摘要
在银行的风险管理中,操作风险一直是管理难点,具有涉及面广、管理半径长、不易识别、不易控制、不易量化等特点,且在管理工具和量化方法上都没有信用风险、市场风险那么成熟。近年来,银行业因操作风险导致的巨大损失却日益增多。本文利用贝叶斯网络进行建模,量化研究关键风险指标与关键风险诱因的因果关系,正向分析各种操作风险诱因的影响程度,反向分析操作风险指标出现预警后,风险诱因的后验概率如何变化,以期不断优化模型,以更好地管理操作风险。此外,本文还初步探讨了关键风险指标阈值设置方法和控制成本与损失成本的关系。同时,结合银行操作风险管理工作的实践,提出了防范操作风险的建议。
In the risk management of banks, operational risk has always been the difficult part, with features includinglong management radius, which is difficult to identify, control, quantify and so on.In addition, the management of operational risk is not so mature as credit risk or market risk in terms of the management tools and quantitative methods. However, the huge losses caused by operational risk in the banking industry are increasingly prominent in recent years. This paper develops a Bayesian network model to study the causality relationship between the key risk index and the key risk factors quantitatively. The forward analysis is conducted on the influence degree of various operational risk factors. The reverse analysis is conducted on how the posterior probability changes of risk factors when there is index warning for operational risk, so as to continuously optimize the model to better manage operational risk. Furthermore, the relationship between threshold value setting method of key risk index and cost control loss is discussed. This paper proposes suggestions on the management of operational risk according to the practice of risk management in the banks. It is hoped that this paper can provide a guide to enhance the operational risk management in the banking industry.
出处
《金融监管研究》
2017年第8期18-38,共21页
Financial Regulation Research
关键词
新资本协议
商业银行
操作风险
贝叶斯网络模型
the New Basel Capital Accord
Commercial Bank
Operational Risk
Bayesian Network Model
