Volatility Effects

Does political drama take over as the principal driver of U.S. stock market implied volatility during election seasons? In their March 2012 paper entitled “U.S. Presidential Elections and Implied Volatility: The Role of Political Uncertainty”, John Goodell and Sami Vähämaa compare the effects of political uncertainty to those of eight other sources of uncertainty on implied stock market volatility (as measured by VIX) during U.S. presidential election campaigns. They define the quadrennial campaign interval as the time from the beginning of February to the beginning of November of election years. They consider two measures of political uncertainty derived from the Iowa Electronic Markets: monthly change in probability of success of the eventual winner; and, monthly change in a measure of how close the race is. They also consider eight competing financial and economic sources of uncertainty as listed below. Using monthly data for these ten variables during the presidential election campaigns of 1992, 1996, 2000, 2004 and 2008 (40 total monthly observations), they find that: Keep Reading

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

Is “Shorting Leveraged ETF Pairs” a good idea? In their brief March 2012 paper entitled “Levered ETFs”, Wenxi Jiang and Hongjun Yan examine the returns from matched positions in long and short leveraged exchange-traded funds (ETF). Specifically, they calculate returns from shorting matched pairs. Using data for matched 2X/-2X and 3X/-3X ETFs during 2007 through 2011, they find that: Keep Reading

Does the S&P 500 implied volatility index (VIX) exhibit systematic behaviors by day of the week? In their February 2012 paper entitled “Day of the Week Effect on the VIX: A Parsimonious Representation”, Maria Gonzalez-Perez and David Guerrero apply methodologies that minimize sensitivity to outliers to examine VIX day-of-the-week patterns. Using daily closes of VIX and the S&P 500 Index during 2004 through 2008, they find that: Keep Reading

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