A Monte Carlo Method for Optimal Portfolios
提出一种基于蒙特卡洛模拟的新方法，用于在状态变量动态复杂、因子和资产数量庞大的现实环境中求解最优投资组合。通过多个示例（包括股票与现金选择、股息收益率预测、财富依赖风险厌恶等）验证了方法的有效性，并处理了包含纳斯达克、标普500、债券和现金的大规模问题。
This paper proposes a new simulation‐based approach for optimal portfolio allocation in realistic environments with complex dynamics for the state variables and large numbers of factors and assets. A first illustration involves a choice between equity and cash with nonlinear interest rate and market price of risk dynamics. Intertemporal hedging demands significantly increase the demand for stocks and exhibit low volatility. We then analyze settings where stock returns are also predicted by dividend yields and where investors have wealth‐dependent relative risk aversion. Large‐scale problems with many assets, including the Nasdaq, SP500, bonds, and cash, are also examined.