Volatility Targeting: It’s Complicated!
以美国股票为例，检验波动率目标化策略能否改善收益、降低尾部损失并稳定风险。纠正了先前研究的方法偏差后，发现该策略能稳定风险并小幅提升收益，但无法减少尾部损失。
This article examines whether volatility targeting can improve returns, decrease tail loss, and deliver a more stable risk profile for risk assets using the example of US equities. The author identifies biases in the methodology used to assess the viability of the strategy in several recently published studies on the subject. After correcting for these biases, he finds that the volatility-targeting strategy results in a more stable risk profile and delivers marginally better returns but fails to reduce tail loss. Furthermore, the author shows that a better volatility-forecasting model could significantly improve returns but not tail loss. <b>TOPICS:</b>Security analysis and valuation, quantitative methods, statistical methods, tail risks, performance measurement <b>Key Findings</b> ▪ This article examines whether volatility targeting can improve returns, decrease tail loss, and deliver a more stable risk profile for risk assets using the example of US equities. ▪ There are two key related biases in several recently published studies on the subject: the use of future information in defining the strategy benchmark and the use of risk-adjusted return measures to gauge the profitability of the strategy. This article corrects these biases. ▪ Volatility targeting results in a more stable risk profile and delivers marginally better returns but fails to reduce tail loss. A better volatility-forecasting model could significantly improve returns but not tail loss.