In this paper we consider the strategic asset allocation of an insurance company. This task can be seen as a special case of portfolio optimization. In the 1950s, Markowitz proposed to formulate portfolio optimization as a bicriteria optimization problem considering risk and return as objectives. However, recent developments in the field of insurance require four and more objectives to be considered, among them the so-called solvency ratio that stems from the Solvency II directive of the European Union issued in 2009. Moreover, the distance to the current portfolio plays an important role. While literature on portfolio optimization with three objectives is already scarce, applications with four and more objectives have not yet been solved so far by multi-objective approaches based on scalarizations. However, recent algorithmic improvements in the field of exact multi-objective methods allow the incorporation of many objectives and the generation of well-spread representations within few iterations. We describe the implementation of such an algorithm for a strategic asset allocation with four objective functions and demonstrate its usefulness for the practitioner. Our approach is in operative use in a German insurance company. Our partners report a significant improvement in their decision making process since, due to the proper integration of the new objectives, the software proposes portfolios of much better quality than before within short running time.
翻译:在本文中,我们考虑了保险公司的战略资产分配问题。这项任务可以被视为投资组合优化的一个特殊案例。1950年代,Markowitz提议将投资组合优化作为一个双标准优化问题,将风险和回报作为目标。然而,保险领域最近的事态发展需要考虑四个和更多的目标,其中包括欧洲联盟2009年发布的第二号《破产指令》产生的所谓偿付能力比率,此外,与当前投资组合的距离也起着重要作用。虽然关于有三个目标的投资组合优化的文献已经很少,但有四个和更多目标的应用迄今还没有通过基于分级的多目标方法得到解决。然而,由于在精确的多目标方法领域最近进行了算法改进,因此可以纳入许多目标,并在少数重复中生成了广泛的表述。我们描述了战略资产分配算法的实施情况,该算法有四个客观功能,并表明其对从业人员的用处。我们的方法在德国一家保险公司中正在实际使用。我们的合作伙伴报告说,由于新目标的适当整合,因此其决策程序有了重大改进,因为软件组合在质量内运行的时间短得多。