Reward goes with risk, and volatility represents risk. Therefore, volatility means reward; investors/traders get paid for riding roller coasters. Right? These blog entries relate to volatility effects.
September 10, 2015 - Volatility Effects
How can investors best exploit research showing that low-beta (high-beta) stocks tend to outperform (underperform)? In their August 2015 paper entitled “Low-Beta Investment Strategies”, Olaf Kornz and Laura-Chloe Kuntz test 32 “zero-cost” (long-short) strategies designed to exploit the beta anomaly as applied to S&P 500 stocks. The strategies derive from three choices: (1) length of the rolling window used to calculate stock and market index betas (one, three, six or 12 months of daily returns); (2) portfolio holding period (12 months or three months); and, (3) portfolio tilt method (four alternatives). The four portfolio tilt alternatives are:
- Long Low-Beta/Short Index – long the equally weighted 50 stocks with the lowest betas, offset by a short position in the S&P 500 Index.
- Long-Short Low-High Beta Equal Weight – equal dollar values for a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, plus a small position in the market to offset any long-short beta mismatch.
- Long-Short Low-High Beta Matched – a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, in different dollar amounts determined to produce a portfolio beta of one.
- Short High-Beta/Long Index – short the equally weighted 50 stocks with the highest betas, offset by a long position in the S&P 500 Index.
Portfolio tilt alternatives also include positions in 1-month U.S. Treasury bill futures to satisfy an exact zero-cost assumption. The authors form portfolios monthly, such that there are 12 (four) overlapping portfolios with 12-month (3-month) holding periods. They also test the ability of the betas of aggregate high-beta stocks and aggregate low-beta stocks to predict market returns. Using daily returns for the S&P 500 index and its component stocks during September 1988 through October 2014, they find that: Keep Reading
September 3, 2015 - Momentum Investing, Volatility Effects
What indicator works best to mitigate stock momentum strategy crashes? In his March 2015 paper entitled “Momentum Crash Management”, Mahdi Heidari compares performances of seven indicators for avoiding conventional stock momentum strategy crashes: (1) prior-month market return; (2) change in prior-month market return: (3) market volatility (standard deviation of 52 weekly returns); (4) dispersion (variance) of daily returns across all stocks; (5) market illiquidity (aggregate impact of trading on price); (6) momentum volatility (standard deviation of momentum strategy returns the past six months); and, (7) change in momentum volatility. The conventional strategy is each month long (short) the value-weighted tenth of stocks with the highest (lowest) returns from 12 months ago to one month ago. For each of the competing indicators, he invests in the conventional momentum strategy (cash) when the indicator is below (within) the top 10% of its values over the past five years. He uses portfolio turnover to compare implementation costs. Using data for a broad sample of relatively liquid U.S. stocks during January 1926 through December 2013, he finds that: Keep Reading
August 28, 2015 - Volatility Effects
Is implied volatility of implied volatility, interpretable as a measure of changes in investor fear level, a useful indicator of future stock market returns or VIX futures returns? To investigate, we examine relationships between the CBOE VVIX Index, a measure of the expected volatility of the 30-day forward level of the S&P 500 Implied Volatility Index (VIX) derived from prices of VIX options, and future returns for SPDR S&P 500 (SPY)and iPath S&P 500 VIX Short-Term Futures (VXX). Using daily levels of VVIX and daily adjusted closes for SPY and VXX as available during January 2007 (VVIX inception) through mid-August 2015, we find that: Keep Reading
August 6, 2015 - Short Selling, Volatility Effects
“Shorting Leveraged ETF Pairs” looks at shorting leveraged long/short pairs of exchange-traded funds (ETF) and letting the short positions “melt away” over long holding periods. Findings suggest that the approach may be profitable, with most of the gain coming when market volatility is high. What about more active strategies of continually renewed short positions? To investigate, we consider monthly renewal of short positions in the ProShares Ultra S&P500 (SSO) / ProShares UltraShort S&P500 (SDS) 2X/-2X pair and the ProShares UltraPro S&P500 (UPRO) / ProShares UltraPro Short S&P500 (SPXU) 3X/-3X pair. Using monthly adjusted closes for these ETFs and for the S&P 500 Volatility Index (VIX) from respective inceptions through June 2015, we find that: Keep Reading
July 28, 2015 - Calendar Effects, Volatility Effects
Does the S&P 500 implied volatility index (VIX) exhibit systematic behaviors by day of the week, around turn-of-the-month (TOTM) or around options expiration (OE)? If so, are the behaviors exploitable? Using daily closing levels of VIX since January 1990, daily opening levels of VIX since January 1992 and daily reverse split-adjusted opening and closing levels of iPath S&P 500 VIX Short-Term Futures ETN (VXX) since February 2009, all through early July 2015, we find that: Keep Reading
July 22, 2015 - Momentum Investing, Value Premium, Volatility Effects
Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading
July 21, 2015 - Commodity Futures, Volatility Effects
Can traders use S&P 500 Implied Volatility Index (VIX) options to exploit predictability in behaviors of underlying VIX futures. In his June 2015 paper entitled “Trading the VIX Futures Roll and Volatility Premiums with VIX Options”, David Simon examines VIX option trading strategies that:
- Buy VIX calls when VIX futures are in backwardation (difference between the front VIX futures and VIX, divided by the number of business days until expiration of the VIX futures, is greater than +0.1 VIX futures point).
