Volatility Effects

Do informed options traders know more than other traders? In other words, are there reliable and exploitable predictive relationships between changes in implied volatility and future returns for associated stocks? In the February 2012 version of their paper entitled “The Joint Cross Section of Stocks and Options”, Andrew Ang, Turan Bali and Nusret Cakici investigate the relationship between changes in implied volatility and stock returns for individual stocks. They consider both call-implied and put-implied volatilities based on near-term expirations. Using daily implied volatilities, associated daily stock prices and firm accounting data for a broad sample of U.S. stocks over the period January 1996 through September 2008 (153 months), they conclude that: Keep Reading

Generally, when stocks go up (down), iPath S&P 500 VIX Short Term Futures (VXX) goes down (up). A reader asked what happens after stocks and VXX move in the same direction. Is this unusual behavior a useful signal? Using daily returns of SPDR S&P 500 (SPY) and VXX from the inception of the latter on 1/30/09 through 2/17/12 (770 trading days), we find that: Keep Reading

Are option traders market leaders, such that information gleaned from options trading anticipates equity returns? In the December 2011 draft of their paper entitled “Exploiting Option Information in the Equity Market”, Guido Baltussen, Bart Van der Grient, Wilma De Groot, Weili Zhou and Erik Hennink examine whether information publicly available from the option market exploitably predicts returns for individual U.S. stocks. Specifically, they investigate the separate and combined information value of four at-the-money (ATM) and out-of-the-money (OTM) equity option trading metrics:

1. OTM Skew: the difference in implied volatilities between OTM puts and ATM calls.
2. RV-IV: the difference between realized volatility over the past 20 trading days (RV) and implied volatility (IV).
3. ATM Skew: the difference in implied volatilities between ATM puts and ATM calls.
4. Change in ATM Skew.

They define an option as ATM (OTM) when the ratio of strike price to stock price is between 0.95 and 1.05 (0.80 and 0.95). They reform equally-weighted quintile sort test portfolios weekly based on Tuesday closes for each metric, with a one-day lag (implementing with Wednesday closing data). Using daily total returns, market capitalizations and options trading data for those of the 1,250 largest stocks in the S&P/Citigroup U.S. Broad Market Index with sufficient options data during January 1996 through October 2009, they find that: Keep Reading

Can investors usefully predict the short-term direction of the stock market by contrasting the outlooks implied by out-of-the-money (OTM) and at-the-money (ATM) market index options. In the October 2011 update of their paper entitled “Implied Volatility Spreads and Expected Market Returns”, Turan Bali, Ozgur Demirtas and Yigit Atilgan investigate the relationship between stock market index implied volatility spread (slope of the volatility smile) and future stock market return. They consider several measures of the implied volatility spread, such as the difference in implied volatilities between the S&P 500 Index OTM put option and the ATM call option that have the highest open interest or trading volume each day. They define moneyness as the ratio of strike price to stock price, with ATM (OTM) having moneyness between 0.95 and 1.05 (from 0.8 to 0.95). They exclude options with time to expiration less than 10 days or more than 60 days, options priced less than $0.125 and options with missing or anomalous data. Using daily closing prices for S&P 500 Index options and S&P 500 Index daily opening and closing levels from January 4, 1996 through September 10, 2008, along with contemporaneous firm and economic data used in robustness tests, they find that: Keep Reading

Does the effectiveness of simple moving average (SMA) crossing signals vary with stock volatility? In the August 2011 update of their paper entitled “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis”, Yufeng Han, Ke Yang and Guofu Zhou investigate the application of SMAs to portfolios of stocks sorted based on realized volatility. Specifically, each year they sort stocks into deciles by volatility (standard deviation of daily returns over the past year). For each decile, they calculate a price index, an SMA for the index and daily returns based on initial equal weighting. When a decile portfolio is above (below) its SMA, they hold the portfolio (30-day Treasury bills), with a one-day delay for switches. They compare the returns for this timing strategy to buy-and-hold by decile. They focus on a 10-day SMA, but also test 20-day, 50-day, 100-day and 200-day SMAs. Using daily returns for a broad sample of U.S. stocks spanning 1963 through 2009, they find that: Keep Reading

Can investors earn a reliable premium from stocks with high downside risk? In their January 2012 paper entitle “Sorting Out Downside Beta”, Thierry Post, Pim Van Vliet and Simon Lansdorp measure in four ways (including regular beta) the premium associated with stock sensitivity to market movements. They estimate excess market returns based on total returns of a broad capitalization-weighted U.S. stock market index relative to one-month U.S. Treasury bills. They use rolling historical windows of 60 months to calculate beta and three alternative measures of downside beta. Using monthly total returns and firm characteristics for a broad sample of U.S. common stocks during 1926 through 2010, they find that: Keep Reading

