Volatility Effects

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

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Does implied stock market volatility (IV) predict stock market returns? In their March 2018 paper entitled “Implied Volatility Measures As Indicators of Future Market Returns”, Roberto Bandelli and Wenye Wang analyze the relationship between S&P 500 Index IV and future S&P 500 Index returns. They consider volatilities implied either by S&P 500 Index options (VIX) or by 30-day at-the-money S&P 500 Index straddles. Specifically, they each day:

1. Rank current S&P 500 Index IV according to ranked tenth (decile) of its daily distribution over the past two years. If current IV is higher than any value of IV over the past two years, its rank is 11.
2. Calculate S&P 500 Index returns over the next one, five and 20 trading days.
3. Relate these returns to IV rank.

They calculate statistical significance based on the difference between the average IV-ranked log returns and log returns over all intervals of the same length. Using daily data for the selected variables during December 1991 through November 2017, they find that: Keep Reading

Is there an easy way for investors to capture jointly the most reliable stock return factor premiums? In their March 2018 paper entitled “The Conservative Formula: Quantitative Investing Made Easy”, Pim van Vliet and David Blitz propose a stock selection strategy based on low return volatility, high net payout yield and strong price momentum. Specifically, at the end of each quarter they:

1. Segment the then-current 1,000 largest stocks into 500 with the lowest and 500 with the highest 36-month return volatilities.
2. Within each segment, rank stocks based on total net payout yield (NPY), calculated as dividend yield minus change in shares outstanding divided by its 24-month moving average.
3. Within each segment, rank stocks based on return from 12 months ago to one month ago (with the skip-month intended to avoid return reversals).
4. Within the low-volatility segment, average the momentum and NPY ranks for each stock and equally weight the top 100 to reform the Conservative Formula portfolio.
5. Within the high-volatility segment, average the momentum and NPY ranks for each stock and equally weight the bottom 100 to reform the Speculative Formula portfolio.

Limiting the stock universe to the top 1,000 based on market capitalization suppresses liquidity risk. Limiting screening parameters to three intensely studied factors that require no accounting data mitigates data snooping and data availability risks. They focus on the 1,000 largest U.S. stocks to test a long sample, but also consider the next 1,000 U.S. stocks (mid-caps) and the 1,000 largest stocks from each of Europe, Japan and emerging markets. They further examine: (1) sensitivity to economic conditions doe the long U.S. sample; and, (2) impact of trading frictions in the range 0.1%-0.3% for developed markets and 0.2%-0.6% for emerging markets. Using quarterly prices, dividends and shares outstanding for the contemporaneously largest 1,000 U.S. stocks since 1926, European and Japanese stocks since 1986 and emerging markets stocks since 1991, all through 2016, they find that:

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Is shorting pairs of leveraged exchange-traded funds (ETF) reliably profitable? In their December 2017 paper entitled “Shorting Leveraged ETF Pairs”, Christopher Hessel, Jouahn Nam, Jun Wang, Xing Cunyu and Ge Zhang examine monthly returns from shorting a pair of leveraged and inverse leveraged ETFs for the same index. They first investigate what circumstances make this strategy profitable. They then test the strategy on each of the triple/inverse triple (3X/-3X) pairs associated with the following six base ETFs: Financial Select Sector SPDR (XLF: FAS/FAZ), Powershares QQQ (QQQ: TQQQ/SQQQ), iShares Russell 2000 Index (IWM: TNA/TZA), SPDR S&P 500 (SPY: UPRO/SPXU), VanEck Vectors Junior Gold Miners ETF (GDXJ: JNUG/JDST) and Energy Select Sector SPDR (XLE: ERX/ERY). Their analysis assumes rebalancing pair short positions to equal value at the end of each month and holding them to the end of the next month. Using monthly data for the selected leveraged ETFs from the end of 2007 (except the end of November 2009 for the leveraged versions of GDXJ) through the end of December 2016, they find that: Keep Reading

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

• Diversifying (based on pairwise correlations) by combining: (1) Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins; (2) VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold; and, (3) ProShares Short VIX Short-Term Futures (SVXY), to capture the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures.
• Capturing upside volatility and managing portfolio drawdown via monthly rebalancing and gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets and $0 to cash. If the risky assets have a month-end combined value less than the combined initial allocations, we rebalance them to equal weights for next month. If the risky assets have a combined month-end value greater than the combined initial allocations, we rebalance to the initial allocations and move the excess permanently (skim) to cash. We assume monthly portfolio reformation frictions of 2% of month-end combined values of risky assets. We assume accrued, skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly adjusted values of GBTC, GDXJ and SVXY and contemporaneous T-bill yield during May 2015 (limited by GBTC) through mid-February 2018, we 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) with crash protection to capture the equity volatility risk premium safely, a subscriber asked about instead using a long position in ProShares Short VIX Short-Term Futures (SVXY). To investigate, we consider three scenarios based on monthly measurements:

1. Buy and Hold – buying an initial amount of SVXY and letting this position ride indefinitely.
2. Monthly Skim – buying the same initial amount of SVXY and transferring to cash any month-end gains exceeding the initial amount (so the beginning-of-month SVXY position may become smaller, but not larger, than the initial investment).
3. Prior Month Positive – starting with the same initial amount each month, holding SVXY (cash) when the prior-month SVXY return is positive (negative).

