Volatility Targeting: Does Scaling Risk Improve Returns?

Nobody has a reliable forecast of next month’s stock return. Next month’s volatility is a different matter. A turbulent week tends to be followed by another turbulent week, and calm stretches tend to stay calm. Volatility targeting tries to use the forecast that works: hold more stock when markets are quiet, less when they are violent, and keep the portfolio’s risk roughly constant.

On a Reuters count from October 1, 2026, funds running some version of this rule hold an estimated $300 billion to $500 billion, and their stock allocations sat at the 98th percentile of their range since 2010, according to Deutsche Bank data.¹ Many retail investors own the strategy without knowing it, through the indices behind fixed indexed annuities. The academic evidence is good on one question and much weaker on the other. A volatility target reliably makes risk steadier. Whether it improves returns depends on the asset, the period, the costs and whether the test could have been run in real time.

How a volatility target sets exposure

The rule divides the risk you want by the risk you forecast:

Here $\sigma^{*}$ is the target volatility, $\hat\sigma_t$ is the forecast, usually the annualized standard deviation of the last one to six months of daily returns, and $w_{\max}$ is a cap on exposure. Whatever is not in the risky asset sits in cash. With a 10% target:

Without borrowing, the cap is 100%, and the rule becomes asymmetric. It can cut risk when markets are rough but cannot add risk when they are calm, so its average stock exposure ends up below a buy-and-hold investor’s. With borrowing, it can lever up in quiet markets, and financing costs, margin rules and the size of the first shock after a calm stretch start to matter.

Two rules that share a name

The rule above scales exposure by one over volatility. The paper most people cite when they say volatility management works, Moreira and Muir’s 2017 Journal of Finance article, scales by one over variance, the square of volatility, using the previous month’s daily returns.² The difference has a reason behind it. In mean-variance theory, the optimal stock weight is

where $\gamma$ is risk aversion. If the expected excess return stays roughly constant while variance moves, the optimal weight moves with one over variance. Inverse-variance scaling reacts much more strongly: when volatility halves, a constant-volatility rule doubles exposure and an inverse-variance rule quadruples it. Our test below shows the gap is large in practice.

Volatility is easier to forecast than returns

Robert Engle’s 1982 ARCH model showed that the variance of an economic series can change over time in a way past data predict, and Tim Bollerslev’s 1986 GARCH model became the standard way to describe how persistent those changes are.³⁴ Engle shared the 2003 Nobel Prize for the work. Volatility clustering is one of the most reliable patterns in financial data, which is why a volatility target can work without any view on where prices are heading.

Predictable volatility is useful only if expected returns do not rise in step with it. If doubling the risk doubled the expected reward, cutting exposure in volatile periods would give up the extra compensation investors are being paid for. French, Schwert and Stambaugh found in 1987 that the expected market risk premium is positively related to predictable volatility, and that unexpected returns are negatively related to unexpected increases in volatility.⁵ Moreira and Muir’s core finding is that volatility moves far more than expected returns do, so the reward per unit of risk is lower when volatility is high.²

Stocks have one more feature that helps. Volatility tends to rise after prices fall. Campbell and Hentschel modeled this as volatility feedback: news that raises expected volatility raises the return investors require, which pushes prices down.⁶ For a volatility target, the sequence runs: prices fall, volatility rises, exposure is cut. In a slow bear market that behaves like a short-term trend rule and helps. In a sharp drop followed by an immediate rebound, the rule cuts exposure near the bottom and buys back late.

What the research found

Early portfolio studies

Fleming, Kirby and Ostdiek tested volatility timing across S&P 500, Treasury bond and gold futures plus cash from 1983 to 1997. A risk-averse investor would have paid more than 170 basis points a year to switch from a static allocation to their volatility-timing strategy.⁷ Their 2003 follow-up found that estimating volatility from intraday data instead of daily data was worth another 50 to 200 basis points a year in the same framework.⁸

Momentum is the strongest case

Momentum strategies have highly variable risk and occasional crashes, which makes them the best candidate for scaling. Barroso and Santa-Clara scaled US momentum to constant volatility and reported a Sharpe ratio of 0.97 against 0.53 unscaled, with the worst month improving from −79% to −28%.⁹ Daniel and Moskowitz found that momentum crashes cluster in “panic states” after market declines, and that a dynamic strategy using forecasts of both mean and variance roughly doubled the static strategy’s Sharpe ratio.¹⁰

