Volatility Effects

Do investors exploiting common stock return anomalies risk extraordinarily large drawdowns during market crashes? In their May 2016 paper entitled “Can Exposure to Aggregate Tail Risk Explain Size, Book-to-Market, and Idiosyncratic Volatility Anomalies?”, Sofiane Aboura and Eser Arisoy investigate whether portfolios based on the size, book-to-market ratio and idiosyncratic volatility effects bear elevated stock market tail risk. They measure market tail risk as change in VIX Tail Hedge Index (VXTH), which hedges extreme drops in the S&P 500 Index by holding the index and one-month far out-of-the-money (30-delta) call options on the CBOE Volatility Index (VIX). They test sensitivity of size and book-to-market factors to overall risk and tail risk by adding change in VIX (market volatility risk factor) and change in VXTH (market tail risk factor) to the Fama-French three-factor (market, size, book-to-market) model of stock returns. They consider two equal subperiods, one containing the 2008 financial crisis, to check robustness of findings. Using monthly values of VIX and VXTH, factor model returns and U.S. Treasury bill yields during January 2007 through February 2016 (110 months), they find that: Keep Reading

Should investors buy or sell stocks experiencing unique (idiosyncratic) volatility spikes? In their August 2016 paper entitled “Unusual News Flow and the Cross-Section of Stock Returns”, Turan Bali, Andriy Bodnaruk, Anna Scherbina and Yi Tang investigate relationships among sudden increases in stock idiosyncratic volatility, unusual firm news, changes in analyst earnings forecast dispersion, short selling and future returns. They identify idiosyncratic volatility shocks as large deviations from the volatility predicted out-of-sample by a regression model that accounts for market, size and book-to-market effects. They identify unusual news flow using Thomson-Reuters News Analytics data (covering 41 media) by comparing the number of stories about a firm in the current month to the average monthly coverage the prior four months, measured overall and separately for positive, negative and neutral stories. They measure changes in analyst earnings forecast dispersion (standard deviation divided by mean) based on data from I/B/E/S as the difference between current dispersion and dispersion two months ago. They measure data on shorting demand and utilization (shares borrowed divided by shares available for lending) using data from Markit. Using monthly values of the specified data from various inceptions through December 2012, they find that: Keep Reading

Does the Fama-French five-factor model of stock returns (employing market, size, book-to-market, investment and profitability factors) explain the outperformance of low-volatility stocks. In their July 2016 paper entitled “The Profitability of Low Volatility”, David Blitz and Milan Vidojevic examine whether: (1) any of several models expose a conventional return-for-risk market beta effect for stocks; and, (2) the low-volatility effect is distinct from a low-beta effect. They calculate volatilities for stocks and the market using daily or monthly returns over the past year. They calculate stock betas using these volatilities and daily or monthly stock-versus-market return correlations over the past five years, with shrinkage by 1/3 toward a value of one. They include momentum (return from 12 months ago to one month ago) as an explanatory factor, even though the five-factor model does not. Using data for a broad sample of U.S. common stocks and model factors (excluding extreme outliers) during July 1963 through December 2015, they find that: Keep Reading

What are the principal strategies for exploiting the volatility and volatility skew risk premiums? In his May 2016 workshop presentation package entitled “Volatility Modelling and Trading”, Artur Sepp provides an overview of systematic volatility risk premium capture strategies. He focuses on simple rule-based strategies with monthly reformation suitable for an investable index or a proprietary strategy. He covers delta-hedged strategies for capturing the volatility/volatility skew risk premiums (straddles/strangles) and buy-write and put-write options strategies as applied to major stock indexes and liquid exchange-traded funds (ETF). He covers the following strategy elements:

1. Measuring realized volatility.
2. Forecasting expected volatility.
3. Measuring and forecasting implied and realized volatility skew.
4. Computing option delta.
5. Trading off transaction costs versus delta risk.
6. Managing tail risk.

Using relevant data for target assets during January 2005 through January 2016, he finds that: Keep Reading

How does stock pairs trading performance interact with lagged pair correlation and volatility? In her May 2016 paper entitled “Demystifying Pairs Trading: The Role of Volatility and Correlation”, Stephanie Riedinger investigates how stock pair correlation and summed volatilities influence pair selection, pair return and portfolio return. Her baseline is a conventional pairs trading method that each month: (1) computes sums of daily squared normalized price differences (SSD) for all possible stock pairs over the last 12 months and selects the 20 pairs with the smallest SSDs; (2) over the next six months, buys (sells) the undervalued (overvalued) member of each of these pairs whenever renormalized prices diverge by more than two selection phase standard deviations; and, (3) closes positions when prices completely converge, prices diverge beyond four standard deviations, the trading phase ends or a traded stock is delisted. A pair may open and close several times during the trading period. At any time, six pairs portfolios trade simultaneously. She modifies this strategy to investigate correlation and volatility effects by: (1) measuring also during the selection phase return correlations and sum of volatilities based on daily closing prices for each possible stock pair; (2) allocating each pair to a correlation quintile (ranked fifth) and to a summed volatility quintile; and, (3) randomly selecting 20 twenty pairs out of each of the 25 intersections of correlation and summed volatility quintiles. She accounts for bid-ask frictions by executing all buys (sells) at the ask (bid) and by calculating daily returns at the bid. Using daily bid, ask and closing prices for all stocks included in the S&P 1500 during January 1990 (supporting initial pair trades in January 1991) through December 2014, she finds that: Keep Reading

