Description: Risk management and capital budgeting are two critical components of the corporate decision process. They often need to be considered jointly at the corporate level because of their natural interaction through the dependent risk exposures and other synergetic relationships within an intricate corporate structure in a dynamic business environment. . This paper develops an integrated framework that aligns these two important corporate strategies across business divisions to optimize the streamlined enterprise strategic goal in a multi-period setting. The proposed integrated framework can serve as a practical guideline for corporate executives to optimally coordinate capital budgeting and risk management at the enterprise level.

Description: This paper investigates the optimal multi-period proportional reinsurance strategy that minimizes the ruin probability of an insurer. Some conditions under which it is optimal to have no reinsurance are derived. Based on these results, a new concept, capital threshold of proportional reinsurance, is introduced. In the case of two periods, a lower bound of the capital threshold of proportional reinsurance is provided. Some economic implications and properties of this new concept are also discussed.

Description: This paper describes a current approach to capital allocation, tail contribution analysis TCA, and then compares it to an alternate method called economic profit analysis, EPA. The advantages of EPA over TCA are then outlined. From 2006 Enterprise risk Management Symposium, M-AS06-1.

Description: Traditional mean-variance method does not take skewness of the random rate of return into consideration. It only considers minimum variance for a given acceptable rate of return, which limited the probability of obtaining higher rate of return. If the skewness of return for the portfolio is incorporated into model of traditional approach, then the resulting non-linear programing model is not computational efficient, which certainly is complicated. On the other hand, possibilistic programming provides more efficient techniques to solve the imprecision nature of rate of return and also preserves the original linear model.