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Volatility Effects

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.

Volatility Trading Strategies

How can investors use exchange-traded products to exploit equity market volatility? In the April 2013 version of his paper entitled “Easy Volatility Investing” (the National Association of Active Investment Managers’ 2013 Wagner Award runner-up), Tony Cooper explores the rewards and risks of five volatility trading strategies including simple buy-and-hold, price momentum, futures roll yield capture, volatility risk premium capture and dynamic hedging. He focuses on four exchange-traded notes (ETN) as trading vehicles:

  •  iPath S&P 500 VIX Short-Term Futures ETN (VXX) – inception January 30, 2009.
  • VelocityShares Daily Inverse VIX Short-Term ETN (XIV) – inception November 30, 2010.
  • iPath S&P 500 VIX Medium-Term Futures ETN (VXZ) – inception February 20, 2009.
  • VelocityShares Daily Inverse VIX Medium-Term ETN (ZIV) – inception November 30, 2010.

He extends the histories for these ETNs back to 2004 by simulating their prices using historical VIX futures data. For signaling, he considers two indexes:

  • S&P 500 1-Month Implied Volatility Index (VIX)
  • S&P 500 3-Month Implied Volatility Index (VXV)

He ignores trading frictions triggered by strategy trades and portfolio rebalancing, and ignores return on cash when not invested. Using levels of VIX and VXV, VIX futures prices and ETN prices as available during 2004 through mid-February 2013, he finds that: Keep Reading

Short-term VXX Shorting Signals?

Analyses in “Shorting VXX with Crash Protection” suggest that one-month momentum may be a useful signal for trading in and out of a short position in iPath S&P 500 VIX Short-Term Futures ETN (VXX). A subscriber inquired whether a short-term version of this signal is effective. Specifically, how useful is a strategy that goes short VXX (to cash) at the close when the same-day VXX return is negative (positive)? To test this daily momentum signal, we consider basic daily return statistics and two VXX shorting scenarios: (1) shorting an initial amount of VXX and letting this position ride indefinitely (Let It Ride); and, (2) shorting a fixed amount of VXX and resetting this fixed position daily (Fixed Reset). For tractability, we ignore shorting costs/fees, but we do consider the trading frictions associated with entering and exiting a short position in VXX based on the daily momentum signal. Using daily reverse split-adjusted closing prices for VXX from the end of January 2009 through mid-April 2013, we find that: Keep Reading

Easy Way to Capture Low-Beta Effect?

Is there a good short-cut for constructing a low-beta portfolio? In their March 2013 paper entitled “Country and Sector Drive Low-Volatility Investing in Global Equity Markets”, Sanne de Boer, Janet Campagna and James Norman investigate the role of country and sector effects in low-volatility investing across global stock markets. They construct country-sector capitalization-weighted sub-indexes (for example, U.S. Utilities) from stocks in the MSCI World Index. They then compare performances of a portfolio of these sub-indexes and a portfolio of individual stocks, both reformed every six months (ends of May and November) to minimize future volatility as predicted by a proprietary model (Axioma’s Global Risk Model). All portfolios are long-only with no leverage. Using monthly returns and market (free-float) capitalizations of MSCI World Index stocks during 1999 through 2012, they find that: Keep Reading

Beta, Value and Momentum for Industries

Do industries exhibit the market beta, value and momentum anomalies overall and in recent data? In his August 2012 paper entitled “The Failure of the Capital Asset Pricing Model (CAPM): An Update and Discussion”, Graham Bornholt examines the beta, value and momentum anomalies using returns for 48 U.S. industries. Each month, he forms three groups of eight equally weighted portfolios of industries ranked separately by: (1) beta based on rolling regressions of industry returns versus value-weighted market returns over the past 60 months; (2) value based on the latest available industry book-to-market ratios (value-weighted composites of component firm book-to-market ratios, updated annually); and, momentum based on lagged six-month industry returns. There are therefore six industries in each portfolio. Using monthly industry returns from Kenneth French’s website, monthly returns for the value-weighted U.S. stock market in excess of the one-month U.S. Treasury bill yield, and industry component book-to-market ratios during July 1963 through December 2009 he finds that: Keep Reading

Linear Factor Stock Return Models Misleading?

Does use of alphas from linear factor models to identify anomalies in U.S. stock returns mislead investors? In the February 2013 draft of their paper entitled “Using Maximum Drawdowns to Capture Tail Risk”, Wesley Gray and Jack Vogel investigate maximum drawdown (largest peak-to-trough loss over a time series of compounded returns) as a simple measure of tail risk missed by linear factor models. Specifically, they quantify maximum drawdowns for 11 widely cited U.S. stock return anomalies identified via one-factor (market), three-factor (plus size and book-to-market ratio) and four-factor (plus momentum) linear models. These anomalies are: financial distress; O-score (probability of bankruptcy); net stock issuance; composite stock issuance; total accruals; net operating assets; momentum; gross profitability; asset growth; return on assets; and, investment-to-assets ratio. They calculate alphas for each anomaly by using the specified linear model risk factors to adjust gross monthly returns from a portfolio that is long (short) the value-weighted or equal-weighted tenth of stocks that are “good” (“bad”) according to that anomaly, reforming the portfolio annually or monthly depending on anomaly input frequency. Using monthly returns and firm fundamentals for a broad sample of U.S. stocks, and contemporaneous stock return model factor returns, during July 1963 through December 2012, they find that: Keep Reading

