论文标题
在银行默认预测中使用网络交易库传播
Using Network Interbank Contagion in Bank Default Prediction
论文作者
论文摘要
从理论上讲，银行间的传染可能会加剧金融系统中的损失，并导致在经济低迷期间造成额外的级联违约。在本文中，我们使用回归和神经网络模型进行默认分析，以验证银行的传染是否为默认事件提供任何预测性解释性。我们预测，使用前四个季度的数据在2010年第一季度的美国居住的商业银行违约。如果既有意义，则包括许多已建立的预测指标（例如1级资本比率和股本回报率），以衡量衡量。基于这种方法，我们得出的结论是，银行的传染在默认预测中是极其解释的，在回归和神经网络模型中通常都优于更既定的指标。这些发现对未来银行传染作为压力测试，银行发行的债券估值和更广泛的银行违约预测的兴趣变量具有很大的影响。
Interbank contagion can theoretically exacerbate losses in a financial system and lead to additional cascade defaults during downturn. In this paper we produce default analysis using both regression and neural network models to verify whether interbank contagion offers any predictive explanatory power on default events. We predict defaults of U.S. domiciled commercial banks in the first quarter of 2010 using data from the preceding four quarters. A number of established predictors (such as Tier 1 Capital Ratio and Return on Equity) are included alongside contagion to gauge if the latter adds significance. Based on this methodology, we conclude that interbank contagion is extremely explanatory in default prediction, often outperforming more established metrics, in both regression and neural network models. These findings have sizeable implications for the future use of interbank contagion as a variable of interest for stress testing, bank issued bond valuation and wider bank default prediction.
