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.
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
July 10, 2015 - Volatility Effects
What volatility weighting scheme best exploits equity return volatility persistence based on net outcome? In the June 2015 version of his paper entitled “Dynamic Volatility Weighting in the Presence of Transaction Costs”, Valeriy Zakamulin examines a volatility weighting strategy with features that allow suppression of rebalancing frictions. The idea behind volatility weighting is to construct a portfolio that targets a specified (benchmark) volatility based on predictability (persistence) of asset volatility. Specifically, he compares three strategies:
- The theoretically (frictionless and with perfectly predictable asset volatility) optimal strategy, which weights an asset according to the ratio of benchmark variance (square of standard deviation of returns) to predicted asset variance.
- An optimized modified volatility weighting strategy, which includes two parameters to suppress trading: (1) a tuning parameter to control the aggressiveness of response to a change in predicted asset volatility; and, (2) a no-transaction buffer around targeted asset weight.
- Conventional volatility targeting, which weights an asset according to the ratio of benchmark volatility (standard deviation of returns) to predicted asset volatility.
For all three strategies, he sets benchmark volatility at an annualized 20%. He forecasts annual asset volatility from an exponentially weighted moving average of daily returns over a rolling window of the past year. He considers daily, 5-day and 21-day volatility forecast revision frequencies. He considers four levels of trading frictions (0.0%, 0.1%, 0.25% and 0.5%) and optimizes modified strategy tuning and buffer parameters for each level. He employs the six Fama-French portfolios formed on size and
book-to-market ratio as test assets. Using daily returns for these six style series and for the aggregate U.S. stock market during January 1989 through December 2014, he finds that: Keep Reading
June 23, 2015 - Volatility Effects
There are many leveraged exchange-traded funds (ETF) designed to track multiples of short-term (daily) changes in popular indexes. Over longer holding periods, these ETFs tend to veer off track. The cumulative tracking error can be large. How well do leveraged ETFs track benchmarks over a multi-year period? What return metric drives the degree to which they fail to achieve targeted leverage? To investigate, we consider two sets of the oldest leveraged ETFs:
- 34 ProShares +2X and -2X leveraged equity index ETFs (17 matched long-short pairs), with start date 3/14/07 (limited by the youngest fund), which track U.S. broad market and sector indexes.
- 10 ProShares +3X and -3X leveraged equity index ETFs (five matched long-short pairs), with start date 2/11/10, which track U.S. broad stock market indexes only.
We measure actual average daily tracking by comparing the average daily return of each leveraged ETF to the average daily return of a +1X ETF that tracks the same index. We measure longer-term (monthly) tracking by comparing the monthly Sharpe ratio of each leveraged ETF to that of a +1X ETF that tracks the same index. Using daily and monthly adjusted closing prices for the above funds and +1X counterparts through May 2015 and the contemporaneous monthly U.S. Treasury bill yield as the risk-free rate for Sharpe ratio calculations, we find that: Keep Reading
June 17, 2015 - Volatility Effects
How far can a fund manager squeeze turnover while still maintaining an effective low-volatility portfolio? In his June 2015 paper entitled “Low Turnover: a Virtue of Low Volatility”, Pim van Vliet investigates the lower limit of turnover for a low-volatility stock portfolio in two ways. First, he reviews 21 published analyses to relate turnover to volatility reduction while controlling for other factors. Second, he directly relates turnover and volatility reduction for an equally weighted portfolio that: (1) initially selects the 500 of 3,000 liquid global stocks with the lowest weekly volatility over the prior three years; and, (2) each subsequent month rebalances stocks that have at least doubled their baseline portfolio weight and sells stocks when they fall out of the top X% of the volatility ranking, with X varying from 20% (baseline) to 90%. He also models the costs of maintaining low-volatility stock portfolios. Using findings from 13 academic journal articles and working papers and weekly returns for the 3,000 most liquid global stocks during January 1989 through December 2013, he finds that: Keep Reading
June 2, 2015 - Momentum Investing, Size Effect, Value Premium, Volatility Effects
Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading
April 28, 2015 - Equity Options, Volatility Effects
Do low-volatility strategies work for all stocks? In their April 2015 paper entitled “Low Risk Anomalies?”, Paul Schneider, Christian Wagner and Josef Zechner examine relationships between low-beta/low-volatility stock anomalies and implied stock return skewness. They compute ex-ante (implied) skewness for each stock via a portfolio of associated options that is long (short) out-of-the-money calls (puts). The more investors are willing to pay for downside risk protection (puts), the more negative this measure becomes. Using stock and option price data for 5,509 U.S. stocks for which options are available during January 1996 through August 2014, they find that: Keep Reading
April 10, 2015 - Momentum Investing, Volatility Effects
Which stock momentum return predictor works best? In his March 2015 paper entitled “Momentum Crash Management”, Mahdi Heidari compares the crash protection effectiveness of seven stock momentum return predictors, categorized into two groups:
- Overall stock market statistics: prior-month market return; change in monthly market return; volatility of market returns (standard deviation of weekly returns for the past 52 weeks); cross-sectional dispersion of daily stock returns for the past month; and, market illiquidity (value-weighted average of the monthly averages of daily price impacts of trading for all stocks).
- Momentum return series statistics: volatility of momentum returns (standard deviation of monthly returns over the past six months); and monthly change in volatility of momentum returns.
He measures momentum conventionally by first ranking all stocks by their returns from 12 months ago to one month ago and then after the skip-month forming a hedge portfolio that is long (short) the value-weighted tenth of stocks with the highest (lowest) past returns. He next tests the power of the above seven variables to predict the resulting monthly momentum return series. Finally, he tests dynamic momentum risk management strategies that execute the conventional momentum strategy (go to cash) when each of the seven predictors is below (above) the 90 percentile of its values over the last five years. Using daily and monthly returns, daily trading volumes and shares outstanding for a broad sample of U.S. common stocks during January 1926 through December 2013, he finds that: Keep Reading