Study On Monte Carlo Simulation Of Credit Portfolio Risk

Financial institutions risk management of banks not only their own survival and development is critical, and the entire society's economic security and stability, but before the financial tsunami illustrates this point. Commercial banks to build effective risk management model is the enterprise development, enhance core competitiveness of the banking needs, but also the regulatory requirements of regulatory authorities, the New Basel Capital Accord explicitly put forward the internationalization of large commercial banks should gradually establish its own internal model.Credit portfolio is the main commercial bank assets, credit risk management banking risk management is the most important elements. However, due to the special nature of credit risk, credit portfolio risk modeling is very difficult: gains or losses on the credit risk of non-normal distribution, not only difficult to fit with the normal distribution, that is, with other partial distribution can hardly have a better approximation, and ultimately the loss of the distribution may be very strange; credit risk elements of its own complexity, exposure to uncertainty, gains and losses non-linear, dynamic recovery have become very complex problem; tremendous number of credit portfolio, the diversity of mathematics, high-dimensional integral can not be realized; At the same time, there are credit management data is missing, the extreme scarcity of event data. Based on the above factors, the credit portfolio risk modeling using analytical method very difficult to accomplish, can only use the simulation method. Therefore, we construct Monte Carlo simulation technique based on the commercial bank's credit portfolio measurement framework.Construct a Monte Carlo simulation technique based model on the risk management of credit portfolio that is difficult to solve because of non-linear, non-normality, diversity factors. Model can produce a simple breach of contract caused the loss distribution, but also can produce consider the value of the credit migration risk distribution, and calculate the different confidence level on the Value-at-Risk (VaR) and expectations of a shortage of (ES). We use a more flexible and realistic bimodal distribution to sample Recovery. Model using a mixed distribution of the sample, and mobile window technology, also used the importance of sampling, low-difference sequence simulation of modern Monte Carlo technology.The model is extended to the stochastic interest rate and stochastic spreads, and credit risk achieves integration of interest rate risk. Under the framework of the integrated portfolio of assets on the relevance, interest rates, asset number of factors are sensitivity analysis, which concluded on portfolio construction and risk management are of great significance.Construct an integrated framework for stress testing based on the model. Develop new metric indicators for compressive capacity, used to measure the portfolio against extreme risks. Stress tests used to measure can set accident caused by changes in risk factors give financial institutions the potential impact of this on the current financial tsunami economic background of China's banking industry is very meaningful. Mixed distribution of stress tests; set up a variety of situational stress testing framework integration. Stress-testing framework for the use of mixed distribution of stress tests; the use of a Markov switching model mechanism;We proposed a new method to evaluation credit rating system. Credit ratings are the basis of this model, so the reliability of ratings is very important, but the actual level of credit can not be observed, so we have made Monte Carlo simulation based on the ratings of rating methods, give a rating system to assess the four metrics indicators, and verify the validity of the Bootstrap method; Finally, the above method has been applied to the actual model assessment and improvement.