Volatility Effects

Do low-volatility stocks outperform high-volatility stocks around the globe? In their October 2012 paper entitled “Stock Return Volatility, Operating Performance and Stock Returns: International Evidence on Drivers of the ‘Low Volatility’ Anomaly”, Tanuj Dutt and Mark Humphery-Jenner investigate links among stock return volatility, stock returns and firm operating performance in emerging and developed markets outside North America. They focus on a 500-day moving variance of daily stock returns as a measure of volatility, but also consider 90-day, 180-day, 250-day and 1000-day alternatives. They assign stocks based on listing exchange to one of four market groups: Emerging Asia, Emerging EMEA, Latin America and Ex-U.S./Canada Developed. For each group each month, they rank stocks into quintiles (fifths) by return volatility and track value-weighted and equal-weighted average quintile returns for the balance of the month. Using daily prices for a broad sample of international stocks (excluding the smallest and least liquid, but still capturing over 90% of market capitalization) and associated firm operating performance data during 1990 through 2009, they find that: Keep Reading

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

Does a low-beta strategy work for mutual funds? In his September 2012 paper entitled “Capitalizing on the Greatest Anomaly in Finance with Mutual Funds”, David Nanigian examines portfolios of funds sorted on lagged beta to determine whether mutual fund investors can capitalize on outperformance of low-beta assets. He calculates rolling betas for each mutual fund based on monthly returns over the prior 60 months (or less, as few as 24 months, when 60 months of returns are unavailable) or 12 months. He then ranks funds based on lagged betas into asset-weighted fifths each month and holds for one month, or each year and holds for one year. Using monthly net returns and total assets for a broad sample of U.S. equity open-end mutual funds, along with contemporaneous U.S. stock market returns, the risk-free rate and commonly applied risk factors, during December 1990 through April 2012, he finds that: Keep Reading

Does the S&P 500 options-implied volatility index (VIX) exhibit predictable daily, overnight and intraday tendencies? In their September 2012 paper entitled “What Makes the VIX Tick?”, Warren Bailey, Lin Zheng and Yinggang Zhou employ high-frequency data to investigate patterns in VIX behavior and measure relationships between VIX and various financial fundamentals, economic announcements and investor sentiment. Using data for VIX and other variables sampled at one-minute intervals (as appropriate) during January 2005 through June 2010, they find that: Keep Reading

The authors of the study summarized in “Exploit ETN Deviation from Indicative Value?” argue that deviations of prices for exchange-traded notes (ETN) from their indicative (immediate redemption) values may be useful as trading signals. How well does this mispricing concept work for the very liquid iPath S&P 500 VIX Short-term Futures ETN (VXX)? To check, we consider several mispricing thresholds and measure the short-term profitability of both long and short trades based on these thresholds. Using daily split-adjusted opening and closing prices and closing indicative values for VXX from inception at the end of January 2009 through mid-September 2012 (916 trading days), we find that: Keep Reading

Risk-focused asset allocation strategies derive from evidence that forecasting asset return volatility is easier than forecasting average return. Is there a best risk-focused strategy? In his September 2012 paper entitled “A Small Survey of Quantitative Models that Discard Estimation of Expected Returns for Portfolio Construction”, Stefano Colucci compares asset allocation strategies that rely on forecasted asset risk metrics but not on forecasted average returns. Specifically, he compares future gross annualized return-risk ratios, Ulcer indexes, one-month maximum drawdowns and average monthly portfolio turnovers for the following asset allocation strategies:

1. Minimum Variance (least volatile, or left-most, efficient portfolio per Modern Portfolio Theory).
2. Minimum Expected Shortfall with weightings estimated by Monte Carlo simulation.
3. Equal Risk Contribution (each asset weighted by the inverse of its forecasted maximum expected shortfall).
4. Maximum Diversification (related to expected shortfall with weightings again estimated by numerical simulation).
5. Risk Parity (each asset weighted by the inverse of its portfolio volatility contribution).
6. Equal Weighting (requiring neither average return nor volatility forecasts) as a benchmark.