The 2017 paper that revived the idea

Moreira and Muir applied inverse-variance scaling to the market and to the main equity factors. The managed market earned an alpha of 4.9% a year against the unmanaged market with a beta of 0.61, a 25% increase in the Sharpe ratio. Momentum, profitability, betting against beta and currency carry also showed positive alphas; the size factor did not.² Their 2019 follow-up estimated that a long-term investor who ignores changes in volatility gives up the equivalent of 2.4% of wealth a year, within their model.¹¹

Sixty assets: tails improve everywhere, Sharpe ratios do not

Harvey and coauthors at Man Group tested volatility targeting on more than 60 assets with daily data beginning as early as 1926. Sharpe ratios rose for equities and credit, and for balanced 60/40 and risk parity portfolios. For bonds, currencies and commodities the effect was negligible. Across all 60-plus assets, targeting reduced the likelihood of extreme returns and the volatility of volatility. For US equities from 1927 to 2017, the Sharpe ratio rose from 0.40 to between 0.48 and 0.51 depending on the half-life of the volatility estimate.¹²

Real-time tests are much weaker

Moreira and Muir’s main test is a spanning regression, and it sets the scaling constant using the full sample, information an investor would not have had at the start. Liu, Tang and Zhou showed that the standard application has this look-ahead problem, and that once corrected, maximum drawdowns of 68% to 93% appear in almost all cases.¹³

Cederburg, O’Doherty, Wang and Yan then ran 103 equity strategies in real time. The managed versions did not systematically beat their unmanaged counterparts: the real-time combination strategy had a lower certainty-equivalent return in 72 of 103 cases. For the market itself, the out-of-sample Sharpe ratio was 0.42 managed against 0.46 unmanaged, and the in-sample gain was concentrated around the Great Depression. They traced the failure to instability in the relationships the in-sample results depend on.¹⁴

Trading costs remove most of the factor gains

Barroso and Detzel added transaction costs. After costs, volatility management of factors other than the market generally produced zero abnormal returns and significantly lower Sharpe ratios. The managed market factor held up after costs, but its benefits showed up only when investor sentiment was high.¹⁵

Outside the US, drawdowns sometimes got worse

Bongaerts, Kang and van Dijk tested conventional volatility targeting in ten equity markets from 1982 to 2019. It did not consistently improve performance, and in the UK, Canada, Australia and Hong Kong it increased the maximum drawdown. Expected shortfall rose in eight of ten markets. Only the US Sharpe ratio gain was statistically significant. Their alternative, which adjusts exposure only when volatility is unusually high or low, did better.¹⁶ Any US-only result, including ours below, may come from the most favorable market for the strategy.

The newest work is more favorable

DeMiguel, Martín-Utrera and Uppal agree that single factors managed one at a time rarely survive costs out of sample. Their 2024 paper scales a combination of nine factors jointly and nets trades across them. From 1977 to 2020, out of sample and net of costs, the conditional portfolio had a Sharpe ratio of 1.062 against 0.940 for the unconditional one, 13% higher. Without the netting of trades, the advantage disappeared: 0.739 against 0.758.¹⁷ Xu reports that a modified real-time implementation improved Sharpe ratios in 148 of 197 factors and anomalies in the accepted version of a 2026 paper.¹⁸ Wang and Yan find that scaling by downside volatility works better than scaling by total volatility.¹⁹ The debate is still open, and the recent papers that find gains rely on more careful implementation than the simple rule.

A 100-year test on US stocks

I ran the simple rule on Ken French’s daily US market returns from 1927 through May 2026: exposure equal to 12% divided by trailing 21-day volatility, measured through the prior day, capped at 100%, the rest in T-bills, with 5 basis points of cost per unit traded. The parameters are round numbers chosen before looking at results. The third row is the comparison that matters: a fixed stock and T-bill mix sized so its volatility matches the strategy’s. Any strategy that holds less stock will have smaller drawdowns than buy and hold; the question is whether it beats a plain lower-risk mix.²⁰²¹

Author’s calculation from Kenneth R. French daily data. The fixed-mix weight is set with hindsight to match the strategy’s realized volatility. Gross of taxes.

