Optimal Investment with Insider Information Using Skorokhod & Russo-Vallois Integration
研究了知情交易者使用不同随机积分技术（Russo-Vallois前向积分与Skorokhod积分）最大化对数效用的效果，发现Skorokhod积分策略优于前向积分，但普通交易者在特定条件下可能超越两者。
Abstract We study the maximization of the logarithmic utility for an insider with different anticipating techniques. Our aim is to compare the utilization of Russo-Vallois forward integral and Skorokhod integral in this context. Theoretical analysis and illustrative numerical examples showcase that the Skorokhod insider outperforms the forward insider. This remarkable observation stands in contrast to the scenario involving risk-neutral traders. Furthermore, an ordinary trader could surpass both insiders if a significant negative fluctuation in the driving stochastic process leads to a sufficiently negative final value. These findings underline the intricate interplay between anticipating stochastic calculus and nonlinear utilities, which may yield non-intuitive results from the financial viewpoint.