We assume trading frictions of 0.25% for movements of funds between SVXY and cash in scenarios 1 and 2. We assume return on cash is the 3-month U.S. Treasury bill (T-bill) yield. Using monthly split-adjusted closing prices for SVXY and contemporaneous T-bill yield during October 2011 through early February 2018 (only 78 months, with February 2018 partial), we find that: Keep Reading

Does shorting the iPath S&P 500 VIX Short-Term Futures ETN (VXX) with crash protection (to capture the equity volatility risk premium safely) work? To investigate, we apply crash protection rules to three VXX shorting scenarios:

1. Let It Ride – shorting an initial amount of VXX and letting this position ride indefinitely.
2. Fixed Reset – shorting a fixed amount of VXX and continually resetting this fixed position (so the short position does not become very small or very large).
3. Gain/Loss Adjusted – shorting an initial amount of VXX and adjusting the size of the short position according to periodic gains/losses.

We consider two simple monthly crash protection rules based on the assumption that volatility changes are somewhat persistent, as follows:

• Prior Month Positive Rule – short VXX (go to cash) when the prior-month short VXX return is positive (negative).
• Prior Week Positive Rule – short VXX (go to cash) when the prior-week short VXX return is positive (negative).

For tractability, we ignore trading frictions, costs of shorting and return on retained cash from shorting gains. Using monthly closes for the S&P 500 Volatility Index (VIX) and monthly and weekly reverse split-adjusted closing prices for VXX from February 2009 through early February 2018 (110 months), we find that: Keep Reading

Do strategies that seek to exploit return volatility persistence by adjusting stock market exposure inversely with recent market volatility relative to some target (including exposures greater than 100%) produce obvious benefits for investors? In their November 2017 paper entitled “Tail Risk Mitigation with Managed Volatility Strategies”, Anna Dreyer and Stefan Hubrich examine usefulness of managing volatility in this way as applied to the S&P 500 Index over a long sample period and across a range of performance measurements. They use daily index returns in excess of the return on cash and rebalance stock index-cash test portfolios daily. Their target volatility is variable, set as the inception-to-date realized daily excess return volatility. They assess robustness across different sample subperiods, past volatility measurement intervals and portfolio holding intervals. They measure portfolio performance conventionally (Sharpe ratio), via effects on portfolio return distribution skewness and kurtosis (as an indicator of tail risk) and with investor utility metrics. Using daily excess returns for the S&P 500 Index during July 1926 through November 2016, they find that:

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What is the best way to think about reliabilities and risks of various anomaly premiums commonly that investors believe to be available for exploitation? In their December 2017 paper entitled “A Framework for Risk Premia Investing”, Kari Vatanen and Antti Suhonen present a framework for categorizing widely accepted anomaly premiums to facilitate construction of balanced investment strategies. They first categorize each premium as fundamental, behavioral or structural based on its robustness as indicated by clarity, economic rationale and capacity. They then designate each premium in each category as either defensive or offensive depending on whether it is feasible as long-only or requires short-selling and leverage, and on its return skewness and tail risk. Based on expected robustness and riskiness of selected premiums as described in the body of research, they conclude that: Keep Reading

What is the best way to implement futures momentum and manage its risk? In their November 2017 paper entitled “Risk Adjusted Momentum Strategies: A Comparison between Constant and Dynamic Volatility Scaling Approaches”, Minyou Fan, Youwei Li and Jiadong Liu compare performances of five futures momentum strategies and two benchmarks:

1. Cross-sectional, or relative, momentum (XSMOM) – each month long (short) the equally weighted tenth of futures contract series with the highest (lowest) returns over the past six months.
2. XSMOM with constant volatility scaling (CVS) – each month scales the XSMOM portfolio by the ratio of a 12% target volatility to annualized realized standard deviation of daily XSMOM portfolio returns over the past six months.
3. XSMOM with dynamic volatility scaling (DVS) – each month scales the XSMOM portfolio by the the ratio of next-month expected market return (a function of realized portfolio volatility and whether MSCI return over the last 24 months is positive or negative) to realized variance of XSMOM portfolio daily returns over the past six months.
4. Time-series, or intrinsic, momentum (TSMOM) – each month long (short) the equally weighted futures contract series with positive (negative) returns over the past six months.
5. TSMOM with time-varying volatility scaling (TSMOM Scaled) – each month scales the TSMOM portfolio by the ratio of 22.6% (the volatility of an equally weighted portfolio of all future series) to annualized exponentially weighted variance of TSMOM returns over the past six months.
6. Equally weighted, monthly rebalanced portfolio of all futures contract series (Buy-and-Hold).
7. Buy-and-Hold with time-varying volatility scaling (Buy-and-Hold Scaled) – each month scales the Buy-and-Hold portfolio as for TSMOM Scaled.

They test these strategies on a multi-class universe of 55 global liquid futures contract series, starting when at least 45 series are available in November 1991. They focus on average annualized gross return, annualized volatility, annualized gross Sharpe ratio, cumulative return and maximum (peak-to-trough) drawdown (MaxDD) as comparison metrics. Using monthly prices for the 55 futures contract series (24 commodities, 13 government bonds, 9 currencies and 9 equity indexes) during June 1986 through May 2017, they find that:

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