Volatility Effects

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

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

Does the tendency of stock market volatility measures to persist offer an exploitable short-term reversion to mean? In other words, can traders win on average by speculating that market volatility spikes will soon reverse? To check, we first test for short-term reversion of the implied volatility of the S&P 500 Index (VIX) over its available history. We then test for exploitability of any discovered reversion via the investable iPath S&P 500 VIX Short-Term Futures ETN (VXX), which seeks to replicate the return on short-term VIX futures. Using daily closes of VIX for January 1990 through December 2012 (5,794 trading days) and daily adjusted closes of VXX for February 2009 through December 2012 (987 trading days), we find that: Keep Reading

Does combining different portfolio performance enhancement concepts actually improve outcome? In their December 2012 paper entitled “Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach”, Wouter Keller and Hugo van Putten investigate the effects of combining momentum, volatility and correlation selection criteria to form an equally weighted portfolio of the three best funds from a set of mutual fund proxies for seven asset classes, as follows:

1. To follow trend, rank funds from highest to lowest lagged total return (relative momentum).
2. To suppress volatility, rank funds from lowest to highest volatility (standard deviation of daily returns).
3. To enhance diversification, rank funds from lowest to highest average pairwise correlation of daily returns.
4. To avoid drawdown, replace with cash any selected fund that has a negative lagged return (intrinsic or absolute momentum). 

Their seven asset class proxies are index mutual funds for U.S. stocks (VTSMX), developed market stocks outside the U.S. and Canada (FDIVX), emerging market stocks (VEIEX), mid-term U.S. Treasuries (VBMFX), short-term U.S. Treasuries (VFISX), commodities (QRAAX) and real estate (VGSIX). They use a default lagged measurement interval of four months for all four selection criteria. Their method of combining rankings for relative momentum, volatility and correlation is simple weighted average (with default weightings of 1, 0.5 and 0.5, respectively). They assume momentum calculations occur at the end of each month, with portfolio changes at the beginning of the next month. Using daily closing prices in U.S. dollars for the seven mutual funds from mid-1997 through mid-December 2012, they find that: Keep Reading

A subscriber suggested review of Empiritrage’s Volatility-Based Allocation (VBA). This strategy applies two monthly signals to an equally weighted portfolio of asset class total return proxies to determine whether to be in each proxy or cash, as follows:

• Step 1: If the 10-day simple moving average (SMA) of the S&P 500 Volatility Index (VIX) is above its 30-day SMA (risk off), substitute the risk-free asset for all asset class proxies.
• Step 2: If the 10-day simple moving average (SMA) of VIX is below its 30-day SMA (risk on), invest in each asset class proxy for which the respective two-month SMA is above the 12-month SMA, and otherwise in the risk-free asset.

Empiritrage’s simulation of VBA employs equal allocations each month to each of five asset class proxies (U.S. stocks, non-U.S. developed market stocks, emerging market stocks, real estate and long-term U.S. government bonds) or to U.S. Treasury bills (T-bills) as signaled, ignoring trading frictions, during March 1986 through August 2012. They find that VBA “dominates” an allocation based only on individual asset class proxy SMAs. However, indexes do not account for the costs of maintaining tradable assets, and the costs of switching between risk assets and cash may be material. For another perspective, we replicate VBA (with switching frictions) using the following exchange-traded funds (ETF) and estimated return on cash:

SPDR S&P 500 (SPY)
iShares MSCI EAFE Index (EFA)
iShares MSCI Emerging Markets Index (EEM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (risk-free rate)

Using daily closes for VIX since March 2003 and monthly closes for the ETFs and risk-free rate since April 2003 (limited by inception of EEM), we find that: Keep Reading

Nassim Taleb introduces his 2012 book, Antifragile, Things That Gain from Disorder, as “…my central work. I’ve had only one master idea, each time taken to the next step, the last step–this book–being more like a big jump. I am reconnected to my ‘practical self,’ my soul of a practitioner, as this is a merger of my entire history as practitioner and ‘volatility specialist’ combined with my intellectual and philosophical interests in randomness and uncertainty… …the relationship of this book to The Black Swan would be as follows:…Antifragile would be the main volume and The Black Swan its backup of sorts, and a theoretical one, perhaps even its junior appendix. Why? Because The Black Swan (and its predecessor, Fooled by Randomness) were written to convince us of a dire situation, and worked hard at it; this one starts from the position that one does not need convincing that (a) Black Swans dominate society and history (and people, because of ex post rationalization, think themselves capable of understanding them); (b) as a consequence, we don’t quite know what’s going on, particularly under severe nonlinearities…” For investors, key points are: Keep Reading

Does the S&P 500 implied volatility index (VIX) behave predictably after up or down streaks? To check, we look at next-day percentage changes in VIX after up and down streaks ranging from two to seven trading sessions. To test exploitability, we repeat the analysis on the much shorter sample available for the iPath S&P 500 VIX ST Futures ETN (VXX). Using daily closes of VIX from the beginning of January 1990 and VXX from the end of January 2009, both through mid-November 2012, we find that: Keep Reading

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