论文标题

在随机波动下渐近生长的千古鲁棒性最大化

Ergodic robust maximization of asymptotic growth under stochastic volatility

论文作者

Itkin, David, Koch, Benedikt, Larsson, Martin, Teichmann, Josef

论文摘要

我们考虑在模型不确定性和(非马克维亚)随机协方差的存在下,考虑一个渐近的生长问题。我们修复了代表资产流程瞬时协方差$ x $的两个输入,这取决于额外的随机因子流程$ y $,以及$ x $的不变密度以及$ y $。随机因子过程$ y $具有连续的轨迹,但甚至不需要是半舞会。我们的设置允许$ x $的漂移不确定性以及$ y $的本地动态的模型不确定性。这项工作是基于Kardaras&Robertson最近的一篇论文,在该论文中,作者认为一个类似的问题,但是,没有其他随机因素过程。在合适的,相当弱的假设下,我们能够表征强大的最佳交易策略和强大的最佳增长率。最佳策略被证明是在功能上生成的,并且显着地不取决于因子过程$ y $。我们的结果为Fernholz在2002年提出的问题提供了全面的答案。从数学上讲,我们结合了部分微分方程(PDE),变化的计算和广义的Dirichlet形式技术。

We consider an asymptotic robust growth problem under model uncertainty and in the presence of (non-Markovian) stochastic covariance. We fix two inputs representing the instantaneous covariance for the asset process $X$, which depends on an additional stochastic factor process $Y$, as well as the invariant density of $X$ together with $Y$. The stochastic factor process $Y$ has continuous trajectories but is not even required to be a semimartingale. Our setup allows for drift uncertainty in $X$ and model uncertainty for the local dynamics of $Y$. This work builds upon a recent paper of Kardaras & Robertson, where the authors consider an analogous problem, however, without the additional stochastic factor process. Under suitable, quite weak assumptions we are able to characterize the robust optimal trading strategy and the robust optimal growth rate. The optimal strategy is shown to be functionally generated and, remarkably, does not depend on the factor process $Y$. Our result provides a comprehensive answer to a question proposed by Fernholz in 2002. Mathematically, we use a combination of partial differential equation (PDE), calculus of variations and generalized Dirichlet form techniques.

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