We propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are determined by solving an optimization problem which includes a CVaR-constraint. The optimization is carried out by means of a Modified CTGAN algorithm which incorporates features (contextual information) and is used to generate synthetic return scenarios, which, in turn, are fed into the optimization engine. For contextual information we rely on several points along the U.S. Treasury yield curve. The merits of this approach are demonstrated with an example based on ten asset classes (covering stocks, bonds, and commodities) over a fourteen-and-half year period (January 2008-June 2022). We also show that the synthetic generation process is able to capture well the key characteristics of the original data, and the optimization scheme results in portfolios that exhibit satisfactory out-of-sample performance. We also show that this approach outperforms the conventional equal-weights (1/N) asset allocation strategy and other optimization formulations based on historical data only.

翻译：我们提出了一种新的投资组合优化方法，该方法创新性地结合了合成数据生成与条件风险价值约束。我们将投资组合优化问题表述为资产配置问题，其中每类资产均通过被动型（指数）基金进行投资。通过求解包含CVaR约束的优化问题来确定各类资产的权重。该优化过程借助改进的CTGAN算法实现，该算法融合了特征（上下文信息）并用于生成合成收益场景，这些场景进而被输入到优化引擎中。在上下文信息方面，我们依赖美国国债收益率曲线的多个关键点。我们通过一个涵盖十类资产（包括股票、债券和大宗商品）、时间跨度十四年半（2008年1月至2022年6月）的案例验证了该方法的有效性。研究表明，合成数据生成过程能够较好地捕获原始数据的关键特征，并且优化方案所构建的投资组合展现出良好的样本外表现。我们还证明了该方法优于传统的等权重（1/N）资产配置策略以及仅基于历史数据的其他优化方案。