Volatility Effects

Can investors exploit the volatility risk premium to improve the hedging performance of S&P 500 Implied Volatility Index (VIX) futures? In his November 2019 paper entitled “Portfolio Strategies for Volatility Investing”, Jim Campasano tests an Enhanced Portfolio strategy which dynamically allocates to the S&P 500 Index and a position in the two nearest VIX futures re-weighted daily to maintain constant 30 days to maturity (VIX₃₀). He specifies the volatility risk premium as VIX₃₀ minus VIX. The Enhanced Portfolio holds a long (short) position in VIX₃₀ when this premium is negative (positive). Within this portfolio, he each day weights the S&P 500 Index and VIX₃₀ so that they have the same expected volatility per predictive regressions starting January 2007. He imposes a 1-day lag between calculations of VIX₃₀ direction/portfolio weights and trading to ensure availability of all inputs. As benchmarks, because of their interactions with the volatility risk premium, he considers three variations of the CBOE S&P 500 BuyWrite Index (BXM, BXY and BXMD), the CBOE S&P 500 PutWrite Index (PUT), a call writing strategy that sells calls only when VIX is above its historical median (COND) and a delta-hedged covered call strategy (RM). He further considers three variants of his Enhanced Portfolio: (1) EnhancedLong holds the S&P 500 Index (Enhanced Portfolio) when the VIX premium is positive (negative); (2) EnhancedShort holds the S&P 500 Index (Enhanced Portfolio ) when the VIX premium is negative (positive); and, (3) Enhanced90 adjusts allocations so that the S&P 500 Index has 90% of expected portfolio volatility. Using the specified daily data during January 2007 through December 2017, he finds that:

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Do both the long and short sides of portfolios used to quantify widely accepted equity factors benefit investors? In their November 2019 paper entitled “When Equity Factors Drop Their Shorts”, David Blitz, Guido Baltussen and Pim van Vliet decompose and analyze gross performances of long and short sides of U.S. value, momentum, profitability, investment and low-volatility equity factor portfolios. The employ 2×3 portfolios, segmenting first by market capitalization into halves and then by selected factor variables into thirds. The extreme third with the higher (lower) expected return constitutes the long (short) side of a factor portfolio. When looking at just the long (short) side of factor portfolios, they hedge market beta via a short (long) position in liquid derivatives on a broad market index. Using monthly returns for the specified 2×3 portfolios during July 1963 through December 2018, they find that:

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How does a shift in emphasis from active to passive investing affect the financial market risk landscape? In their September 2019 paper entitled “The Shift From Active to Passive Investing: Potential Risks to Financial Stability?”, Kenechukwu Anadu, Mathias Kruttli, Patrick McCabe, Emilio Osambela and Chaehee Shin analyze how a shift from active to passive investing affects:

1. Investment fund redemption liquidity risks.
2. Market volatility.
3. Asset management industry concentration.
4. Co-movement of asset returns and liquidity.

They also assess how effects are likely to evolve if the active-to-passive shift continues. Based on their framework/analysis, they conclude that: Keep Reading

What is the best way to avoid stock momentum portfolio crashes? In her July 2019 paper entitled “Momentum with Volatility Timing”, Yulia Malitskaia tests a long-only volatility-timed stock momentum strategy that exits holdings when strategy volatility over a past interval exceeds a specified threshold. She focuses on a recent U.S. sample that includes the 2008-2009 market crash and its aftermath. She considers the following momentum portfolios:

• WML10 – each month long (short) the tenth, or decile, of stocks with the highest (lowest) returns from 12 months ago to one month ago.
• W10 and L10 – WML10 winner and loser sides separately.
• WML10-Scaled – adjusts WML10 exposure according to the ratio of a volatility target to actual WML10 annualized daily volatility over the past six months. This approach seeks to mitigate poor returns when WML10 volatility is unusually high.
• W10-Timed – holds W10 (cash, with zero return) when W10 volatility over the past six months is below (at or above) a specified threshold. This approach seeks to avoid poor post-crash, loser-driven WML10 performance and poor W10  performance during crashes.

