Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2019, 2(3); doi: 10.25236/AJCIS.020303.

Measuring systemic risk using contingent claims analysis model with higher-moment and machine learing technology

Author(s)

Yurong Zhang1,*, Zhengqi Xu2, Chenghao Xu3, Jialu Xu4

Corresponding Author:
Yurong Zhang
Affiliation(s)

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: yz845@cornell.edu

Abstract

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.

Keywords

Systemic risk contingent claims analysis (CCA); higher-moment; machine learning

Cite This Paper

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.

References

[1] Jyh-JiuanLin, Shih-HengPao, Jyh-HorngLin. The impact of e-finance strategies on depository financial intermediary''s value: an option-based optimization[J]. Journal of Information & Optimization Sciences, 2005, 26(3):509-526.
[2] Jinn-GangLiang, Gow-LiangHuang, Chia-ChuanYeh, et al. Multiple equilibria and the currency crisis: Evidence for Thailand 1997 financial crisis[J]. Journal of Statistics & Management Systems, 2011, 14(3):639-657.
[3] Gong X. Measuring and Analyzing China's Macro-Financial Risk with CCA Approach[J]. Economic Research Journal, 2012.
[4] Shusong B A, Shan J U, Zhu Y. Research on Banking Systemic Risk of Default with Systemic-CCA Method[J]. Journal of Financial Research, 2013.
[5] Aitchison P, Stone M. The impact of e-commerce on UK financial services product providers and their intermediary relationships[J]. Pensions An International Journal, 2002, 8(1):63-78.
[6] Teruo Tanaka. Optimal stochastic sequential control with fractional rewards. Journal of Statistics and Management Systems, 2018, 21(7):1173-1188.
[7] Pranav Dass, Naved Anjum & Divya Gupta. Genetically modified organisms: Reliability analysis and perceptions. Journal of Information and Optimization Sciences, 2018, 39(7):1401-1415.