Banks and financial institutions all over the world manage portfolios containing tens of thousands of customers. Not all customers are high credit-worthy, and many possess varying degrees of risk to the Bank or financial institutions that lend money to these customers. Hence assessment of credit risk is paramount in the field of credit risk management. This paper discusses the use of Bayesian principles and simulation-techniques to estimate and calibrate the default probability of credit ratings. The methodology is a two-phase approach where, in the first phase, a posterior density of default rate parameter is estimated based the default history data. In the second phase of the approach, an estimate of true default rate parameter is obtained through simulations

翻译：全世界银行和金融机构管理着由成千上万客户组成的投资组合。并非所有客户都具有高信用价值,而且许多客户对银行或向这些客户借钱的金融机构的风险程度不同。因此,信用风险评估在信用风险管理领域至关重要。本文讨论了使用巴耶斯原则和模拟技术来估计和校准信用评级的默认概率。这种方法是一个两阶段方法,第一阶段,根据默认利率的后密度参数估算出默认历史数据。在第二阶段,通过模拟获得对真实违约率参数的估计。