Dynamic and static fund separations and their stability for long-term optimal investments

TL;DR

This paper analyzes the stability and convergence of dynamic and static fund separations in long-term optimal investment models with various state processes, providing explicit convergence rates and robustness results.

Contribution

It introduces a comprehensive analysis of fund separation stability across multiple market models, including explicit convergence rates and sensitivity assessments.

Findings

Dynamic portfolios converge to static portfolios over time.

Convergence is stable under model parameter perturbations.

Explicit rates of convergence for portfolios and sensitivities are derived.

Abstract

This paper investigates dynamic and static fund separations and their stability for long-term optimal investments under three model classes. An investor maximizes the expected utility with constant relative risk aversion under an incomplete market consisting of a safe asset, several risky assets, and a single state variable. The state variables in two of the model classes follow a 3/2 process and an inverse Bessel process, respectively. The other market model has the partially observed state variable modeled as an Ornstein-Uhlenbeck state process. We show that the dynamic optimal portfolio of this utility maximization consists of m+3 portfolios: the safe asset, the myopic portfolio, the m time-independent portfolios, and the intertemporal portfolio. Over time, the intertemporal portfolio eventually vanishes, leading the dynamic portfolio to converge to m+2 portfolios, referred to as the static portfolio. We also prove that the convergence is stable under model parameter perturbations. In addition, sensitivities of the intertemporal portfolio with respect to small parameters perturbations also vanish in the long run. The convergence rate for the intertemporal portfolio and its sensitivities are computed explicitly for the presented models.