He reforms portfolios every 20 trading days (approximately monthly) and estimates future risk metrics based on a rolling historical window of 500 trading days (approximately two years). Using daily returns over recent periods for stock and bond indexes and individual stocks segregated into several asset selection universes, he finds that: Keep Reading

What is the best way to bet against beta in equity markets? In their August 2012 paper entitled “Beta-Arbitrage Strategies: When Do They Work, and Why?”, Tony Berrada, Reda Jurg Messikh, Gianluca Oderda and Olivier Pictet derive and test a dynamic low-beta portfolio strategy designed to maximize excess return relative to the market portfolio. They test the strategy on a broad sample of U.S. stocks, 18 developed country stock indexes and 10 equity sector indexes by varying the emphasis on low versus high beta each month based on beta dispersion in lagged rolling 60-month intervals. Using monthly total returns for U.S. stocks during July 1925 through December 2011, for developed country stock market indexes during January 1970 through November 2010 and for sector indexes during January 1995 through November 2010, they find that: Keep Reading

How should investors view the predictability of stock market returns and volatility? In sections 5 and 6 of the July 2012 version of his draft chapter entitled “Equity Market Level”, Andrew Ang examines the predictability of the equity risk premium and equity market volatility. He also addresses the exploitability of any predictive power found. Using both theoretical arguments and empirical tests based on long-run data through December 2011, he concludes that: Keep Reading

Does demand for high-beta stocks by money managers extinguish the risk-return relationship? In his May 2012 paper entitled “Agency-Based Asset Pricing and the Beta Anomaly”, David Blitz investigates whether a volatility preference among stock portfolio managers flattens any relationship between beta and expected returns, thereby invalidating the most widely used asset pricing models. Because institutional investors typically evaluate portfolio managers versus market returns and prohibit or limit leverage, these managers have an incentive (under a belief in reward-for-risk) to focus investments in high-beta stocks with high expected returns. He calculates beta of a stock by regressing its monthly returns (in excess of the risk-free rate) against stock market excess monthly returns over the prior 60 months. Using monthly returns and characteristics for a broad sample of U.S. common stocks during July 1926 through December 2010, along with various benchmark data, he finds that: Keep Reading

Are popular exchange-traded products (ETP) such as VXX (iPath S&P 500 VIX Short Term Futures) and VXZ (iPath S&P 500 VIX Mid-Term Futures), designed to track specific S&P 500 VIX futures constant maturity index series, good hedges for stock portfolios? In their June 2012 paper entitled “Are VIX Futures ETPs Effective Hedges?”, Geng Deng, Craig McCann and Olivia Wang investigate whether these ETPs effectively hedge basic U.S. stock portfolios or exchange-traded funds (ETF) that leverage U.S. stock market indexes. Because the ETPs are only very recently available, they use the one-month (SPVXSP) and five-month (SPVXMP) S&P 500 VIX futures constant maturity indexes as proxies for them. They examine the hedging effectiveness of these indexes for five stock portfolios: 100% SPDR S&P 500 (SPY); 100% Vanguard Total Stock Market Index Fund (VTSMX); 80% VTSMX and 20% Vanguard Total Bond Market Index Fund (VBMFX); 60% VTSMX and 40% VBMFX; and, 40% VTSMX and 60% VBMFX. They also examine the hedging effectiveness of these indexes for three 2x-leveraged exchange-traded funds (ETF): ProShares Ultra S&P500 (SSO); ProShares Ultra QQQ (QLD); and, ProShares Ultra Dow30 (DDM). They compute optimal hedge ratios using consecutive (non-overlapping) 26-week lagged regressions of weekly total returns of each portfolio/ETF versus weekly returns of the hedging instrument. Using weekly data for all portfolio funds and VIX futures indexes since December 2005, and for leveraged ETFs since late July 2006, all through mid-April 2012, they find that: Keep Reading