Volatility Effects

Can investors profitably trade the effects of leveraged and inverse exchange-traded funds (LETF) share creation and redemption on prices? In his July 2025 paper entitled “Am I the Patsy? LETF Issuance is Signal, Not Noise: How Trading LETFs a Day Late can make you a Dollar Richer”, Rob Bezdjian introduces the “Day Late-Dollar Richer” (DLDR) strategy, which exploits LETF share creation and redemption behaviors. Issuers of LETFs must, in aggregate, overprice created shares and underprice redeemed shares to remain solvent. DLDR therefore uses LETF share data (typically released by 8:00PM ET) to trade opposite issuers at the next close, as follows:

• If the number of shares increases, sell or short at the next close.
• If the number of shares decreases, buy or close short at the next close.
• If the number of shares is unchanged, do not trade.

Testing assumes trades occur at net asset values (NAV) with opening trade sizes equal to changes in number of shares. Applying DLDR as modeled to four volatility and eight commodities ProShares LETFs during January 2015 (or inception) through mid-July 2025, he finds that:

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Does asymmetry in the design/performances of leveraged and inverse exchange-traded products create a reliable edge? In his July 2025 paper entitled “Deception by Design: Leveraged ETFs, Structural Fraud, and Proof of Outperformance”, Rob Bezdjian analyzes the general construction and performance of leveraged and inverse exchange-traded funds (ETF). He then devises and tests a (“Handsome Rob”) strategy that shorts related pairs of leveraged:inverse ETFs at a 1:1.5 ratio, rebalanced quarterly, with a top-up rule that amplifies exposure only after price declines. Using mathematical models and historical data for six pairs of leveraged and inverse ETFs from the first quarter of 2016 through the second quarter of 2025, he finds that: Keep Reading

Can investors safely capture the U.S. stock market volatility risk premium (VRP), the tendency of implied volatility to exceed realized volatility. In their June 2025 paper entitled “The Volatility Edge, A Dual Approach For VIX ETNs Trading”, Carlo Zarattini, Andrew Aziz and Antonio Mele concisely review the history of volatility trading. They then investigate whether investors can capture some of VRP via the following four progressively constructed strategies (table from the paper):

• Strategy 1 is a benchmark, a continuous 20% position in VIXSHORT (modeled from the S&P 500 VIX Short-Term Futures Inverse Daily Index with 0.80% annual fee). The remaining 80% is in cash with no earned interest. Rebalancing occurs via market-on-close (MOC) trades whenever the VIXSHORT allocation drifts from 20% by at least 2% as measured daily at 3:45pm, with 0.05% trading frictions (Tcost).
• Strategy 2 is the same as Strategy 1, except the VIXSHORT allocation is made only when the expected VRP (eVRP) is positive when measured daily at 3:45pm as VIX minus the annualized standard deviation of 10-day (3:45pm) SPDR S&P 500 ETF (SPY) returns.
• Strategy 3 adds a signal to Strategy 2, with allocations to VIXSHORT or VIXLONG (modeled from S&P 500 VIX Short-Term Futures Index Total Return with 0.50% annual fee), depending on whether the 3:45pm VIX term structure is in backwardation or contango (BoC), meaning that 90-day implied volatility (VIX3M) is less than or greater than VIX.
• Strategy 4 replaces the fixed allocations in Strategy 3 with allocations based on the 3:45pm level of VIX.

Using daily and 1-minute intraday data for the specified input variables during January 2005 through May 2025, they find that:

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Given the body of research on the outperformance of low-risk stocks, should the equity asset pricing community add a low-volatility factor in standard models of stock returns? In their June 2025 paper entitled “Factoring in the Low-Volatility Factor”, Amar Soebhag, Guido Baltussen and Pim van Vliet investigate adding a low-volatility factor to standard models via four scenarios:

1. Gross (frictionless) returns for long-minus-short portfolios for all factors as conventionally done in prior factor model research.
2. Gross returns for market-hedged long and short legs as separate aspects of all factors.
3. Net returns approximated from estimated bid-ask spreads and shorting fees for separate market-hedged long and short legs of all factors.
4. Net returns for only the long legs of all factors.

They compute stock volatilities based on a rolling window of 252 trading days for low-volatility factor calculations. They compare models by weighting their respective factors at each rebalancing to achieve maximum test period Sharpe ratio. Using firm/stock data for U.S. common stocks with positive book-to-market ratios to construct long-minus-short and long or short factor returns for well-known asset pricing models, and estimated trading frictions and shorting costs, during January 1970 through December 2023, they find that:

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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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