She performs robustness tests on  MSCI developed and emerging markets risk-adjusted momentum indexes. Using daily and monthly returns for W10 and L10 portfolios since 1980 and for MSCI momentum indexes since 2000, all through 2018, she finds that:

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What are the essential points from the stream of research on low-volatility investing? In their August 2019 paper entitled “The Volatility Effect Revisited”, David Blitz, Pim van Vliet and Guido Baltussen provide an overview of the low-volatility (or as they prefer, low-risk) effect, the empirical finding in stock markets worldwide and within other asset classes that higher risk is not rewarded with higher return. Specifically, they review:

• Empirical evidence for the effect.
• Whether other factors, such as value, explain the effect.
• Key considerations in exploiting the effect.
• Whether the effect is fading due to market adaptation.

Based on findings and interpretations on low-risk investing published since the 1970s, they conclude that: Keep Reading

What is the best risk management approach for a conventional stock momentum strategy? In their August 2019 paper entitled “Enhanced Momentum Strategies”, Matthias Hanauer and Steffen Windmueller compare performances of several stock momentum strategy risk management approaches proposed in prior research. They use the momentum factor, returns to a monthly reformed long-short portfolio that integrates average returns from 12 months ago to two months ago with market capitalization, as their base momentum strategy (MOM). They consider five risk management approaches:

1. Constant volatility scaling with 6-month lookback (cvol_6M) – scales the base momentum portfolio to a constant target volatility (full sample volatility of the base strategy) using volatility forecasts from daily momentum returns over the previous six months (126 trading days).
2. Constant volatility scaling with 1-month lookback (cvol_1M) – same as cvol_6M, but with volatility forecasts from daily momentum returns over the previous month (21 trading days).
3. Dynamic volatility scaling estimated in-sample (dyn_IS) – enhances constant volatility scaling by also forecasting momentum portfolio returns based on market return over the past two years using the full sample (with look-ahead bias).
4. Dynamic volatility scaling estimated out-of-sample (dyn) – same as dyn_IS, but with momentum portfolio return forecasts from the inception-to-date market subsample.
5. Idiosyncratic momentum (iMOM) – sorts stocks based on their residuals from monthly regressions versus market, size and value factors from 12 months ago to one month ago (rather than their raw returns) and scales residuals by monthly volatility of residuals over this same lookback interval. 

They evaluate momentum risk management strategies based on: widely used return and risk metrics; competition within a mean-variance optimization framework; and, breakeven portfolio reformation frictions. Using monthly and daily returns in U.S. dollars for U.S. common stocks since July 1926 and for common stocks from 48 international markets since July 1987 (July 1994 for emerging markets), all through December 2017, they find that: Keep Reading

Should investors consider allocations to products that track equity volatility indexes? In her July 2019 paper entitled “Challenges of Indexation in S&P 500 Index Volatility Investment Strategies”, Margaret Sundberg examines whether behaviors of S&P 500 Index option-based volatility indexes justify treatment of volatility as an asset class. To assess potential strategies, she employs the following indexes:

• S&P 500 Index
• S&P 500 Total Return Index
• S&P 500 Option-implied volatility index (VIX)
• S&P 500 VIX Short-Term Futures Index
• S&P 500 VIX Short-Term Futures Inverse Index
• S&P 500 Iron Condor Index
• S&P 500 PutWrite Index

Using daily time series for these indexes during April 2008 through March 2019, she finds that: Keep Reading

In their July 2019 paper entitled “Momentum-Managed Equity Factors”, Volker Flögel, Christian Schlag and Claudia Zunft test exploitation of positive first-order autocorrelation (time series, absolute or intrinsic momentum) in monthly excess returns of seven equity factor portfolios:

1. Market (MKT).
2. Size – small minus big market capitalizations (SMB).
3. Value – high minus low book-to-market ratios (HML).
4. Momentum – winners minus losers (WML)
5. Investment – conservative minus aggressive (CMA).
6. Operating profitability – robust minus weak (RMW).
7. Volatility – stable minus volatile (SMV).

