Credit risk management is becoming more and more important in recent years. Credit risk refers to the risk that an obligor fails to make payments on any type of debt at the time of maturity. Credit risk models are statistical tools to infer the future default probabilities and loss distribution of values of a portfolio of debts. This doctoral thesis focus on the application of credit risk management in different areas. To better understand the credit risk management, in the first chapter, we introduce the basic ideas in credit risk management and review the models developed in the last decades. To empirical test the performance of models reviewed in the first chapter, in the second chapter, we compare the reduce-form model with the structural model based on the China’s stock market. It turns out that both models contribute to explaining the default risk of listed firms, however, reduce-form model outperformances the structural model. The empirical results from the second chapter suggests that reduce-form model can better predict the firm’s default risk, but the correlated default risk between firms has not been answered yet. So therefore in the third chapter, we investigate the correlated default risk using copula theory which has been introduced in the first chapter. Based on the insurances firms and other financial firms in the US market, both short-term and long-term default dynamic correlations are found. Another interesting finding from the third chapter is that insurance firms which were considered to be stable actually have higher default risk. This motive us to further explore the determinants of default risk of insurance firms in the fourth chapter and new risk factors (macroeconomic and insurance-specific variables) are found.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:705614 |
| Date | January 2017 |
| Creators | Zhang, Xuan |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/7988/ |
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