Layers of Low Beta

Do low-beta equity strategies work differently for industries and countries compared to individual stocks? In their January 2013 paper entitled “The Low Risk Anomaly: A Decomposition into Micro and Macro Effects”, Malcolm Baker, Brendan Bradley and Ryan Taliaferro decompose the low-beta anomaly into individual stock (micro) and industry/country (macro) components. To study individual stock versus industry effects, they use a long sample (48 years) of data for U.S. stocks, with betas estimated over lagged 60-month intervals from monthly excess returns (relative to U.S. Treasury bills). To study individual stock versus country effects, they use a shorter sample (about 22.5 years) of data for developed market (including the U.S.) stocks, with betas estimated over lagged 60-week intervals from weekly excess returns. Their principal performance metrics are: (1) gross one-factor (excess market return) alpha; and, (2) the difference in gross alphas between the value-weighted lowest-beta and highest-beta fifths (quintiles) of assets, reformed monthly. They decompose effects for individual stocks and industries/countries via double-sorts. Using monthly returns, SIC codes and market capitalizations for U.S. common stocks during January 1963 through December 2011, and both weekly and monthly returns and market capitalizations for common stocks from 30 developed country stock markets as available during July 1989 through January 2012, they find that: Keep Reading

Country Stock Market Return-Risk Relationship

Do returns for country stock markets vary systematically with the return volatilities of those markets? In their December 2012 paper entitled “Are Investors Compensated for Bearing Market Volatility in a Country?”, Samuel Liang and John Wei investigate the relationships between monthly returns and both total and idiosyncratic volatilities for country stock markets. They measure total market volatility as the standard deviation of country market daily returns over the past month. They measure idiosyncratic market volatility in two ways: (1) standard deviation of three-factor (global market, size, book-to-market ratio) model monthly country stock market return residuals over the past three years; and, (2) standard deviation of one-factor (global market) model country stock market return residuals over the past month. They then relate monthly country market raw return, global one-factor alpha and global three-factor alpha to prior-month country market volatility. Using monthly returns and characteristics for 21 developed country stock markets (indexes) and the individual stocks within those markets, and contemporaneous global equity market risk factors, during 1975 through 2010, they find that: Keep Reading

Compounding Loss from High Beta?

How does volatility interact with market beta? In his 2012 paper entitled “Volatility and Compounding Effects on Beta and Returns”, William Trainor investigates the performance of stocks sorted on market beta overall and during intervals of low and high market volatility. He considers both ideal (theoretical) betas and betas estimated from lagged returns. He defines low (high) market volatility as below (above) the long-term annual average for a value-weighted index constructed from a broad sample of U.S. stocks (15.8%). Using both theoretical derivations and empirical monthly returns for sampled stocks during January 1926 through December 2009, he finds that: Keep Reading

News, VIX and Stock Market Returns

How does aggregate stock news sentiment relate to equity market return and volatility? In his October 2012 paper entitled “Time-Varying Relationship of News Sentiment, Implied Volatility and Stock Returns”, Lee Smales investigates relationships among aggregate unscheduled firm-specific news sentiment, changes in the S&P 500 Implied Volatility Index (VIX) and both contemporaneous and future S&P 500 Index returns. He measures daily aggregate unscheduled firm-specific news sentiment as an average of scores calculated by the RavenPack news analysis tool for articles with headlines specifying S&P 500 stocks published for the first time that day on the Dow Jones news wire and in the Wall Street Journal. Unscheduled means exclusion of scheduled news releases such as earnings and dividend announcements. Using daily aggregated news sentiment for S&P 500 firms and levels of the S&P 500 Index and VIX during January 2000 through December 2010, he finds that: Keep Reading

Option Straddles Around Earnings Announcements

Does market underestimation of stock price uncertainty around earnings announcements support a short-term straddle strategy (call option and put option with matched strike and expiration, profitable with large stock price moves)? In their January 2013 paper entitled “Anticipating Uncertainty: Straddles Around Earnings Announcements”, Yuhang Xing and Xiaoyan Zhang investigate the performance of short-term, near-the-money straddles during intervals around earnings announcements. Short-term means no more than 60 days to expiration. Near-the-money means moneyness in the range 0.95 to 1.05. They focus on a delta-neutral straddle constructed by appropriately weighting the call and put positions at initiation, but they also consider a simple one call-one put alternative. They examine several straddle holding periods starting at the close five, three or one trading day before scheduled earnings announcement date and ending at the close on or one day after earnings announcement date. They calculate option returns based on the mid-point of the daily closing bid and ask as a fair option price (and require it to be at least $0.125). Using daily stock returns and option prices (with data filtered to exclude implausible data), along with contemporaneous quarterly firm fundamentals, during January 1996 through December 2010, they find that: Keep Reading

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