Volatility Effects

For which stocks does market-adjusted (idiosyncratic) volatility work as an indicator of future returns (see “No Reward for Risk?”)? In their January 2012 paper entitled “Dissecting the Idiosyncratic Volatility Anomaly”, Linda Chen, George Jiang, Danielle Xu and Tong Yao measure the idiosyncratic volatility premium in different subsamples of U.S. stocks. To measure the premium, they focus on differences in average monthly returns and four-factor (market, size, book-to-market, momentum) alphas between both value-weighted and equal-weighted top and bottom deciles of prior-month idiosyncratic volatility. They consider the following stock universe subsamples: (1) common stocks and non-common stocks; (2) microcap, small and big stocks (breakpoints at the 20th and 50th percentiles of NYSE stock capitalizations); (3) stocks priced above $10, between $5 and $10 and below $5; and, (4) January and non-January returns. Using daily returns for NYSE/AMEX/NASDAQ common and non-common stocks, and for a value-weighted market index, to calculate monthly idiosyncratic volatility during 1963 through 2010, they find that: Keep Reading

Should investors diversify U.S. equity holdings with S&P 500 volatility index (VIX) futures or exchange-traded notes (ETN) constructed from these futures? In the March 2012 version of their paper entitled “Diversification of Equity with VIX Futures: Personal Views and Skewness Preference”, Carol Alexander and Dimitris Korovilas examine the performance and equity diversification power of VIX futures. They focus on ETNs with one-month constant maturity, available since January 30, 2009 as VXX (iPath S&P 500 VIX Short Term Futures), and five-month constant maturity, available since February 20, 2009 as VXZ (iPath S&P 500 VIX Mid-Term Futures). They extend these proxies back to December 2005 using matched S&P 500 VIX futures constant maturity index series and further back to April 2004 using futures price data and the Standard & Poor’s methodology. They use SPDR S&P 500 (SPY) to represent equity exposure. For diversity in equity market conditions, they consider three subperiods: April 2004 through September 2006 (tranquil); October 2006 through March 2009 (crisis); and, April 2009 through December 2011 (punctuated volatility). When examining VIX futures contract returns, they roll five days prior to maturity to avoid the effect of maturity on final settlement. Using daily data for SPY, VIX futures, VIX futures indexes, VXX and VXZ as available from March 26, 2004 (the inception of VIX futures) through December 2011, they find that: Keep Reading

In general, when the U.S. stock market goes down, the S&P 500 volatility index (VIX) goes up. VIX is not investable, but VIX futures are available. Are short-term VIX futures a good way to hedge equity market declines and guard against market blow-ups? To investigate we focus on returns from holding the contract nearest to maturity, rolling to the next nearest on maturity dates. For simplicity, we assume that rolling is frictionless (favorable to futures) and that available capital always matches a round number of futures contracts (no residual cash). Using daily levels of VIX and daily settlement values of all VIX futures series from late March 2004 through late March 2012 (eight years), we find that: Keep Reading

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