parallax background


Volatility targeting seeks to counter the fluctuations in volatility. It leads to leveraging a portfolio at times of low volatility.
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. 
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.

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.

Opinions expressed are those of the author and may not be shared by all personnel of Park Capital SA. These opinions are subject to change without notice, are for information purposes only and do not constitute an offer or invitation to make an investment in any financial instrument or in any product to which the Company and/or its affiliates provides investment advisory or any other financial services. Any organisations, financial instrument or products described in this material are mentioned for reference purposes only which should not be considered a recommendation for their purchase or sale. Neither the Company nor the authors shall be liable to any person for any action taken on the basis of the information provided. Some statements contained in this material concerning goals, strategies, outlook or other non-historical matters may be forward-looking statements and are based on current indicators and expectations. These forward-looking statements speak only as of the date on which they are made, and the Company undertakes no obligation to update or revise any forward-looking statements. These forward-looking statements are subject to risks and uncertainties that may cause actual results to differ materially from those contained in the statements. The Company and/or its affiliates may or may not have a position in any financial instrument mentioned and may or may not be actively trading in any such securities. This material is proprietary information of the Company and its affiliates and may not be reproduced or otherwise disseminated in whole or in part without prior written consent from the Company. The Company believes the content to be accurate. However, accuracy is not warranted or guaranteed. The Company does not assume any liability in the case of incorrectly reported or incomplete information. Unless stated otherwise all information is provided by the Company. Past performance is not indicative of future results.