Volatility Effects

The main criticism of conventional volatility (standard deviation of returns) as a risk metric is that weights deviations above and below the mean equally. But, is volatility still adequate for most investors as an indicator of downside risk? In his June 2025 paper entitled “Volatility: A Dead Ringer for Downside Risk”, Javier Estrada compares Spearman correlations of the ranking of country stock markets and industries by volatility to each of the rankings for those same return series according to seven downside risk risk metrics: semi-deviation, probability of loss, average loss, expected loss, worst loss, maximum drawdown and value at risk. Using monthly total returns in U.S. dollars for 47 countries (23 developed and 24 emerging) and 65 industries from respective MSCI inceptions through December 2024, he finds that:

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Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

• Invesco S&P 500 Low Volatility Portfolio (SPLV) – the 100 stocks from the S&P 500 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for SPLV is SPDR S&P 500 (SPY).
• iShares Edge MSCI Min Vol USA (USMV) – seeks to track an index composed of U.S. equities that, in the aggregate, have lower volatility characteristics relative to the broader U.S. equity market. The benchmark ETF for USMV is iShares Russell 3000 (IWV).
• iShares Edge MSCI Min Vol EAFE (EFAV) – seeks to track an index composed of developed market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed equity markets, excluding the U.S. and Canada. The benchmark ETF for EFAV is iShares MSCI EAFE Index (EFA).
• iShares Edge MSCI Min Vol Emerging Markets (EEMV) – seeks to track an index composed of emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader emerging equity markets. The benchmark ETF for EEMV is iShares MSCI Emerging Markets Index (EEM).
• iShares Edge MSCI Min Vol Global (ACWV) – seeks to track an index composed of developed and emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed and emerging equity markets. The benchmark ETF for ACWV is iShares MSCI ACWI (ACWI).
• Invesco S&P International Developed Low Volatility Portfolio (IDLV) – the 200 least volatile stocks of the S&P Developed excluding U.S. and South Korea LargeMid Cap BMI Index over the past 12 months, reformed quarterly. The Index is computed using net return, which withholds taxes applicable to non-resident investors. The benchmark ETF for IDLV is Vanguard FTSE All-Wld ex-US ETF (VEU).
• Invesco S&P MidCap Low Volatility Portfolio (XMLV) –  the 80 out of 400 medium-capitalization stocks from the S&P MidCap 400 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XMLV is SPDR S&P MidCap 400 (MDY).
• Invesco S&P SmallCap Low Volatility Portfolio (XSLV) – the 120 out of 600 small-capitalization stocks from the S&P SmallCap 600 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XSLV is iShares Core S&P Small-Cap (IJR).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through May 2025, we find that: Keep Reading

Why do factor timing strategies that shine in research papers disappoint in real life? In his May 2025 paper entitled “Caveats of Simple Factor Timing Strategies”, David Blitz discusses the following  simple factor timing strategies with material and statistically significant outperformance per published studies:

• Short-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on prior-month highest to lowest returns.
• Medium-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on past 12-month highest to lowest returns.
• Structurally overweighting momentum – each month gives double weight to the momentum factor and zero weight to size factor.
• Volatility scaling of the momentum factor – each month scales the momentum factor allocation between 40% and 0% based on the ratio of its 20-year volatility to its 12-month volatility, with remaining funds allocated equally to the other four factors.
• Seasonal momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on their average historical returns for the same calendar month over the last 20 years.
• Positioning based on investor sentiment – each month takes 200% (0%) exposure to an equal-weighted factor portfolio when last-month Baker-Wurgler investor sentiment is positive (negative).
• Exploiting long-term factor decay – takes an initial 200% exposure to an equal-weighted factor portfolio and linearly reduces exposure to 0% at the end of the sample.

