Volatility Effects

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

“Identifying VXX/SVXY Tendencies” finds that S&P 500 implied volatility index (VIX) futures roll return, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, may be a useful predictor of iPath S&P 500 VIX Short-Term Futures ETN (VXX) and ProShares Short VIX Short-Term Futures ETF (SVXY) returns. VXX and SVXY target 1X daily performance for VXX and -0.5X for SVXY relative to the S&P 500 VIX Short-Term Futures Index. Is there a way to exploit this predictive power? To investigate, we compare performances of:

1. SVXY B&H – buying and holding SVXY.
2. SVXY-Cash – holding SVXY (cash) when prior-day roll return is negative (zero or positive).
3. SVXY-VXX – holding SVXY (VXX) when prior-day roll return is negative (zero or positive).

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics. Using daily split-adjusted closing prices for SVXY and VXX and daily settlement prices for VIX futures from SVXY inception (October 2011) through December 2019, we find that:

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Are there reliable predictors supporting strategies for timing exchange-traded notes (ETN) constructed from near-term S&P 500 Volatility Index (VIX) futures, such as iPath S&P 500 VIX Short-Term Futures ETN (VXX) and ProShares Short VIX Short-Term Futures ETF (SVXY), available since 1/30/09 and 10/4/11, respectively. The managers of these securities buy and sell VIX futures daily to maintain a constant maturity of one month, continually rolling partial positions from nearest to next nearest contracts. VXX and SVXY target 1X and -0.5X daily performance relative to the S&P 500 VIX Short-Term Futures Index, respectively. We consider five potential predictors for these ETNs:

1. Level of VIX, in case a high (low) level indicates a future decrease (increase) in VIX that might affect VXX and SVXY.
2. Change in VIX (VIX “return”), in case there is some predictable reversion or momentum for VIX that might affect VXX and SVXY.
3. Implied volatility of VIX (VVIX), in case uncertainty in the expected level of VIX might affect VXX and SVXY.
4. Term structure of VIX futures (roll return) underlying VXX and SVXY, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, indicating a price headwind or tailwind for a fund manager continually rolling from one to the other. VIX roll return is usually negative (contango), but occasionally positive (backwardation).
5. Volatility Risk Premium (VRP), estimated as the difference between VIX and the annualized standard deviation of daily S&P 500 Index returns over the past 21 trading days (multiplying by the square root of 250 to annualize), in case this difference between expectations and recent experience indicates the direction of future change in VIX. VRP is usually positive, but occasionally negative.

We measure predictive power of each in two ways: (1) correlations between daily VXX and SVXY returns over the next 21 trading days to daily predictor values; and, (2) average next-day SVXY returns by ranked tenth (decile) of daily predictor values. Using daily levels of VIX and VVIX, settlement prices for VIX futures contracts, level of the S&P 500 Index and split-adjusted prices for VXX and SVXY from inceptions of the ETNs through December 2019, we find that: Keep Reading

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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