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波动率控制下的动态股票、债券与黄金组合

文章 arXiv papers · 作者: Nikhil Devanathan et al.
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总结

本文将固定股票与债券配置、股票债券黄金配置，与可能持有现金的动态多头组合进行比较。组合使用公开数据，每月再平衡，并根据收益、波动率、超额夏普比率、回撤、换手率及实现年化波动率的一致性进行评估。评估中保守地计入交易成本。

一种简单方法是将固定权重组合与现金混合，以控制风险并达到目标波动率；目标波动率根据历史收益估算。论文报告称，在2006至2026的评估期间，该方法改善了风险调整后表现和回撤指标。论文还报告，使用历史收益单独构建的凸优化组合，或结合少量公开经济数据构建的凸优化组合，表现进一步提升，并称这些组合优于所测试的风险平价和最小方差等风险配置方法。文本没有提供详细的实施方案或数值结果，结论仅适用于所述资产、数据、时期和成本假设。

核心观点

  • 研究测试了股票、债券、黄金和现金的月度多头配置。
  • 波动率目标控制通过将固定配置与现金混合来调整敞口。
  • 波动率控制方法根据历史收益估算组合风险。
  • 凸优化组合使用历史收益，其中一个版本还使用公开经济数据。
  • 在研究假设下，报告结果显示动态方法在风险调整和回撤指标方面占优。

标签

股票固定收益大宗商品多资产波动率投资组合构建
全文
# Simple Dynamic Stock/Bond/Gold Portfolios


# Simple Dynamic Stock/Bond/Gold Portfolios









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。