随机因素模型中稳健前向投资绩效过程的博弈方法

Juan Li, Wenqiang Li, Gechun Liang
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引用次数: 2

摘要

本文研究了不完全市场中具有模型不确定性的最优远期投资问题,其中标的股票的动态取决于相关的随机因素。不确定性源于投资者选择的概率度量来评估业绩。将零和随机微分对策与遍历BSDE方法相结合,直接得到幂鲁棒前向性能过程的因子形式表示。我们还建立了与具有遍历收益准则的无限视界上的风险敏感零和随机微分对策的联系,以及与长期视界的经典功率鲁棒期望效用的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Game Theoretical Approach to Homothetic Robust Forward Investment Performance Processes in Stochastic Factor Models
This paper studies an optimal forward investment problem in an incomplete market with model uncertainty, in which the dynamics of the underlying stocks depends on the correlated stochastic factors. The uncertainty stems from the probability measure chosen by an investor to evaluate the performance. We obtain directly the representation of the power robust forward performance process in factor-form by combining the zero-sum stochastic differential game and ergodic BSDE approach. We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical power robust expected utility for long time horizons.
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