Volatility Effects

Is reward-for-risk or reward-for-not taking risk the rule among stocks? In their April 2012 paper entitled “Low Risk Stocks Outperform within All Observable Markets of the World”, Nardin Baker and Robert Haugen measure performance differences between low-volatility stocks and high-volatility stocks in developed and emerging equity markets worldwide. They define a stock’s lagged volatility as the standard deviation of its monthly total returns over the past 24 months. Each month beginning in 1990, they form ten (apparently) equally weighted portfolios in each country ranked by lagged volatility. They then calculate the differences in average annualized gross returns, standard deviations of annualized gross returns and annualized gross Sharpe ratio (estimated as the ratio of average return to volatility) between the portfolios with the lowest and highest lagged volatilities. Using monthly returns for broad samples of stocks in 21 developed and 12 emerging markets during 1988 through 2011 (288 months), they find that: Keep Reading

Should investors focus on relatively wild (high-volatility) or tame (low-volatility) stocks in emerging stock markets? In their April 2012 paper entitled “The Volatility Effect in Emerging Markets”, David Blitz, Juan Pang and Pim van Vliet examine the empirical relationship between risk and return in emerging equity markets. At the end of each month, they form equally-weighted quintile portfolios of emerging market stocks ranked separately on: (1) lagged volatility (standard deviation of total monthly returns in local currency over the past 36 months); and, (2) lagged beta (from regression of total monthly returns in U.S. dollars versus the appropriate S&P/IFCI country market index over the past 36 months). They make portfolios country-neutral by distributing each country’s stocks evenly across quintiles. They calculate annualized arithmetic and geometric average returns, volatilities and Sharpe ratios for the quintile portfolios based on their monthly total returns in U.S. dollars in excess of the one-month Treasury bill (T-bill) yield. Using monthly total returns in local currencies and U.S. dollars for stocks from 30 emerging markets (an average of about 1,000 stocks per year) during December 1988 through December 2010, along with the contemporaneous T-bill yield, they find that: Keep Reading

Are there ways to enhance the currency carry trade (long currencies offering high interest rates and short those offering low rates)? In the May 2012 version of their paper entitled “Average Variance, Average Correlation and Currency Returns”, Gino Cenedese, Lucio Sarno and Ilias Tsiakas investigate the ability of components of the currency exchange market risk (variance of the average return for all exchange rates) to predict carry trade returns. Their baseline carry trade portfolio involves U.S. dollar nominal exchange rates, rebalanced monthly. They decompose the market variance into two components: average variance of individual exchange rate returns, and average correlation of exchange rate returns. They examine the effects of changes in these risk components on the entire future distribution of currency trade returns (via quantile breakdowns), focusing on the large losses in the left tail and large gains in the right tail. Using daily spot and forward exchange rates for 33 currencies relative to the U.S. dollar as available during 1976 through February 2009 (15 active exchange rates at the beginning and 22 at the end), they find that: Keep Reading

Does the variance risk premium (derived from the mostly positive gap between options-implied equity market volatility and actual equity market volatility) robustly predict stock market returns worldwide? In the March 2012 version of their paper entitled “Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence”, Tim Bollerslev, James Marrone, Lai Xu and Hao Zhou test the statistical and geographic robustness of the power of the aggregate variance risk premium to predict overall stock market returns. Statistical robustness testing addresses sampling frequency and the use of overlapping measurement intervals. Geographic robustness testing involves measurement of the variance risk premium for French CAC 40, the German DAX 30, the Japanese Nikkei 225, the Swiss SMI and the UK FTSE 100. While tests in past research employ monthly data, they calculate the implied-realized volatility gap for the U.S. by subtracting a measure of the actual S&P 500 Index return variance over the past 20 trading days from the square of VIX (see the chart below). Using S&P 500 Index daily returns and VIX levels for February 1996 through December 2007 and comparable data for other country stock markets for January 2000 through December 2011, they find that: Keep Reading

Do economic announcements systematically remove uncertainty from financial markets and thus reliably lower implied volatility indexes? In their September 2010 paper entitled “The Impact of Macroeconomic Announcements on Implied Volatilities”, Roland Füss, Ferdinand Mager and Lu Zhao measure the reactions of the Chicago Board Options Exchange Volatility Index (VIX) and the DAX Volatility Index (VDAX) to U.S. and German macroeconomic announcements. They consider announcements of Gross Domestic Product (GDP), the Producer Price Index (PPI) and the Consumer Price Index (CPI). The measurement interval is apparently close-to-close from the day before to the day of announcement. Using monthly/quarterly macroeconomic announcement dates from January 2005 through December 2009 and contemporaneous daily data for VIX and VDAX (60 months), they find that: Keep Reading

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