Volatility Effects

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

Can investors use leveraged exchange-traded funds (ETF) to construct attractive versions of simple 60%/40% (60/40) and 40%/60% (40/60) stocks-bonds portfolios? In their March 2020 presentation package entitled “Robust Leveraged ETF Portfolios Extending Classic 40/60 Portfolios and Portfolio Insurance”, flagged by a subscriber, Mikhail Smirnov and Alexander Smirnov consider several variations of classic stocks/bonds portfolios implemented with leveraged ETFs. They ultimately focus on a monthly rebalanced partially 3X-leveraged portfolio consisting of:

• 40% ProShares UltraPro QQQ (TQQQ)
• 20% Direxion Daily 20+ Year Treasury Bull 3X Shares (TMF)
• 40% iShares 20+ Year Treasury Bond ETF (TLT)

To validate findings, we consider this portfolio and several 60/40 and 40/60 stocks/bonds portfolios. We look at net monthly performance statistics, along with compound annual growth rate (CAGR), maximum drawdown (MaxDD) based on monthly data and annual Sharpe ratio. To estimate monthly rebalancing frictions, we use 0.5% of amount traded each month. We use average monthly 3-month U.S. Treasury bill yield during a year as the risk-free rate in Sharpe ratio calculations for that year. Using monthly adjusted prices for TQQQ, TMF, TLT and for SPDR S&P 500 ETF Trust (SPY) and Invesco QQQ Trust (QQQ) to construct benchmarks during February 2010 (limited by TQQQ inception) through February 2025, we find that: Keep Reading

How might an investor construct a portfolio of very risky assets? To investigate, we revisit ideas first considered five years ago:

• First, diversify with monthly rebalancing of (the GBTC portfolio):
  1. Grayscale Bitcoin Trust ETF (GBTC), an indirect Bitcoin holding.
  2. SPDR Gold Shares (GLD), an indirect gold holding.
  3. ProShares Short VIX Short-Term Futures ETF (SVXY), to capture part of the U.S. stock market volatility risk premium by shorting short-term VIX futures.
• Second, capturing upside volatility and managing drawdown of this portfolio via gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three assets. We perform a monthly skim as follows: (1) if the risky assets have month-end combined value less than combined initial allocations ($30,000), we rebalance to equal weights for next month; or, (2) if the risky assets have combined month-end value greater than combined initial allocations, we rebalance to initial allocations and move the excess permanently (skim) to cash. We very conservatively assume monthly portfolio reformation frictions of 1% of month-end combined value of risky assets. We assume accrued skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly prices of GBTC, GLD and SVXY adjusted for splits/dividends and contemporaneous T-bill yield during May 2015 (limited by GBTC) through January 2025, we find that:

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