论文标题
强大的最佳投资和学习的再保险问题
Robust Optimal Investment and Reinsurance Problems with Learning
论文作者
论文摘要
在本文中，我们考虑了可以学习的部分未知模型参数的最佳投资和再保险问题。该模型包括多个业务线路及其之间的依赖性。目的是最大程度地提高终端财富的预期指数效用，这表明意味着一种强大的方法。我们可以使用广义的HJB方程来解决此问题，在该方程中，衍生物被广义的Clarke梯度代替。可以明确确定最佳投资策略，并根据方程解决方案给出最佳的再保险策略。由于难以求解该方程式，因此我们通过比较参数得出了最佳再保险策略的界限。
In this paper we consider an optimal investment and reinsurance problem with partially unknown model parameters which are allowed to be learned. The model includes multiple business lines and dependence between them. The aim is to maximize the expected exponential utility of terminal wealth which is shown to imply a robust approach. We can solve this problem using a generalized HJB equation where derivatives are replaced by generalized Clarke gradients. The optimal investment strategy can be determined explicitly and the optimal reinsurance strategy is given in terms of the solution of an equation. Since this equation is hard to solve, we derive bounds for the optimal reinsurance strategy via comparison arguments.
