic Journal of Computing & Information Science, 2019, 2(3); doi: 10.25236/AJCIS.020303.
Yurong Zhang1,*, Zhengqi Xu2, Chenghao Xu3, Jialu Xu4
1. SC Johnson College of Business, Cornell University, Ithaca, New York, USA
2. St. George’s Senior School, Vancouver, Canada
3. Hangzhou Foreign Languages School, Cambridge-A Level Center, Hangzhou, China
4. Watkinson School, Hartford, Connecticut, USA
*Corresponding author Email: [email protected]
Accompanied by financial risk all time, financial market has made great progress. It is necessary to measure the change and character of financial risk and try to avoid the risk in terms of theory and practice. Based on the contingent claims analysis (CCA) framework, this paper tries to use higher-moment transformation to quantify the risk of financial distress better. The average distance to default under HCCA model of the Chinese banks fluctuated at low-level in recent years, the same goes for traditional average distance to default. And, more remarkable, the systematic distance to default under HCCA model and original normal distribution random model fluctuated at low-level too. These are signs that the systemic risks for the listed banks in China take on an increasing trend at the high level. HCCA model can improve the accuracy and effectiveness of financial policies, and strengthen the forward-looking prediction of policy measures.
Systemic risk contingent claims analysis (CCA); higher-moment; machine learning
Yurong Zhang, Zhengqi Xu, Chenghao Xu, Jialu Xu. Measuring systemic risk using contingent claims analysis model with higher-moment and machine learing technology. Academic Journal of Computing & Information Science (2019), Vol. 2, Issue 3: 16-21. https://doi.org/10.25236/AJCIS.020303.
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