Volatility Effects

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

• First, diversifying with monthly rebalancing of:
  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.
  3. ProShares Short VIX Short-Term Futures (SVXY), to capture part of the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures. SVXY has a change in investment objective at the end of February 2018 (see “Using SVXY to Capture the Volatility Risk Premium”).
• 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 risky assets. We execute 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 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, GDXJ and SVXY adjusted for splits and dividends and contemporaneous T-bill yield during May 2015 (limited by GBTC) through June 2019, we find that:

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How should investors evaluate the effectiveness of a safe haven asset? In their July 2020 paper entitled “A Safe Haven Index”, Dirk Baur and Thomas Dimpfl devise and apply a safe haven index (SHI) to evaluate over 20 individual potential safe haven assets. SHI consists of seven equal-weighted assets: gold, Swiss franc, Japanese yen, 2-year, 10-year and 30-year U.S. Treasuries and 10-year German government bonds. For evaluations, they focus on four safe haven events: the October 1987 stock market crash, the September 2001 terrorist attacks, the September 2008 Lehman collapse and the March 2020 COVID-19 pandemic. Using daily data for index components and other potential safe haven assets as available during January 1985 through May 2020, they find that:

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What are rational uses of leveraged and inverse exchange-traded products (ETP), which offer easy access to amplified positions in various benchmark indexes spanning stocks, bonds, commodities and volatility? In their April 2020 paper entitled “Levered and Inverse ETPs: Blessing or Curse?”, Colby Pessina and Robert Whaley review the mechanics of leveraged and inverse ETPs, simulate their expected performance of those based on six popular benchmarks and document actual performance of 35 ETPs. They employ Monte Carlo simulations assuming normally distributed log returns for underlying indexes, with mean and standard deviation estimates based on historical daily returns during December 20, 2005 through March 13, 2020. Using simulation inputs as specified and data for 35 actual ETPs as available through mid-March 2020, they find that:

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Are there equity styles that tend to perform relatively well during and after stock market crashes? In their April 2020 paper entitled “Equity Styles and the Spanish Flu”, Guido Baltussen and Pim van Vliet examine equity style returns around the Spanish Flu pandemic of 1918-1919 and five earlier deep U.S. stock market corrections (-20% to -25%) in 1907, 1903, 1893, 1884 and 1873. They construct three factors by:

1. Separating stocks into halves based on market capitalization.
2. Sorting the big half only into thirds based on dividend yield as a value proxy, 36-month past volatility or return from 12 months ago to one month ago. They focus on big stocks to avoid illiquidity concerns for the small half.
3. Forming long-only, capitalization-weighted factor portfolios that hold the third of big stocks with the highest dividends (HighDiv), lowest past volatilities (Lowvol) or highest past returns (Mom).

They also test a multi-style strategy combining Lowvol, Mom and HighDiv criteria (Lowvol+) and a size factor calculated as capitalization-weighted returns for the small group (Small). Using data for all listed U.S. stocks during the selected crashes, they find that: Keep Reading

Does shorting the iPath S&P 500 VIX Short-Term Futures ETN (VXX) with crash protection (attempting 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 March 2020, we find that: Keep Reading

Low-risk investment strategies buy or overweight low-risk assets and sell or underweight high-risk assets. Growth in low-risk investing is stimulating much pro and con debate in the financial community. Which assertions are valid, and which are not? In their February 2020 paper entitled “Fact and Fiction about Low-Risk Investing”, Ron Alquist, Andrea Frazzini, Antti Ilmanen and Lasse Pedersen identify five facts and five fictions about low-risk investing. They employ long-short U.S. stock portfolio strategies to illustrate relative performance of low-risk versus high-risk assets. They consider six statistical and four fundamental risk metrics, emphasizing differences between dollar-neutral and market-neutral strategy designs. Focusing on a few prominent low-risk metrics, they compare performances of low-risk strategies to those based on conventional size, value, profitability, investment and momentum factors. Using daily returns for U.S. stocks since January 1931 and firm fundamental data since January 1957, all through August 2019, they find that:

