Risk analysis and computational finance

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The modern financial industry-comprising the banking, investment and insurance sectors - relies heavily on modern, computer-based risk analysis and management. As the array of financial products grows in variety and complexity, accurate and reliable risk management has become both more complex and more essential. Several tragic failures in recent years-such as the rapid demise of Enron, Barings Bank, Long Term Capital, Equitable Life in UK, and more recently the subprime crisis - have caused millions of investors to lose their money, and have provided ample evidence of the consequences of inappropriate risk management. Reliable risk mitigating and risk managing of the enterprise become a critical issue for the viability of a financial institution. It is this important area where Prof. Tan will have a significant contribution. Prof. Tan's research will focus on three key areas. The first objective of Prof. Tan's research program is devoted to developing robust and theoretical sound model for optimally using reinsurance to manage the risks of an insurance company. The second objective of Prof. Tan's research program is to analyze and assess the riskiness of an insurance business by analyzing the evolution of the surplus and its interplay between the premium, claims and interest. By incorporating many empirically observed characteristics of the claim experience, such as correlation and the possibility of large claims, the interest factor and delayed factor, Prof. Tan derives some theoretical results on the likelihood that an insurance business's surplus will ever fall below zero. This provides important information on the viability of insurance business and calls for appropriate risk mitigation measures if this probability is large. While the first two objectives focus mostly on the theoretical analysis, Prof. Tan also recognizes that many of the problems encountered in practice are often not tractable. Hence the last objective of Prof. Tan's research program focuses on the state-of-the-art numerical solution for complex models. The enhanced simulation technique has the potential of translating into a significant savings, both in time and in computing effort. This greatly complements the analytical results established in the first two objectives.