Over the full century, the rule beat the matched mix by 1.3 points a year at the same volatility, and the Sharpe ratio gain is close to the one Harvey and coauthors report for 1927 to 2017. The steadier risk shows up clearly: trailing one-year volatility stayed between 9% and 15% in 83% of rolling windows, while buy and hold ranged from 5% to 46%. The volatility of volatility fell from 8.8% to 2.5%.

The subperiods are less flattering:

• From 2000: Sharpe ratio 0.48 against 0.42 for buy and hold. The maximum drawdown was −37.5%, slightly worse than the −35.7% of a 60% stock mix with the same volatility. The smaller loss compared with buy and hold came from holding less stock.
• From 1936, after the Depression: Sharpe ratio 0.60 against 0.53, with a maximum drawdown of −45.0% against −41.3% for the matched mix.
• Costs: at 0, 5, 20 and 50 basis points per unit traded, the full-period Sharpe ratio was 0.57, 0.56, 0.52 and 0.45. At 50 basis points the advantage was gone. The rule traded the equivalent of 2.75 times the portfolio a year, and in a taxable account much of that would realize gains.
• Parameters: across lookbacks of one, three and six months and targets from 8% to 15%, Sharpe ratios ranged from 0.51 to 0.57 for the full period and 0.44 to 0.48 from 2000. No setting stood out, and none produced a large edge after 2000.
• Monthly rebalancing cut turnover to 1.2 times a year with a Sharpe ratio of 0.54. A rule that trades only when the target exposure moves by more than 10 points kept a Sharpe ratio of 0.57 with turnover of 1.4 times a year.

I also ran Moreira and Muir’s monthly inverse-variance version on the same data. With the scaling constant set from the full sample, the Sharpe ratio rose from 0.45 to 0.52 over 1927 to 2026. From 1936 on, even that look-ahead version trailed buy and hold, 0.50 against 0.53. Estimating the constant in real time from August 1936 gave 0.45 against 0.53, and the implied stock exposure reached 15 times capital in the calmest months. From 2000 the real-time version did better, 0.52 against 0.48, while calling for up to 5.8 times leverage. The uncapped inverse-variance rule is the more aggressive bet, and the one whose results depend most on the window.

Crashes and rebounds

The episode returns show both sides of the trade. Same rule as above: 12% target, 21-day lookback, no leverage.

Author’s calculation from Kenneth R. French daily data, including the 5 basis point trading cost.

The rule cut the 2007 to 2009 loss in half and the COVID crash loss by about 60%. It then captured about a third of each rebound, because exposure sat near 14% to 32% when the recoveries began and rose only as volatility faded. Over the whole of 2020, buy and hold returned 24.2% and the rule 14.3%. The two sudden shocks of 2018 and 2024 hit while exposure was at the 100% cap after calm markets, so the rule barely helped. A volatility target protects best in long, grinding declines and worst in fast drops that reverse.

Multi-asset portfolios: targeting total risk

Volatility targeting and risk parity get lumped together, but they answer different questions. Risk parity decides how to split risk across assets, so that stocks do not dominate a portfolio’s risk. Volatility targeting decides how much total risk to carry right now. One can be applied on top of the other: build a balanced portfolio, then lever or delever the whole thing to a target. Our risk parity guide covers the first question.

For a portfolio with weights $w$ and covariance matrix $\Sigma$, the forecast volatility and the scaling factor are

This keeps the strategic mix intact and changes only its size. It also exposes the main weakness of the multi-asset version: total risk depends on correlations as well as on each asset’s volatility. When stocks and high-quality bonds offset each other, forecast portfolio volatility is low and the rule levers up. In a crisis where both fall together, every input to the denominator rises at once.