He applies these strategies to five widely accepted U.S. stock market factors: size, value, profitability, investment and momentum. His benchmark is the monthly rebalanced equal-weighted portfolio of these five factors. For each strategy, he addresses general concerns such as portfolio maintenance frictions and recent performance decay, and he identifies strategy-specific concerns. He concludes with minimum standards for future factor timing studies (see the table below). Using monthly returns for the selected factors during July 1963 until December 2024, he finds that:

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Are exchange-traded funds (ETF) focused on Initial Public Offerings of stocks (IPO) attractive? To investigate, we consider three of the largest IPO ETFs and one recent Special Purpose Acquisition Company (SPAC) ETF, one of which is no longer available, in order of longest to shortest available histories:

• First Trust US Equity Opportunities ETF (FPX) – seeks to track price and yield of the IPOX-100 U.S. Index.
• Renaissance IPO ETF (IPO) – reflects approximately the top 80% of new public firms weighted by free float capitalization with a 10% cap for any one position. Large IPOs enter quickly and others enter during quarterly reviews. All exit two years after initial trade date.
• First Trust International Equity Opportunities ETF (FPXI) – seeks gross investment results that track price and yield of the IPOX International Index.
• Defiance Next Gen SPAC Derived ETF (SPAK) – 60% weight to IPOs derived from SPACs and 40% weight to common stock of newly listed SPACs, excluding warrants (dead as of the end of August 2022).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For all these ETFs, we use SPDR S&P 500 (SPY) as the benchmark. Using monthly returns for the IPO ETFs and SPY as available through April 2025, we find that:

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Is the ratio of implied volatility of implied volatility (CBOE VVIX Index), interpretable as a measure of changes in investor fear level, to the CBOE VIX Index itself a useful indicator of future stock market returns? To investigate, we relate monthly VVIX/VIX and monthly change in VVIX/VIX to monthly SPDR S&P 500 ETF Trust (SPY) total returns. Using end-of-month levels of both VVIX and VIX and dividend-adjusted monthly SPY closes during September 2006 (limited by VVIX) through March 2025, we find that:

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Is the implied volatility of implied volatility (CBOE VVIX Index), interpretable as a measure of changes in investor fear level, a useful indicator of future stock market returns? To investigate, we relate monthly VVIX and monthly change in VVIX to monthly SPDR S&P 500 ETF Trust (SPY) total returns. Using end-of-month levels of both VVIX and dividend-adjusted monthly SPY closes during September 2006 (limited by VVIX) through March 2025, we find that:

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What are the costs of mitigating tail risk via exchange-traded funds (ETF) designed to manage it? To investigate, we consider seven such ETFs, three dead and four live, as follows:

• • VelocityShares Tail Risk Hedged Large Cap ETF (TRSK) – hedges against tail risk by allocating 85% (15%) of assets to ETFs that track the S&P 500 Index (a volatility component designed to hedge against extreme market declines). This ETF is dead.
  • Cambria Global Tail Risk ETF (FAIL) – invests at least 40% of assets in investment grade, intermediate U.S. treasuries and TIPS, at least 40% in non-U.S. sovereign bonds and about 1% per month in put options. This ETF is dead.
  • Cambria Tail Risk ETF (TAIL) – holds cash and U.S. government bonds and about 1% of assets per month in put options.
  • Global X NASDAQ 100 Tail Risk ETF (QTR) – invests at least 80% of assets in the securities of the Nasdaq-100 Quarterly Protective Put 90 Index, which holds NASDAQ 100 stocks and put options on the NASDAQ 100 Index.
  • Global X S&P 500 Tail Risk ETF (XTR) – invests at least 80% of assets in the S&P 500 and put options on the S&P 500 Index.
  • Simplify Tail Risk Strategy ETF (CYA) – invests 50%-90% of assets in income-generating ETFs and up to 20% in derivatives to hedge tail risk. This ETF is dead.
  • Alpha Architect Tail Risk ETF (CAOS) – normally invests in S&P 500 Index put spreads. 

