Volatility Effects

Does predictable end-of-day rebalancing behavior of leveraged exchange-traded funds (ETF) present trading opportunities? In their October 2012 paper entitled “Intraday Share Price Volatility and Leveraged ETF Rebalancing”, Arthur Rodier, Edgar Haryanto, Pauline Shum and Walid Hejazi examine: (1) the effects of leveraged ETF rebalancing activities on late-day market volatility; and, (2) the profitability of trading strategies designed to anticipate these rebalancing activities. For the former, they measure the average intraday volatility of 346 large-capitalization stocks within indexes tracked by many leveraged ETFs. For the latter, they consider intraday trades of ProShares Ultra S&P500 (SSO) and ProShares UltraShort S&P500 (SDS) according to whether anticipated late-day rebalancing pressure is likely to be bullish or bearish, respectively. Using intraday returns for the S&P 500 Index, 346 individual S&P 500 stocks, SSO and SDS during late June 2006 through mid-July 2011, they find that: Keep Reading

Do allocations aimed at managing volatility beat simple equal weighting as applied to the cheapest third of 32 country stock markets based on 10-year cyclically adjusted price-to-earnings ratio (CAPE, or P/E10). In their October 2012 paper entitled “Global CAPE Model Optimization”, Adam Butler, Michael Philbrick, Rodrigo Gordillo and Mebane Faber compare the following six volatility management strategies to EW for a low-P/E10 equity index portfolio:

1. Equal Volatility: each selected index contributes equally to the volatility of a fully invested portfolio.
2. Fixed Volatility Limit: allocates to each selected index up to a daily volatility limit of 1%, with any remaining funds going to cash.
3. Portfolio Volatility Target: allocates equally to all selected indexes with a portfolio-level annualized volatility target of 10%, going to cash when above target (but not applying leverage when below).
4. Risk Parity with Portfolio Volatility Target: a combination of strategies 1 and 3; allocates such that all indexes contribute equally to portfolio volatility with a portfolio-level annualized volatility target of 10%, going to cash when above target (but not applying leverage when below).
5. Minimum Variance: minimum variance via a combination of low volatilities and low correlations per Modern Portfolio Theory, but always fully invested. 
6. Minimum Variance with Portfolio Volatility Target: minimum variance allocations adjusted at the portfolio level to target 10% annualized portfolio volatility, going to cash when above target (but not applying leverage when below).

For all strategies, they estimate prescribed volatilities based on the last 60 days of returns, with monthly portfolio reformation. Using daily and monthly total returns for the 32 country stock indexes during April 1999 through August 2012, they find that: Keep Reading

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