That happened in March 2020. The European Central Bank’s Financial Stability Review reported that volatility rose sharply across all asset classes and cross-asset correlations climbed to historically high levels. In the ECB’s stylized model, a risk parity portfolio levered to about twice its capital in calm markets had to sell assets worth nearly 225% of its capital to stay on target. The ECB put the global total in volatility-sensitive strategies at up to $2 trillion, including about $300 billion in risk parity funds.²² In 2022 stocks and bonds fell together, and RPAR, a risk parity ETF, lost 22.8% at NAV.²³

The DeMiguel result above points in a useful direction for multi-asset investors: scale the combined portfolio with one signal and net the trades, instead of running a separate volatility target on each sleeve.¹⁷

Everyone running the rule sells at once

A volatility target buys after calm periods and sells after volatile ones. For one investor that is risk control. When hundreds of billions of dollars follow similar rules, the selling arrives together. The IMF’s October 2017 Global Financial Stability Report counted $440 billion in volatility-targeting variable annuities with 8% to 12% targets and $220 billion in trend-following funds, estimated that these strategies might hold more than $0.5 trillion of US equities, and cited estimates that volatility-target funds accounted for 9% to 16% of S&P 500 futures volume during the August 24 to 26, 2015 selloff.²⁴

Attribution in any single episode is contested. The Bank for International Settlements traced the February 2018 volatility spike mainly to leveraged and inverse VIX products, which were much smaller than volatility-target funds.²⁵ Bank estimates of volatility-control selling in specific selloffs come from trading desks and are not audited. The October 2026 Reuters report cites a Barclays model in which a typical 10%-target fund, then holding 88% equities, could sell more than $100 billion of stocks in a bearish scenario.¹

You may already own a volatility target

Many fixed indexed annuities credit interest based on a “volatility-controlled” index instead of the S&P 500. A 2026 American Academy of Actuaries paper describes custom indices that “typically include built-in volatility control features.” Options on a low-volatility index cost less, so the insurer can advertise a higher participation rate.²⁶ The S&P 500 Daily Risk Control 10% index, a common template, sets exposure each day to 10% divided by realized volatility, capped at 150%. It estimates volatility as the larger of a fast and a slow moving average, so it de-risks quickly and re-risks gradually. Many of these are excess-return indices: they earn the stock return minus a short-term interest rate on their exposure and nothing on the uninvested portion.²⁷

That construction matters for comparisons. In my simplified simulation from 1995 to May 2026 (10% target, 150% cap, 21-day volatility), the excess-return version compounded at 6.0% a year. The same rule with cash earning T-bills compounded at 8.5%, and the stock market at 11.5%. The gap between the first two is the T-bill rate, 2.4% a year on average over the period. The insurer prices the participation rate off whichever index it uses, so the construction is a pricing choice. In practice, a 100% participation rate on an excess-return volatility-control index and a 50% rate on the S&P 500 measure different things, and much of an index’s history before its launch date is backtested.

Variable annuities went the same way after 2008. In a 2011 speech, the SEC’s director of investment management noted that buyers of guaranteed benefits faced “increasing limitations on investment choices, reflecting an effort by insurers to limit volatility,” and asked for disclosure of “the possibility of missing a market uptick.”²⁸ Managed futures funds are the other common place to meet the rule: AQR’s Managed Futures Strategy Fund (AQMIX) targets annualized volatility between 5% and 13%.²⁹ RPAR, by contrast, sets weights from long-term volatilities and rebalances quarterly instead of targeting portfolio volatility day to day.²³

Questions to ask about any volatility-controlled index or fund:

• What is the target, and what is the maximum exposure?
• Is it an excess-return index, and is there a fixed decrement or fee deducted from the index level?
• When did the index launch, and how much of the history shown is a backtest?
• How fast does it re-risk after a selloff? Check how it did from March 24 to the end of 2020.

Try it yourself

The simulator runs the same rule on the same daily data. Move the target, lookback, cap and trading cost, and watch the third column: the fixed mix with the same realized volatility. The exposure chart under the growth chart shows when the rule was out of the market. There is deliberately no button that searches for the best parameters.

What I recommend

For a DIY investor with a static diversified allocation, I would not build a daily volatility-targeting system. The evidence that it steadies risk is strong. The evidence that it raises returns after realistic costs, out of sample, is thin, and outside the US the drawdown benefit sometimes reversed. A fixed stock and bond mix rebalanced once a year is cheaper, tax-efficient, needs no model and is easier to stick with than a rule that entered a 61% rebound with 14% of its money in stocks.

It becomes more reasonable in a few cases:

• You already use leverage. If you run a levered portfolio, through margin, futures or leveraged funds, cutting exposure when volatility spikes is basic risk management and keeps you away from margin calls. A slow, capped rule on the whole portfolio is enough.
• You hold risk parity or trend strategies. These usually vol-target internally already. Knowing that explains how they behave in fast reversals.
• You own a volatility-controlled annuity index. Judge it with the questions above, and compare its participation rate only with other indices built the same way.