Are realized (actual historical) and implied volatilities the whole story for equity option valuation? In their December 2011 paper entitled “Fundamental Analysis and Option Returns”, Theodore Goodman, Monica Neamtiu and Frank Zhang investigate the extent to which the equity options market fails to recognize volatility of firm operations (accounting data) and whether any such failure is exploitable. They focus tests on long, one-month-to-expiration, at-the-money straddles (long both a call and a put), which profit from large moves in underlying stock prices. They estimate future volatility in firm fundamentals via regression based on a combination of short-term sales/earnings growth and long-term sales/earnings growth volatility (standard deviation over the last six years). They isolate a “pure” expected fundamental volatility via regression versus implied volatility and the implied-realized volatility gap. Using data as available to estimate the relationship between fundamental volatility and returns on options for individual U.S. stocks during January 1996 through September 2010 (52,251 firm-quarters involving 3,481 distinct firms), they find that: Keep Reading

Can investors exploit idiosyncratic volatility exhibited by commodity futures? In their December 2011 paper entitled “Idiosyncratic Volatility Strategies in Commodity Futures Markets”, Adrian Fernancez-Perez, Ana-Maria Fuertes and Joelle Miffre investigate the usefulness of idiosyncratic volatility as a predictor of commodity futures returns. They define idiosyncratic volatility of commodity futures as return volatility not explained by contemporaneous variation in hedging pressure. They calculate hedging pressure from CFTC Commitments of Traders reports by relating long positions to total positions across trader categories. Return calculations assume: (1) holding the first nearby contract up to one month before maturity and then rolling to the next-nearest contract; (2) trading on a fully collateralized basis, meaning that half of trading capital earns the risk-free rate (three-month Treasury bill yield); and, (3) reporting only returns in excess of the risk-free rate, which averages about 3.3% annually over the sample period. They test all combinations of commodity ranking (whether for idiosyncratic volatility, return momentum or roll return) and portfolio holding intervals of 4, 13, 26 and 52 weeks. They calculate alpha by regressing long-short commodity futures portfolio returns against the same-interval hedging pressure risk premium. Using Friday settlement prices of nearest and second-nearest contracts for 27 commodity futures and weekly hedging pressure data during September 30, 1992 through March 25, 2011, they find that: Keep Reading

Are the relatively placid financial markets of the American Century evolving to a high-volatility regime in a more evenly competitive world? In his December 2011 paper entitled “Adaptive Markets and the New World Order”, Andrew Lo examines the implications of the Adaptive Markets Hypothesis (AMH), wherein “markets are not always efficient, but they are usually highly competitive and adaptive, varying in their degree of efficiency as the economic environment and investor population change over time.” He believes that investors can prepare for occasional failures of market efficiency by viewing financial markets and institutions from the perspective of evolutionary biology. Applying this perspective to markets since 1926, he concludes that: Keep Reading

A subscriber suggested applying a simple momentum trading strategy to a set of leveraged equity style (size, value-growth) exchanged-traded funds (ETF), including leveraged long and leveraged short counterparts to exploit both positive and negative markets. It seems plausible that leverage may make funds react quickly and strongly to business cycle shifts that affect style performance. However, the costs of maintaining leverage are countervailing. We test a set of 12 ProShares 2X and -2x leveraged sector ETFs, all of which have trading data back at least as far as April 2007:

ProShares Ultra Russell1000 Value (UVG)
ProShares Ultra Russell1000 Growth (UKF)
ProShares Ultra Russell MidCap Value (UVU)
ProShares Ultra Russell MidCap Growth (UKW)
ProShares Ultra Russell2000 Value (UVT)
ProShares Ultra Russell2000 Growth (UKK)

ProShares UltraShort Russell1000 Value (SJF)
ProShares UltraShort Russell1000 Growth (SFK)
ProShares UltraShort Russell MidCap Val (SJL)
ProShares UltraShort Russell MCap Growth (SDK)
ProShares UltraShort Russell2000 Value (SJH)
ProShares UltraShort Russell2000 Growth (SKK)

As in “Simple Sector ETF Momentum Strategy Performance” and “Doing Momentum with Style (ETFs)”, we consider a basic momentum strategy that allocates all funds at the end of each month to the ETF with the highest total return over the past six months (6-1). Using monthly adjusted closing prices for the 12 leveraged style ETFs and S&P Depository Receipts (SPY) over the period April 2007 through November 2011 (only 56 months), we find that: Keep Reading