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Findings in “Add Stop-gain to Asset Class Momentum Strategy?” suggest that systematic capture of upside volatility may enhance the base strategy. Does this conclusion hold for a simpler application to a single liquid asset over a longer sample period? To investigate, we apply a stop-gain rule to SPDR S&P 500 (SPY) that: (1) exits SPY if its intra-month return exceeds a specified threshold (sacrificing any dividend paid that month); and, (2) re-enters SPY at the end of the month. We also look at a corresponding stop-loss rule. Using monthly unadjusted highs, lows and closes (for stop-gain and stop-loss calculations) and dividend-adjusted closes (for return calculations) for SPY during February 1993 through February 2020, we find that:

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“Monthly Rebalanced Shorting of Leveraged ETF Pairs” finds that shorting some pairs of leveraged ETFs may be attractive. How has the strategy worked recently and how sensitive are findings to execution costs? To investigate, we consider three pairs of monthly reset equal short positions in:

1. ProShares Ultra S&P500 (SSO) and ProShares UltraShort S&P500 (SDS)
2. ProShares UltraPro S&P500 (UPRO) and ProShares UltraPro Short S&P500 (SPXU)
3. ProShares UltraPro QQQ (TQQQ) and ProShares UltraPro Short QQQ (SQQQ)

We take initially, and at the end of each month renew, a -$100,000 short position in each pair member. This strategy generates an initial $200,000 cash in the portfolio and subsequently adds to or subtracts from this cash monthly based on short position performance. We initially assume return on cash covers any costs (transaction fees, bid/ask spread and interest on borrowed positions), but then test sensitivity to net carrying cost. Using monthly adjusted closes for these ETFs from respective inceptions through January 2020, we find that: Keep Reading

Has growth in futures-based exchange-traded funds (ETF) predictably affected pricing of underlying assets? In his November 2019 paper entitled “Passive Funds Actively Affect Prices: Evidence from the Largest ETF Markets”, Karamfil Todorov investigates impacts of ETF trading on pricing of futures on equity volatility (VIX) and commodities, the two asset classes most dominated by ETFs. He decomposes sources of these impacts into three rebalancing needs: (1) rolling of futures contracts as they expire; (2) inflow/outflow of investor funds; and, (3) maintenance of constant daily leverage. By modeling the fundamental value of VIX futures contracts using S&P 500 Index and VIX option prices, he quantifies non-fundamental ETF rebalancing impacts on VIX futures prices. Finally, he tests a strategy to exploit the need for daily leverage rebalancing by trading against it. Specifically, he approximates daily liquidity provision by each intraday reforming portfolios that short a pair of long and short futures-based ETFs on the same underlying asset (volatility, natural gas, gold or silver). In other words, he shorts at the open and covers at the close each day. Using daily data for selected ETFs and their underlying futures for VIX, U.S. natural gas, silver, gold and oil as available during January 2000 through December 2018, he finds that: Keep Reading

Does return distribution skewness predict relative performance of assets across asset classes? In their December 2019 paper entitled “Cross-Asset Skew”, Nick Baltas and Gabriel Salinas investigate realized skewness as a relative return predictor within and across four asset classes (equity indexes, government bonds, currencies and commodities). Specifically, at the end of each month, they:

1. For each asset, measure skewness using daily returns over the last 12 months.
2. Within each asset class, rank assets by skewness and reform a skewness portfolio that is long rank-weighted assets with relatively low (most negative) skewnesses and short those with relatively high (least negative or positive) skewnesses, with equal dollars allocated to the long and short sides.
3. Scale each asset class skewness portfolio to full-sample volatility of 10%, and reform a Global Skewness Factor (GSF) portfolio that equally weights these scaled asset class portfolios.

Using daily returns for 19 equity index futures, 9 government bond futures, 9 currency forwards and 24 commodity futures series, along with monthly value, momentum and carry factor returns, during January 1990 through December 2017, they find that: Keep Reading