The Impact of Volatility Targeting


Volatility clustering is a key feature of financial asset returns:

  • High volatility over the recent past tends to be followed by high volatility in the near future.
  • This underpins Robert Engle’s work on ARCH, for which he was awarded the 2003 Nobel Prize in Economics.

Volatility targeting seeks to counter the fluctuations in volatility:

  • It leads to leveraging a portfolio at times of low volatility, and scaling down exposures at times of high volatility.
  • This approach targets a constant level of volatility, rather than a constant notional exposure.

Impact on Sharpe ratio

  • Volatility targeting improves the Sharpe ratio of “risk assets” (equities and credit), and that of “balanced” and “risk parity” portfolios that have a substantial allocation to these risk assets.
  • For equity and credit, volatility targeting effectively introduces some momentum overlay due to the so-called leverage effect: the negative relationship between returns and changes in volatility.
  • In contrast, for bonds, currencies, and commodities the impact on the Sharpe ratio is negligible.

Impact on likelihood of tail events

  • Volatility targeting reduces the likelihood of extreme returns for all asset classes.
  • Importantly, “left-tail” events tend to be less severe, as they typically occur at times of elevated volatility, when a target-volatility portfolio has a scaled-down notional exposure.
30 MAY 2018


Campbell Harvey
Campbell Harvey
Investment Strategy Advisor at Man Group

Professor Campbell Harvey, a leading financial economist, has been an Investment Strategy Advisor to Man Group since 2005 and has contributed to a variety of research produced by the firm.

He is a Professor of Finance at the Fuqua School of Business, Duke University, and Research Associate at the National Bureau of Economic Research in Cambridge, Massachusetts. He served as Editor of The Journal of Finance from 2006 to 2012 and as the 2016 President of the American Finance Association.

A distinguished academic, Professor Harvey received the 2016 and 2015 Bernstein Fabozzi/Jacobs Levy Award for the Best Article from The Journal of Portfolio Management for his research on differentiating luck from skill. He has also received eight Graham and Dodd Awards/Scrolls for excellence in financial writing from the CFA Institute. Moreover, he has published over 125 scholarly articles on topics spanning investment finance, emerging markets, corporate finance, behavioural finance, financial econometrics and computer science.

Professor Harvey has served on the faculties of the Stockholm School of Economics, the Helsinki School of Economics, and the Booth School of Business at the University of Chicago. He has also been a visiting scholar at the Board of Governors of the Federal Reserve System. He was awarded an honorary doctorate from Svenska Handelshögskolan in Helsinki. He is a Fellow of the American Finance Association.

He holds a PhD in Finance from the University of Chicago.


One of the key features of volatility is that it is persistent, or “clusters”. High volatility over the recent past tends to be followed by high volatility in the near future. This observation underpins Engle’s (1982) pioneering work on ARCH models.1 In this paper, we study the risk and return characteristics of assets and portfolios that are designed to counter the fluctuations in volatility. We achieve this by leveraging the portfolio at times of low volatility, and scaling down at times of high volatility. Effectively the portfolio is targeting a constant level of volatility, rather than a constant level of notional exposure. Conditioning portfolio choice on volatility has attracted considerable recent attention. The financial media has zoomed in on the increasing popularity of risk parity funds.2 In recent work, Moreira and Muir (2017) find that volatility-managed portfolios increase the Sharpe ratios in the case of the broad equity market and a number of dynamic, mostly long-short stock strategies.

While most of the research has concentrated on equity markets, we investigate the impact of volatility targeting across more than 60 assets, with daily data from 1926. We find that Sharpe ratios are higher with volatility scaling for risk assets (equities and credit), as well as for portfolios that have a substantial allocation to these risk assets, such as a balanced (60-40 equity-bond) portfolio and a risk parity (equity-bond-credit-commodity) portfolio. Risk assets exhibit a so-called leverage effect, i.e., a negative relation between returns and volatility, and so volatility scaling effectively introduces some short-term momentum into strategies. Historically such a short-term trend strategy has performed well; see e.g. Hamill, Rattray, and Van Hemert (2016). For other assets, such as bonds, currencies, and commodities, volatility scaling has a negligible effect on realized Sharpe ratios.

We also show that volatility targeting can consistently reduce the likelihood of extreme returns (and the volatility of volatility) across our 60+ assets. Under reasonable investor preferences, a thinner left tail is much preferred (for a given Sharpe ratio).3 Volatility targeting reduces the maximum drawdowns for both the balanced and risk parity portfolio.

The outline of this paper is as follows. In Section 1, we discuss the data, volatility-scaling methods, and statistics used for comparing the performance of unscaled and volatility-scaled portfolios. In Section 2, we focus on US equities, for which we have data starting in 1926. In Section 3 we study US bonds and credit, and in Section 4 we look at 50 global equity indices, fixed income, currency, and commodity futures and forwards. The analyses for the multi-asset balanced and risk parity portfolios are covered in Section 5. In Section 6, we discuss the leverage effect to provide further insights as to why the Sharpe ratio of risk assets is improved by volatility scaling. We offer some concluding remarks in the final section and comment on methods other than volatility scaling that may improve the Sharpe ratio and left-tail risk of a long-only portfolio.


1. ARCH is autoregressive conditional heteroscedasticity. Robert Engle shared the 2003 Nobel Prize in Economics “for methods of analyzing economic time series with time-varying volatility (ARCH)” (
2 See e.g. the August 6, 2017 Wall Street Journal article “What is risk parity?”,
3 Under the common assumption of a concave utility function, and for a given Sharpe ratio, also a thinner right tail is preferred. A thinner left tail is more relevant though, as large negative returns have a disproportionately large effect on an investor’s utility.

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