As far back as 1976, with the publication of Fischer Black’s “Studies of Stock Price Volatility Changes,” financial economists have known that volatility and returns are negatively correlated. This relationship results in the tendency to produce negative equity returns in times of high volatility. In addition, the research, including the 2017 study “Tail Risk Mitigation with Managed Volatility Strategies,” demonstrates that while past returns do not predict future returns, past volatility largely predicts future near-term volatility, i.e., volatility is persistent (it clusters). High (low) volatility over the recent past tends to be followed by high (low) volatility in the near future. Taken together, these findings have led to the development of strategies that scale volatility inversely to past realized volatility, with the strategies tending to produce a positive impact on investment performance.
Research Findings
The authors of the 2018 study “The Impact of Volatility Targeting” examined the impact of volatility targeting on 60 assets, with daily data beginning as early as 1926 and ending in 2017. They not only confirmed that risk assets exhibit a negative relationship between returns and volatility, but also found that in addition to reducing volatility, scaling also reduces excess kurtosis (fatter tails than in normal distributions), cutting both tails, right (good tail) and left (bad tail). And for portfolios of risk assets, Sharpe ratios (measures of risk-adjusted return) are higher with volatility scaling. Another key finding was that, since volatility often increases in periods of negative returns, targeting volatility causes positions to be reduced, which is the same direction one would expect from a time-series momentum (trend-following) strategy.
These findings are consistent with those of other research, including the June 2017 study “A Century of Evidence on Trend-Following Investing, the October 2019 study “Volatility Expectations and Returns,” and the 2019 study “Portfolio Management of Commodity Trading Advisors With Volatility Targeting.” However, these findings are contrary to traditional finance theory and the intuition that the standard risk-return tradeoff should lead to underperformance of a portfolio that scales down exposure during volatile periods. The strategy has worked because volatility clusters and historically increased volatility has not been compensated by higher returns.
Latest Research
Georg Cejnek and Florian Mair contribute to the literature with their study “Understanding Volatility-Managed Portfolios” in which they investigated the mechanisms that lead to the outperformance of volatility management. Their data series covered the period December 1928 to December 2019. Following is a summary of their findings:
- Volatility management outperforms by levering up in good times without increasing downside exposure to fundamental risk drivers – regression results showed an upside beta of 1.3 and a downside beta of 0.8.
- In the period spanning the biggest six-month cumulative cash-flow shock, from September 2008 to February 2009, the unmanaged index returned -43%, while the volatility-managed strategy dropped only by 17.1%.
- The biggest underperformance of scaling volatility is generated in periods of a recovery after severe and volatile drawdowns, such as March 1933 to August 1933 or March 2009 to August 2009. The outperformance of volatility management is not confined to a certain level of past realized volatility.
- The outperformance of volatility management does not have any exposure to cash-flow news.
- While a simple linear regression showed no clear relationship between outperformance and volatility, once volatility exceeded a certain threshold, the volatility-managed portfolio always outperformed the unmanaged strategy. This threshold corresponded to an annualized realized volatility of about 68%. In all five of the most volatile months, the strategy outperformed the unmanaged index – in the most volatile month, October 2008, the outperformance was 13.8%.
- Volatility management outperformed in almost all of the biggest drawdowns as measured by cumulative negative cash flow, discount rate and expected volatility news, increasing investor utility.
- The most severe drawdown of the vol-managed CRSP strategy was -63% from September 1929 to June 1932 versus the 85% excess return loss for the CRSP value-weighted index.
- The strategy produced economically significant outperformance of around 3% per year when applied to CRSP value-weighted returns and the Fama-French market factor. Results were similar in the shorter subsample period beginning in August 1963.
Cejnek and Mair concluded: “Our results show that volatility management levers up good times, does not alter the composition of risk drivers in down states and outperforms by levering down in the most severe cumulative news shocks.” They added: “Volatility-management tends to underperform in the very best periods after a recovery while it is able to partly avoid the most severe shocks to cash-flow and discount-rate news.”
Summary
Cejnek and Mair demonstrated that volatility management leads to a positive return asymmetry where positive returns are scaled up, except for very high returns occurring during reversals, while negative returns are scaled down. The most severe negative returns are especially scaled down, thereby increasing investor utility.
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