Note that TRSK, QTR, XTR and CYA are composite portfolios holding equities and embedded tail risk protection, while FAIL, TAIL and CAOS are pure tail risk protection usable as adjuncts to separate equity portfolios. We use SPDR S&P 500 ETF Trust (SPY), iShares MSCI EAFE ETF (EFA) and Invesco QQQ Trust (QQQ) over matched sample periods for reference. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the seven tail risk ETFs, SPY, EFA and QQQ as available through March 2025, we find that:

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Are machine learning predictions for both stock returns and return volatilities attractively exploitable? In their March 2025 paper entitled “Deep Learning of Conditional Volatility and Negative Risk-Return Relation”, Qi Wu, Xing Yan and Wenxuan Ma apply non-linear deep learning models (neural networks of one to five layers) and likelihood estimation to forecast next-month stock returns and volatilities. They select model settings and hyperparameters based on established practices from the literature and common experience, without extensive searching. They apply the neural network models to 153 firm-specific variables for U.S. stocks annually during 1991 through 2020 using 11-year rolling windows (seven years of training, three years of validation and one year of testing) for a total of 20 years of out-of-sample testing. Specifically, they each month for each model variation:

• Double sort stocks first on next-month predicted volatility and then on next-month predicted return.
• Reform portfolios that are long (short) the tenth, or decile, of stocks with the best (worst) expected returns.

They also consider single-variable sorts for benchmarking. They focus on gross Sharpe ratio of resulting portfolios as the key performance statistic. Using monthly data as specified for a broad sample of U.S. stocks, excluding those priced under $1.00 or with market capitalizations below $5 million, during January 1991 through December 2020 (18,916 stocks, with some data grooming), they find that:

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In response to “Shorting VXX with Crash Protection”, which investigates shorting iPath S&P 500 VIX Short-Term Futures (VXX) to capture the equity volatility risk premium, a subscriber asked about instead using a long position in ProShares Short VIX Short-Term Futures (SVXY). To investigate, we consider two scenarios based on monthly measurements:

1. Buy and Hold – buy an initial amount of SVXY and let this position ride indefinitely. This is a long-term investment strategy.
2. Monthly Skim – buy the same initial amount of SVXY and move to SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) any month-end gains over the initial investment (the beginning-of-month SVXY position may become smaller, but not larger, than the initial investment). This is an income-generating investment strategy.

The offeror changed the SVXY investment objective at the end of February 2018 (when short VIX strategies crashed), more conservatively targeting henceforth -0.5 times the daily performance of the S&P 500 VIX Short-Term Futures Index rather than -1.0 times as before. We therefore examine SVXY performance separately before and after that change. We assume o.2% SVXY-BIL switching frictions in scenario 2. Using monthly adjusted closing prices for SVXY and BIL during October 2011 through February 2025, we find that: Keep Reading

Do leveraged exchange-traded funds (LETF) with daily leverage resets reliably fall behind portfolios with the same initial leverage but no resets? In his February 2025 paper entitled “Multi-day Return Properties of Leveraged Index ETFs”, Baolian Wang compares the multi-day return properties of leveraged, daily reset LETFs to those of matched initial leverage positions with no resets. He estimates reset costs for the former and margin financing costs (based on the overnight Effective Federal Funds Rate) for the latter. Specifically, he considers LETFs for different combinations of:

• S&P 500 Index, Nasdaq 100 Index, Bloomberg U.S. Treasury 7-10 Year Index, MSCI EAFE Index and MSCI Emerging Markets Index as underlying assets.
• Holding intervals of 5, 10, 21, 63 and 252 trading days.
• Leverage multiples of 2x, 3x, -1x, -2x and -3x.

For each combination, he simulates 1,000 paths of daily returns using random draws of historical returns. For each path, he calculates holding interval returns for the daily reset and no reset alternatives, and their difference. Using daily total return data for the specified indexes and the overnight Effective Federal Funds Rate from respective index inceptions through June 2023, he finds that: Keep Reading