The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has r...The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.展开更多
In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk e...In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.展开更多
文摘The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.
基金This research was partially supported by the National Natural Science Foundation of China under Grant Nos.70221001,70701035the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant Nos.3547600,3046540,3047540+1 种基金the Key Research Institute of Philosophies and Social Sciences in Hunan Universitiesthe National Natural Science Foundation of China/Research Grants Council (RGC) of Hong Kong Joint Research Scheme under Grant No.N_CityU110/07.
文摘In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.