Does the U.S. stock market volatility risk premium (VRP), measured as the difference between the volatility implied by stock index option prices recent actual index volatility, usefully predict stock market returns? To investigate, we consider a simple VRP specification: S&P 500 Implied Volatility Index (VIX) minus standard deviation of daily S&P 500 Index returns over the past 21 trading days. Since VIX is an annualized percentage, we annualize actual daily volatility by multiplying by the square root of 252. We then relate this simple VRP to future S&P 500 Index returns and apply a VRP-related signal to time SPDR S&P 500 (SPY). Using daily data for the S&P 500 Index since December 1989, VIX since January 1990, and SPY and 13-week U.S. Treasury bills (T-bill) since the end of January 1993, all through April 2016, we find that: Keep Reading

Subscribers have proposed that asset class momentum effects should accelerate (shorter optimal ranking interval) when markets are in turmoil (bear market/high volatility). “Asset Class Momentum Faster During Bear Markets?” addresses this hypothesis in a multi-class, relative momentum environment. Another approach is to evaluate the relationship between time series (intrinsic or absolute) momentum and volatility. Applied to the S&P 500 Index and the S&P 500 Implied Volatility Index (VIX), this alternative offers a longer sample period less dominated by the 2008-2009 equity market crash. Specifically, we examine monthly correlations between S&P 500 Index return over the past 1 to 12 months with next-month return to measure strength of time series momentum (positive correlations) or reversal (negative correlations). We compare correlations by ranked fifth (quintile) of VIX at the end of the past return measurement interval to determine (in-sample) optimal time series momentum measurement intervals for different ranges of VIX. We also test whether: (1) monthly change in VIX affects time series momentum for the S&P 500 Index; and, (2) VIX level affects time series momentum for another asset class (spot gold). Using monthly S&P 500 Index levels and spot gold prices since January 1989 and monthly VIX levels since inception in January 1990, all through April 2016, we find that: Keep Reading

Do peaks in the S&P 500 Implied Volatility Index (VIX) signal positive abnormal U.S. stock market returns? If so, can investors exploit these returns? In the May 2016 version of his paper entitled “Abnormal Stock Market Returns Around Peaks in VIX: The Evidence of Investor Overreaction?”, Valeriy Zakamulin analyzes U.S. stock market returns around VIX peaks. He employs two formal methods to detect peaks:

1. When a local maximum (minimum) is at least 20% higher (30% lower) than the last local minimum (maximum), it is a peak (trough).
2. First, identify all local maximums (peaks) and minimums (troughs) within 8-day windows. Then winnow peaks and troughs and systematize alternation by: excluding peaks and troughs in the first and last 20 days; eliminating cycles (peak-to-peak or trough-to-trough) shorter than 22 days; and, excluding phases (trough-to-peak or peak-to-trough) shorter than 10 days, unless daily percentage change exceeds 30%.

He then tests for abnormal stock market returns around VIX peaks and during preceding and following intervals of rising and falling VIX. Abnormal means relative to the average market return for the sample period. Finally, he investigates whether abnormal returns around peaks are due to investor overreaction. Using daily closes for VIX and daily returns of the broad capitalization-weighted U.S. stock market during January 1990 through December 2015, he finds that: Keep Reading

Is there a best measure of investor sentiment for predicting stock market returns? In his March 2016 paper entitled “Investor Sentiment and Stock Market Returns”, Lee Smales updates relationships between stock market/portfolio returns and five sentiment measures:

1. CBOE Implied Volatility Index (VIX).
2. Baker-Wurgler composite sentiment index (readily available only through 2012).
3. American Association of Individual Investors (AAII) investor sentiment.
4. University of Michigan Consumer Sentiment Index.
5. Commitment of Traders (COT) reports from the Commodity Futures Trading Commission.

He controls for multiple economic and financial variables likely to be related to stock market returns (gross domestic product, industrial production, unemployment rate, consumer price index, Federal Funds target rate, term spread, credit spread and dividend yield). He also investigates economic recessions separately. Principal tests relate sentiment levels and changes in sentiment levels to S&P 500 Index and style/industry portfolio returns (from Kenneth French’s data library) at horizons of 1, 3, 6 and 12 months. Using monthly values of sentiment measures as available and monthly index/portfolio returns during January 1990 through December 2015, he finds that: Keep Reading

Is the VIX futures roll yield (roll return) exploitable via exchange-traded products (ETPs) designed to track direct, levered or inverse VIX futures indexes? In their March 2016 paper entitled “VIX Exchange Traded Products: Price Discovery, Hedging and Trading Strategy”, Christoffer Bordonado, Peter Molnar and Sven Samdal test abilities of the seven most traded such ETPs (VXX, XIV, TVIX, UVXY, SVXY, VIXY and VXZ) to hedge the S&P 500 Index. They also propose a trading strategy designed to capture VIX futures roll yield that pairs VXX with SPDR S&P 500 Trust ETF (SPY) as a hedge and XIV with ProShares Short S&P 500 ETF (SH) as a hedge. The strategy:

• Buys XIV+SH when nearest-month VIX futures cross above VIX by a specified relative threshold (+8%) and sells when they cross back below a threshold (+6%).
• Buys VXX+SPY when nearest-month VIX futures cross below VIX by a specified relative threshold (-8%) and sells when they cross back above a threshold (-6%).

They test SH/SPY hedges ranging from 0% to 100% of associated XIV/VXX positions, in increments of 10% (with no rebalancing while positions are open). They assume brokerage fee 0.20% and bid-ask spread 0.15% (based on historical average) to estimate trading frictions. Using simulated daily data for the ETPs before their respective inceptions (ranging from January 2009 to October 2011) and actual daily data thereafter during late June 2006 through late April 2014, they find that: Keep Reading