For factors 2-7, monthly returns derive from portfolios that are long (short) the value-weighted fifth of stocks with the highest (lowest) expected returns. In general, factor momentum timing means each month scaling investment in a factor from 0 to 1 according its how high its last-month excess return is relative to an inception-to-date window of past levels. They consider also two variations that smooth the simple timing signal to suppress the incremental trading that it drives. In assessing costs of this incremental trading, they assume (based on other papers) that realistic one-way trading frictions are in the range 0.1% to 0.5%. Using monthly data for a broad sample of U.S. common stocks during July 1963 through November 2014, they find that: Keep Reading

How reliable and variable are the most widely accepted long-short factor premiums across asset classes? Can investors time factor premium? In their June 2019 paper entitled “Factor Premia and Factor Timing: A Century of Evidence”, Antti Ilmanen, Ronen Israel, Tobias Moskowitz, Ashwin Thapar and Franklin Wang examine multi-class robustness of and variation in four prominent factor premiums:

1. Value – book-to-market ratio for individual stocks; value-weighted aggregate cyclically-adjusted price-to-earnings ratio (P/E10) for stock indexes; 10-year real yield for bonds; deviation from purchasing power parity for currencies; and, negative 5-year change in spot price for commodities.
2. Momentum – past excess (relative to cash) return from 13 months ago to one month ago.
3. Carry – front-month futures-to-spot ratio for equity indexes since 1990 and excess dividend yield before 1990; difference in short-term interest rates for currencies; 10-year minus 3-month yields for bonds; and, percentage difference in prices between the nearest and next-nearest contracts for commodities.
4. Defensive – for equity indexes and bonds, betas from 36-month rolling regressions of asset returns versus equal-weighted returns of all countries; and, no defensive strategies for currencies and commodities because market returns are difficult to define.

They each month rank each asset (with a 1-month lag for conservative execution) on each factor and form a portfolio that is long (short) assets with the highest (lowest) expected returns, weighted according to zero-sum rank. When combining factor portfolios across factors or asset classes, they weight them by inverse portfolio standard deviation of returns over the past 36 months. To assess both overfitting and market adaptation, they split each factor sample into pre-discovery subperiod, original discovery subperiod and post-publication subperiod. They consider factor premium interactions with economic variables (business cycles, growth and interest rates), political risk, volatility, downside risk, tail risk, crashes, market liquidity and investment sentiment. Finally, they test factor timing strategies based on 12 timing signals based on 19 methodologies across six asset classes and four factors. Using data as available from as far back as February 1877 for 43 country equity indexes, 26 government bonds, 44 exchange rates and 40 commodities, all through 2017, they find that: Keep Reading

Do equity strategy portfolios characterized by aggressive (conservative) scaling when portfolio volatility is recently low (high) reliably beat unmanaged performance? In their March 2019 paper entitled “On the Performance of Volatility-Managed Portfolios”, Scott Cederburg, Michael O’Doherty, Feifei Wang and Xuemin Yan assess whether practical volatility management is systematically attractive. For each of 103 anomalies (nine widely used factors and 94 other published anomalies), they construct a hedge portfolio that is each month long (short) the value-weighted tenth of stocks with the highest (lowest) expected returns. They then construct volatility-managed versions of these portfolios based on inverse variance of daily portfolio returns the prior month. Focusing on gross Sharpe ratio, they compare head-to-head performances of volatility-managed portfolios and unmanaged counterparts. Focusing on gross Sharpe ratio and certainty equivalent return (CER), they also employ an historical training subsample to estimate mean-variance optimal allocations for: (1) a strategy that chooses among a given volatility-managed portfolio, its unmanaged counterpart and a risk-free asset; and, (2) a strategy chooses between only the unmanaged counterpart and the risk-free asset. Using daily returns for the 103 equity hedge portfolios, they find that:

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