This thesis deals with credit risk and selected methods of its evalua- tion. It is focused on assumptions, calculation methods, results and specifics of the CreditMetrics and the CreditRisk+ models. The CreditRisk+ model analytically determines the portfolio credit losses distribution that is caused by defaults of counterparties. In the CreditMetrics model, the credit migration risk is addition- ally considered and the future portfolio value distribution is calculated using the Monte Carlo simulation. The third approach covered in this thesis is the Solvency II, the set of requirements proposed by the European Union for determination of regulatory capital for insurance companies. In the practical part the three ap- proaches are applied on a set of three portfolios of different credit quality. Their results, particularly the determined level of capital required to cover the risk of unexpected credit losses, are analyzed and compared.

This thesis deals with credit risk and selected methods of its evalua- tion. It is focused on assumptions, calculation methods, results and specifics of the CreditMetrics and the CreditRisk+ models. The CreditRisk+ model analytically determines the portfolio credit losses distribution that is caused by defaults of counterparties. In the CreditMetrics model, the credit migration risk is addition- ally considered and the future portfolio value distribution is calculated using the Monte Carlo simulation. The third approach covered in this thesis is the Solvency II, the set of requirements proposed by the European Union for determination of regulatory capital for insurance companies. In the practical part the three ap- proaches are applied on a set of three portfolios of different credit quality. Their results, particularly the determined level of capital required to cover the risk of unexpected credit losses, are analyzed and compared.

The goal of this thesis is to measure the concentration risk of a portfolio as a part of a investment risk considered from the view of insurance companies by various methods and also to compare achieved results. Concentration risk in credit portfolios originates in uneven distribution of invested funds to individual obligors and it is important to manage it. In the theoretical part there are two methods presented - one is being used in practice CreditMetrics), the other one, the EU Directive, will be put into effect in the near future (Solvency II). In the practical part the methods are applied on model portfolios and the results are compared in order to decide how the methods reflect the concentration risk.

Thesis Portfolio Credit Risk Modeling focuses on state-of-the-art credit models largely implemented by banks into their banking risk-assessment and complementary valuation system frameworks. Reader is provided in general with both theoretical and applied (practical) approaches that are giving a clear notion how selected portfolio models perform in real-world environment. Our study comprises CreditMetrics, CreditRisk+ and KMV model. In the first part of the thesis, our intention is to clarify theoretically main features, modeling principles and moreover we also suggest hypotheses about strengths/drawbacks of every scrutinized model. Subsequently, in the applied part we test the models in a lab-environment but with real-world market data. Noticeable stress is also put on model calibration. This enables us to con firm/reject the assumptions we made in the theoretical part. In the very end there follows a straightforward general overview of all outputs and a conclusion.

This thesis deals with credit risk and selected methods of its evalua- tion. It is focused on assumptions, calculation methods, results and specifics of the CreditMetrics and the CreditRisk+ models. The CreditRisk+ model analytically determines the portfolio credit losses distribution that is caused by defaults of counterparties. In the CreditMetrics model, the credit migration risk is addition- ally considered and the future portfolio value distribution is calculated using the Monte Carlo simulation. The third approach covered in this thesis is the Solvency II, the set of requirements proposed by the European Union for determination of regulatory capital for insurance companies. In the practical part the three ap- proaches are applied on a set of three portfolios of different credit quality. Their results, particularly the determined level of capital required to cover the risk of unexpected credit losses, are analyzed and compared.

This thesis is focused on a theoretical explication of the basic methods of the calculation Value at Risk for the market and credit risk. For the market risk there is in detail developed the variance -- covariance method, historical simulation and Monte Carlo simulation, above all for the nonlinear portfolio. For all methods the assumptions of their applications are highlighted and the comparation of these methods is made too. For the credit risk there is made a theoretical description of CreditMetrics, CreditRisk+ and KMV models. Analytical part is concerned in the quantification of Value at Risk on two portfolios, namely nonlinear currency portfolio, which particular assumptions of the variance -- covariance method a Monte Carlo simulation are tested on. Then by these methods the calculation of Value at Risk is realized. The calculation of Credit Value at Risk is made on the portfolio of the US corporate bonds by the help of CreditMetrics model.

在金融海嘯之後, 信用風險的重要性益發為銀行金融業所重視。 為深入探索此議題, 本文以 CreditMetrics(TM) 模型為基底, 設定台灣 458 間上市櫃公司為虛擬資產組合, 做出其資產組合價值分配與資產組合損失分配, 以估量信用風險的大小, 提供銀行業計提資本時一個適當的方向。 在模型上, 本文採納 CreditMetrics(TM) 考量交易對手資產報酬率相關性的優點, 此點使我們交易對手評等的移轉產生相關性, 不致低估信用風險; 並修正其以外部評等機構所提供的無條件移轉矩陣為模型參數的設定, 使用排序普羅比模型 (Ordered Probit Model) 在移轉矩陣上引入總體因子, 搭配 Svensson 四因子模型所估計的放款殖利率, 做出條件情境的的經濟資本, 增加資本計提的準確度。 此外, 為了解總體因子的重要性, 本文將之與評等因子做比較。 實證結果發現, 加入總體因子會對信用風險造成一定程度的衝擊, 銀行業實不宜再以無條件情境做為計提資本的標準。 而在評等與曝險額呈現正相關的條件下, 評等因子的重要性比起總體因子有過之而無不及。 銀行業在計提資本時, 與其費盡心思在模型中納入總體因子, 也許應該先看看評等是否已經納入考量。