Bayesian Methods With Application To Credit Risk Analysis

Credit risk expand all over the financial business,default riks of loan is the focus of credit risk analysis in financial institution,especially in bank.Loan portfolio is the method that bank lend the money to two or more debtors to diversify credit risk under the constraint of total loans. As the effects of macroeconomic factors such as industry characteristics and the business cycle, as well as micro-factors such as related commercial activity between enterprises,default dependence of loan portfolio exhibit in cyclical correlation and default contagion.The higher the dependency of default,the the greater the potential risk of loan portfolio loss.How to fully accurate reflect default dependence in loan portfolio credit risk measurement,is the the focal point of current academic research and practical applications.China is gradually implementing the new Basel Accord,extending the study of risk measurement techniques from the perspective of single debtor to the loan portfolio,can get a more accurate calculation of the accord request risk capital.At the same time,although the new accord improve many credit risk measurement model,but these models are not suitable for the macro level to assess the credit risk of the whole economy,which is the main problems that banking supervision departments will be faced in the evaluation of the stability of the whole banking system.From the micro and macro perspective to study the default probability of loan portfolio and the distribution of credit losses,will not only help banks to diversify the risk and improve the credit risk measurement techniques,but also have important reality significance for regulatory agencies to assess the stability of the financial environment and reinforce risk management.Loan portfolio credit risk measurement is significantly characterized by lack of empirical default data.Bayesian statistical methods can be used to study the uncertainty associated with parameters in the probability model,is a research technique that can use subjective experience such as expert opinion scientifically and effectively.The applications of Bayeisan methods in loan portfolio credit risk measurement are mainly reflected in two ways.The first,Bayesian methods can be used to parameterize models,which are technical support tools to estimate the probability of default and other key variables in the process of risk management decision analysis.The second,Bayesian methods can be used to estimate the distribution of credit losses, the Bayesian method can effectively describe dynamic changes of the loan portfolio credit losses, and divide loss distribution into observation variable,and diagnosis the fluctuation rate of of loss.In the loan portfolio credit risk measurement study,including the two main conclusions,one is based on Bayesian methods to build the credit risk measurement framework,combined with the application of MCMC simulation techniques,can alleviate the problem of lack of empirical default data to a certain extent.And the other is through neatly use of of the latent factors in Bayesian model,can accurately reflect the default dependence in loan portfolio,and can give the dynamic describe of credit loss distribution of the whole economy.The major innovations are in the following areas:Firstly,expand the application of the latent factors,especially in the loan portfolio credit risk measurement.The impact of factors such as the quality of the individual debtor,industry characteristics and the business cycle can all be described through the latent factors.And then use hierarchical prior distribution to build multi-level model deal with default dependency and heterogeneity of debtor,thereby not only made the results more accurate to avoid underestimating the risks,but also have more concise definition of statistical inference.Secondly,construct Bayesian model from the micro perspective of commercial bank to measure default probability of loan portfolio,which not only cover the macroeconomic impact, given the heterogeneity of the debtor,and allow the existence of cross-phase-related of changes in credit quality,so that have a more precise description of credit rating changes in the loan process,can explain the risk of macro-system influence on the probability of default.And for different data constraints,the model is derived the specific forms.At the same time improve the sample testing methods in Baselâ…¡,not only use Divance Information Criterion to validate model, and strengthen the capacity of models to predict,but also through improved out-of sample model comparison methods to further explain the uncertainty of conclusions.Thirdly,construct the framwork of Bayesian assessment of loss distribution from the macro perspective of regulatory institutions.Propose the approach of dynamic parameterizing of the loss distribution,and decompose the loss distribution into observed variables,further explain the contagion of default,and give the diagnostic methods of loss volatility.In addition to the mass calculation in the credit risk measurement model based on Bayesian methods,there still have some deviations between prediction and the actual probability of default, at the same time the robustness of the model need for further testing,which are to be studied in-depth.