Volatility Effects

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:

Keep Reading

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:

Keep Reading

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:

Keep Reading

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:

Keep Reading

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