- Buy VIX puts when VIX futures are in contango (difference between the front VIX futures and VIX, divided by the number of business days until expiration of the VIX futures, is less than -0.1 VIX futures point).
- Buy VIX puts when the VIX options-futures volatility premium (spread between VIX option implied volatility and lagged 10-trading day VIX futures volatility adjusted for number of trading days to expiration) is greater than 10%.
He measures trade returns for a holding period of five trading days, with entry and exit at bid-ask midpoints. An ancillary analysis relevant to strategy profitability looks at hedged returns on VIX options to determine whether they are overpriced: (1) generally; and, (2) for the top 25% of VIX options-futures volatility premiums. Using daily data for VIX options data and for VIX futures (nearest contract with at least 10 trading days to expiration) during January 2007 through March 2014, he finds that: Keep Reading
July 16, 2015 - Volatility Effects
“Identifying VXX/XIV Tendencies” finds that the Volatility Risk Premium (VRP), estimated as the difference between the current level of the S&P 500 implied volatility index (VIX) and the annualized standard deviation of S&P 500 Index daily returns over the previous 21 trading days (multiplying by the square root of 250 to annualize), may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare performance data for:
- Buying and holding XIV.
- Timing XIV to avoid times when VRP is low.
- Timing XIV and VXX to exploit both high and low VRP conditions.
Using daily closes for XIV, VXX, VIX and the S&P 500 Index from XIV inception (end of November 2010) through most of June 2015, we find that: Keep Reading
July 15, 2015 - Commodity Futures, Volatility Effects
“Identifying VXX/XIV 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 VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare performance data for:
- Buying and holding XIV.
- Timing XIV to avoid times when the roll return is positive.
- Timing XIV and VXX to exploit both negative and positive roll return conditions.
Using daily closing prices for XIV and VXX and daily settlement prices for VIX futures from XIV inception (end of November 2010) through most of June 2015, we find that: Keep Reading
July 14, 2015 - Commodity Futures, Volatility Effects
A subscriber inquired about strategies for trading exchange-traded notes (ETN) constructed from near-term S&P 500 Volatility Index (VIX) futures: iPath S&P 500 VIX Short-Term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-Term (XIV), available since 1/30/09 and 11/30/10, respectively. The managers of these securities buy and sell VIX futures daily to maintain a constant maturity of one month (long for VXX and short for XIV), continually rolling partial positions from the nearest term contract to the next nearest. We consider four potential predictors of the price behavior of these securities:
- The level of VIX, in case a high (low) level indicates a future decrease (increase) in VIX that might affect VXX and XIV.
- The change in VIX, in case there is some predictable reversion or momentum for VIX that might affect VXX and XIV.
- The term structure of VIX futures (roll return) underlying VXX and XIV, 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. Roll return is usually negative (contango), but occasionally positive (backwardation).
- The 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.
We identify predictive power by relating daily VXX and XIV returns over the next 21 trading days to daily values of each indicator. Using daily levels of VIX, settlement prices for VIX futures contracts, levels of the S&P 500 Index and split-adjusted prices for VXX and XIV from inceptions of the ETNs through most of June 2015, we find that: Keep Reading