If you experiment anyway, use a tax-advantaged account, target the whole portfolio with one signal, cap exposure, rebalance only when the target exposure moves by a set band, and pick the target the same way you would pick a stock-bond mix: by how much volatility you can live with. No target level is optimal in any statistical sense. Summitward’s portfolio analytics show the realized volatility and drawdowns of your current holdings, a useful baseline before choosing any target.

Key Takeaways

• Volatility is forecastable; returns mostly are not. A volatility target uses that by holding target volatility divided by forecast volatility, usually with a cap.
• The risk-control evidence is broad. Harvey and coauthors found fewer extreme returns and lower volatility of volatility across more than 60 assets.
• The return evidence is narrow. Sharpe gains appear for equities, credit and momentum in historical tests, but real-time tests of 103 strategies found no systematic improvement, and most factor gains vanished after trading costs.
• On US stocks, 1927 to May 2026, a 12% target raised the Sharpe ratio from 0.46 to 0.56 in our test, but only to 0.48 from 0.42 after 2000, with no drawdown advantage over a plain stock-and-T-bill mix of the same volatility.
• It lags sharp rebounds. In the same test it gained 20% from March 24 to August 31, 2020, against 61% for the market.
• Many annuity indices use it. Excess-return construction and backtested history make their participation rates hard to compare with plain S&P 500 crediting.

Frequently Asked Questions

What is volatility targeting?

A rule that scales a portfolio’s exposure so its expected volatility stays near a chosen level. Exposure equals the target divided by forecast volatility, usually estimated from recent daily returns, with the remainder in cash and a cap on leverage.

Does volatility targeting improve returns?

It usually lowers returns in absolute terms because it holds less risk on average. In US stock data it has raised the Sharpe ratio over long periods, but real-time academic tests and international data show much smaller or no gains, and trading costs and taxes reduce them further.

What is the difference between volatility targeting and risk parity?

Risk parity sets the mix across assets so each contributes a similar share of risk. Volatility targeting sets the total amount of risk over time. Institutional risk parity portfolios often do both: balance risk across assets, then lever the whole portfolio to a volatility target.

What target volatility should I use?

The target is a risk-tolerance choice, like choosing between a 60/40 and an 80/20 allocation. US stocks have averaged about 17% volatility since 1927, so a 10% to 12% target without leverage behaves roughly like a 60% to 70% stock allocation in risk terms.

Are volatility-controlled indices in annuities bad?

Not necessarily, but they are hard to compare. Many are excess-return indices that give up a short-term interest rate, some deduct a fixed decrement, and most show backtested history before launch. Compare participation rates only between indices built the same way.

Does volatility targeting cause market crashes?

It can add to selling once a decline is under way, because many funds cut exposure when volatility rises. The ECB and IMF have both described this procyclical effect. Whether it caused any specific selloff is disputed; the BIS attributed the February 2018 spike mainly to VIX products.

Related Guides

• The Risk Parity Reality Check for splitting risk across assets and whether to lever the result.
• Minimum Volatility and Betting Against Beta for the cross-sectional cousin: holding less volatile stocks instead of varying exposure over time.
• Missing the Best Days for why the best and worst days cluster in the same volatile stretches.
• Managed Futures and Trend Following for funds that vol-target internally.
• Does Trend Following Work on Crypto? for separating volatility scaling from the trend signal.

