This article deals with the actual status quo of measuring credit risk in the German banking sector. It defines the kinds of VaR approaches and discusses the basics and models for quantifying credit risk. The VaR tools used in the German banking sector to measure credit risk are analysed in a next step. Further, the complex character of the Monte Carlo approach is explained at the example of an Excel tool. The outlook of this article consists of a critical analysis of the efficiency in the context of the actual financial crisis in Germany.
The paper extends the basic aspects of three former publications of the author, published in the specialized banking magazine Bankpraktiker 07-08.2006, pp. 366 – 371, the Conference paper for the ESF Conference on 25.06. – 26.06.2008 in Brno, Czech Republic, pp. 325 – 333 and the ControllerMagazin 05.2009, pp. 84 – 92.
Table of Content
List of Figures
List of Abbreviations
1. Introduction
1.1 Reasoning and Motivation
1.2 Structure of the Article
2. Risks in the Banking Sector
2.1 Definition of Risk
2.2 Structuring Risks in the Banking Sector
3. Measuring Risk with the Value at Risk
3.1 Definition of the Value at Risk
3.2 Meaning of the VaR for Risk Management in Banks
3.3 Structuring the Types of VaR Models
4. Modelling Credit Risk
4.1 Determinants for Modelling Credit Risk
4.2 Combining the Input Factors
4.3 Distribution of Credit Risk
5. The Monte Carlo Simulation
5.1 Basic Idea of the Monte Carlo Simulation
5.2 Migration Metrics by Random Scenarios
5.3 Discussing Advantages and Disadvantages of the Monte Carlo Approach
6. Development of a Simplified Monte Carlo Tool at the Example of a Bond Portfolio
6.1 Describing the Model
6.2 Setting up the Excel Sheet
6.3 Programming the Monte Carlo in Excel VBA
6.4 Analysing and Interpreting the Results
7. Monte Carlo Models in the German Banking Sector
7.1 General Overview
7.2 CreditPortfolioView: The Solution of the Savings Bank Sector
8. Final Conclusion and Critical Outlook
Bibliography
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