Dynamic Stock, Bond, and Gold Portfolios with Volatility Control
Summary
This paper compares fixed stock-and-bond and stock, bond, and gold allocations with long-only dynamic portfolios that may also hold cash. The portfolios use public data, rebalance monthly, and are assessed using return, volatility, excess Sharpe ratio, drawdown, turnover, and the consistency of realized annual volatility. Trading costs are included conservatively in the evaluation.
A simple approach controls risk by mixing a fixed-weight portfolio with cash to target a chosen volatility, estimated from past returns. The paper reports improved risk-adjusted and drawdown measures for this method over its 2006–2026 evaluation period. It also reports further gains from convex-optimization portfolios using either past returns alone or returns plus a small set of public economic data, and says these outperform tested risk-based allocations such as risk parity and minimum variance. The text does not provide detailed implementation choices or numerical results, and conclusions are limited to the stated assets, data, period, and cost assumptions.
Key ideas
- The study tests monthly, long-only allocations across stocks, bonds, gold, and cash.
- Volatility targeting adjusts exposure by mixing a fixed allocation with cash.
- Portfolio risk is estimated from past returns for the volatility-control method.
- Convex optimization portfolios use historical returns, with one version also using public economic data.
- Reported results favor the dynamic approaches on risk-adjusted and drawdown metrics under the study’s assumptions.
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Full text
# 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.
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