Convergence Rates of Turnpike Theorems for Portfolio Choice in Stochastic Factor Models

TL;DR

This paper investigates how quickly optimal investment strategies in stochastic factor models approach the classical CRRA strategy as the investment horizon extends, focusing on convergence rates influenced by bond prices and utility behavior.

Contribution

It derives explicit convergence rates for optimal feedback functions in nonlinear stochastic factor models and quadratic term structure models, linking them to bond prices and utility properties.

Findings

Convergence rates depend on zero-coupon bond decay and utility power approximation.

Myopic portfolios in nonlinear models converge at quantifiable rates.

Sharing rules influence convergence in collective investment scenarios.

Abstract

Turnpike theorems state that if an investor's utility is asymptotically equivalent to a power utility, then the optimal investment strategy converges to the CRRA strategy as the investment horizon tends to infinity. This paper aims to derive the convergence rates of the turnpike theorem for optimal feedback functions in stochastic factor models. In these models, optimal feedback functions can be decomposed into two terms: myopic portfolios and excess hedging demands. We obtain convergence rates for myopic portfolios in nonlinear stochastic factor models and for excess hedging demands in quadratic term structure models, where the interest rate is a quadratic function of a multivariate Ornstein-Uhlenbeck process. We show that the convergence rates are determined by (i) the decay speed of the price of a zero-coupon bond and (ii) how quickly the investor's utility becomes power-like at high levels of wealth. As an application, we consider optimal collective investment problems and show that sharing rules for terminal wealth affect convergence rates.