Sources

1. Saqib Iqbal Ahmed, “Volatility control funds near record equity exposure, raising selloff risk,” Reuters, October 1, 2026, via The Globe and Mail. Asset and selling figures are bank estimates.
2. Alan Moreira and Tyler Muir, “Volatility-Managed Portfolios,” Journal of Finance 72(4), 2017, 1611–1644. doi.org
3. Robert F. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica 50(4), 1982, 987–1007. doi.org
4. Tim Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics 31(3), 1986, 307–327. doi.org
5. Kenneth R. French, G. William Schwert and Robert F. Stambaugh, “Expected Stock Returns and Volatility,” Journal of Financial Economics 19(1), 1987, 3–29. doi.org
6. John Y. Campbell and Ludger Hentschel, “No News Is Good News: An Asymmetric Model of Changing Volatility in Stock Returns,” Journal of Financial Economics 31(3), 1992, 281–318. doi.org
7. Jeff Fleming, Chris Kirby and Barbara Ostdiek, “The Economic Value of Volatility Timing,” Journal of Finance 56(1), 2001, 329–352. doi.org
8. Jeff Fleming, Chris Kirby and Barbara Ostdiek, “The Economic Value of Volatility Timing Using ‘Realized’ Volatility,” Journal of Financial Economics 67(3), 2003, 473–509. doi.org
9. Pedro Barroso and Pedro Santa-Clara, “Momentum Has Its Moments,” Journal of Financial Economics 116(1), 2015, 111–120. doi.org
10. Kent Daniel and Tobias J. Moskowitz, “Momentum Crashes,” Journal of Financial Economics 122(2), 2016, 221–247. doi.org
11. Alan Moreira and Tyler Muir, “Should Long-Term Investors Time Volatility?” Journal of Financial Economics 131(3), 2019, 507–527. doi.org
12. Campbell R. Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison and Otto Van Hemert, “The Impact of Volatility Targeting,” Journal of Portfolio Management 45(1), 2018, 14–33. duke.edu (PDF)
13. Fang Liu, Xiaoxiao Tang and Guofu Zhou, “Volatility-Managed Portfolio: Does It Really Work?” Journal of Portfolio Management 46(1), 2019, 38–51. doi.org
14. Scott Cederburg, Michael S. O’Doherty, Feifei Wang and Xuemin (Sterling) Yan, “On the Performance of Volatility-Managed Portfolios,” Journal of Financial Economics 138(1), 2020, 95–117. doi.org
15. Pedro Barroso and Andrew Detzel, “Do Limits to Arbitrage Explain the Benefits of Volatility-Managed Portfolios?” Journal of Financial Economics 140(3), 2021, 744–767. doi.org
16. Dion Bongaerts, Xiaowei Kang and Mathijs van Dijk, “Conditional Volatility Targeting,” Financial Analysts Journal 76(4), 2020, 54–71. doi.org
17. Victor DeMiguel, Alberto Martín-Utrera and Raman Uppal, “A Multifactor Perspective on Volatility-Managed Portfolios,” Journal of Finance 79(6), 2024, 3859–3891. doi.org
18. Xia Xu, “Improving Volatility-Managed Portfolios in Real Time,” Critical Finance Review 15(2), 2026, 179–207. Figures from the accepted version. doi.org
19. Feifei Wang and Xuemin Sterling Yan, “Downside Risk and the Performance of Volatility-Managed Portfolios,” Journal of Banking & Finance 131, 2021, 106198. doi.org
20. Kenneth R. French, Data Library, “Fama/French 3 Factors [Daily],” value-weighted US market return and one-month T-bill rate, July 1926 to May 2026. dartmouth.edu
21. Author’s calculation. Script, data and full output: summitward-research
22. European Central Bank, “Volatility-targeting strategies and the market sell-off,” box in Financial Stability Review, May 2020. ecb.europa.eu
23. RPAR Risk Parity ETF, fund page and prospectus. rparetf.com
24. International Monetary Fund, Global Financial Stability Report, October 2017, Chapter 1, Figure 1.21. imf.org (PDF)
25. Bank for International Settlements, “Volatility is back,” BIS Quarterly Review, March 2018. bis.org (PDF)
26. American Academy of Actuaries, “Fixed Indexed Annuities: Product Mechanics and Risk Management,” February 2026. actuary.org (PDF)
27. S&P 500 Daily Risk Control 10% Excess Return Index methodology, as reproduced in an HSBC index supplement, February 2016. hsbc.com (PDF)
28. Eileen Rominger, Director, SEC Division of Investment Management, keynote address to the Insured Retirement Institute, June 28, 2011. sec.gov
29. AQR Funds, prospectus filed April 28, 2026 (SEC EDGAR accession 0001193125-26-185725), AQR Managed Futures Strategy Fund principal investment strategies. sec.gov

Author disclosure

Summitward has no business relationship with AQR, RPAR’s sponsors, S&P Dow Jones Indices, any insurer or any firm mentioned here and receives no compensation from them. Figures labeled as the author’s calculation are historical measurements over the stated window, not forecasts, and are gross of taxes. Nothing here is investment advice.