This paper presents an asset liability management model based on robust optimization techniques. The model explicitly takes into consideration the time-varying aspect of investment opportunities. The emphasis of the proposed approach is on computational tractability and practical appeal. Computational studies with real market data study the performance of robust-optimization-based strategies, and compare it to the performance of the classical stochastic programming approach.

The paper develops a network-based framework to assess the impact of a firm's financial interconnections and interdependencies on it optimal hedging and risk transfer strategy. This is important consideration as risk concentrations and propagation embedded in risk transfers pose as a significant challenge for financial regulations and risk management. Using an a priori and an a posteriori analysis, we investigate the impact of the network structure on optimal hedge decisions and hedge efficacy. The a priori analysis shows that network features play an important role in determining corporate hedging decisions through their impact on hedge costs. In the a posteriori analysis for counterparty risk, benefit of modification from the a priori optimal hedge decision is realized when variance of firm value matters, but not when tail risk measures are utilized. Financial network assessment indicates that an a priori analysis is insufficient for robust hedging decisions, and choice of risk measures behind risk management objectives dictates the extent of impact of network characteristics.