简单的股票/债券/黄金动态投资组合
Simple Dynamic Stock/Bond/Gold Portfolios
AI总结：
本文利用公开数据和量化方法，通过简单波动率控制及凸优化动态组合，在2006-2026年间显著提升股票/债券/黄金固定权重基准的风险调整回报和回撤指标。
AI中文摘要：
四十多年来，60/40股票/债券投资组合一直作为在不过度风险下提供合理回报的基准。最近，有人提出了50/30/20股票/债券/另类资产投资组合。我们使用黄金作为另类资产和通胀对冲工具。在本文中，我们提出一个问题：利用广泛可得的公开数据和量化金融的标准方法，相对于这些基准固定权重投资组合，可以获得多大的改进？我们将自己限制在仅做多的股票、债券和黄金动态投资组合，外加现金，每月再平衡，仅使用公开可得的数据。我们在常规指标上评估投资组合：回报率、波动率、夏普比率（按联邦基金利率超额计算）、回撤和换手率，此外还有随时间表现的稳定性，以已实现年化波动率的稳定性来判断。在2006年至2026年的20年期间，使用保守的交易成本估计，我们表明，通过简单的波动率控制，即动态地将固定权重投资组合与现金混合以目标固定波动率，所有风险调整和回撤指标均得到改善。该方法依赖于基于过去回报的简单投资组合波动率估计。我们还证明，基于凸优化的更复杂投资组合——类似于量化对冲基金使用的那些——在回报率和风险调整回报率方面产生进一步的实质性改进。我们考虑两种这样的投资组合：一种使用基于过去回报的简单未来回报估计，另一种基于过去回报和仅少数广泛可得的公开经济数据来预测未来回报。这些投资组合也优于在同一资产和数据上评估的一系列标准基于风险配置方法，如风险平价和最小方差。
英文摘要：
For more than four decades, the 60/40 stock/bond portfolio has served as a benchmark for delivering reasonable returns without excessive risk. More recently, a 50/30/20 stock/bond/alternative portfolio has been suggested. We use gold as the alternative and as an inflation hedge. In this paper we ask: how much improvement over these benchmark fixed-weight portfolios can be obtained using widely available public data and standard methods from quantitative finance? We restrict ourselves to long-only dynamic portfolios of stocks, bonds, and gold, plus cash, rebalancing monthly, using only publicly available data. We evaluate portfolios on the conventional metrics: return, volatility, Sharpe ratio (computed in excess of the federal funds rate), drawdown, and turnover, in addition to consistency of performance over time, judged by the consistency of the realized annual volatility. Over the 20--year period 2006--2026, using a conservative estimate of trading costs, we show that all risk-adjusted and drawdown metrics are improved using simple volatility control, where we dynamically mix the fixed-weight portfolios with cash so as to target a fixed volatility. This method relies on a simple estimate of portfolio volatility derived from past returns. We also demonstrate that more sophisticated portfolios based on convex optimization---similar to those used in quantitative hedge funds---yield further substantial improvement in return and risk-adjusted return. We consider two such portfolios, one that uses a simple estimate of future returns based on past returns, and one that forecasts future returns based on past returns and just a handful of widely available public economic data. These portfolios also outperform a suite of standard risk-based allocation methods, such as risk parity and minimum variance, evaluated on the same assets and data.
