Bank credit risk stress testing and Macroeconomic Factors

• Main Authors: SHU-HAO HSU, 許書豪
• Other Authors: Da-Bai Shen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/42709982597596251149

碩士 === 東吳大學 === 會計學系 === 101 === Today's complex financial environment, banks are facing the risk of attention more than ever, stress tests assessing banks' own risk management is an important tool, according to the consortium Joint Credit Information Center in July 2010announced bank stress testing operational planning only with a gross domestic product (GDP), the house price level and the unemployment rate as the overall economic factors, to link corporate revenue, the collateral price and income changes as perform stress tests of reference, However, this approach may have general economic factors link was not strong enough to measure the credit risk concerns, this study hope that through the discussion of general economic factors and credit risk arising relevance, are expected to improve their stress testing framework. The study pre-financial early warning model concept to identify effective to identify a breach of the financial factor and construct a financial early warning model through Panel Logistc model, and then through the exogenous smooth transition autoregressive (Smooth transition autoregressive in exogenous) model will be effective identify the default Company's financial factor to make the link with the overall economic factor, in order to construct the optimal financial factors under the stress test scenarios, financial early warning model to re-calculate the probability of default under stressful situations may finally bring back the financial factor changes, research results show that the overall economic factor in the context of analog pressure projected financial factors and actual financial data to calculate the probability of default difference is not too large, the probability of default and the overall economic factor simulated and actual financial data to calculate the probability of default phase compared to more conservative prediction is similar to the concept, and stress testing; and looking at the overall economic factors point of view, leading economic indicators in the simulation to predict the effect of the strongest, less susceptible to the impact of the number of homes on the data, and the integrity of the the macro-economic indicators are more susceptible to the limit on the number of information on the home, it is recommended to build in addition to considering the overall macroeconomic indicators do stressful situations and considerations leading economic indicators.