近年来，随着场外衍生品市场及互联网金融的迅速发展，如何在互联网信息爆炸时代下有效管理信用风险成为金融机构面临的难题之一。此外，随着金融全球一体化的加速，突发事件的发生变得越来越频繁，此时纯扩散模型将不能很好地解释现实金融市场中因突发事件带来的跳跃现象。本项目引入双指数跳跃扩散过程，在随机负债及理论风险下研究信用违约资产定价问题，并进一步扩展到交叉金融市场，构建含汇率风险的信用违约资产定价模型；此外，结合在线投资组合理论，研究信用违约资产配置问题，建立包含场外产品交易的在线投资组合模型，并给出对应的投资策略；最后，结合文本挖掘算法深入分析公告、报表、论坛等非结构化网络文本信息，构建信用风险指标体系，进一步完善信用违约资产定价及配置模型。本项目的研究成果将进一步完善金融资产定价及配置理论体系，丰富信用风险管理方法，为金融机构进行信用风险管理提供理论依据。

In recent years, with the rapid development of the OTC derivatives market and internet finance, the effective management of credit risk has become one of the challenges faced by financial institutions in the era of internet information explosion. Meanwhile, the acceleration of financial globalization resulted in an increasing number of emergencies. Because of those emergencies, the pure diffusion models cannot provide satisfactory explanations regarding the jump phenomena in the financial markets. In this project, we introduce a double exponential jump-diffusion model to investigate the credit default asset pricing problem. We further extend our model to cross-financial markets and build a credit default asset pricing model with exchange rate risk. Furthermore, combining with the on-line portfolio theory, we study the allocation problems of credit default asset, establish an on-line portfolio model which contains OTC products trading and then present the corresponding investment strategies. Finally, we establish a credit risk index system using text mining algorithms to analyze announcements, reports, forums and other network text information, based on which we further improve the credit default asset pricing and allocation models. The research findings from this project will be significant to improve the framework of financial asset pricing and allocation theory, enrich the methods of credit risk management, and provide theoretical foundations for financial institutions to manage their credit risk.