PORTFOLIOS AND RISK PREMIA FOR THE LONG RUN

• 成果类型: Article
• 署名作者: Guasoni, Paolo; Robertson, Scott
• 署名单位: Boston University; Dublin City University; Carnegie Mellon University
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/11-AAP767
• 发表日期: 2012
• 页码: 239-284
• 关键词: markov process expectations; Optimal investment; asymptotic evaluation; optimal consumption; sensitive control; incomplete markets; large time; martingale; strategies; prices

摘要

This paper develops a method to derive optimal portfolios and risk premia explicitly in a general diffusion model for an investor with power utility and a long horizon. The market has several risky assets and is potentially incomplete. Investment opportunities are driven by, and partially correlated with, state variables which follow an autonomous diffusion. The framework nests models of stochastic interest rates, return predictability, stochastic volatility and correlation risk. In models with several assets and a single state variable, long-run portfolios and risk premia admit explicit formulas up the solution of an ordinary differential equation which characterizes the principal eigenvalue of an elliptic operator. Multiple state variables lead to a quasilinear partial differential equation which is solvable for many models of interest. The paper derives the long-run optimal portfolio and the long-run optimal pricing measures depending on relative risk aversion, as well as their finite-horizon performance.