Volatility Effects

A reader commented and asked:

“The article ‘Volatility is a Bear Market Signal’ by David Schwartz measures volatility not in terms simply of big percentage days, but a cluster of such days within a specified time period (movements in excess of 1% on FTSE on at least 20 of 40 consecutive trading days).  The prediction made in 2007 looks to have been well founded, giving the strategy an apparent success rate of 8 out of 9 hits if the author’s data can be trusted. What do you think?”

To check this signal independently, we measure returns at intervals of 5, 10, 21, 63, 126 and 252 trading days after onset of concentrations of days with close-to-close volatility greater than 1% for the S&P 500 Index. Using daily closes of the index for January 1950 through May 2010, we find that: Keep Reading

There is a stream of research finding that asset price volatility is much more predictable than returns. Is there a way to extract economically meaningful gains from the predictability of volatility. In his March 2010 paper entitled “Alpha Generation and Risk Smoothing using Volatility of Volatility” (the National Association of Active Investment Managers’ 2010 Wagner Award winner), Tony Cooper investigates dynamic leverage as a means to exploit volatility predictions by applying higher (lower) leverage when returns compound rapidly (slowly). Just before the close each trading day, he executes an algorithm to predict the volatility for the next trading day and adjusts leverage for that predicted volatility at the close. Using daily closes of several broad stock market indexes (excluding dividends) spanning 1885-2009, he finds that: Keep Reading

Do measures of momentum portfolio risk affect returns and return components? In their April 2010 paper entitled “Asymmetric Momentum Effects Under Uncertainty”, David Kelsey, Roman Kozhan and Wei Pang investigate how the profitability of a momentum hedge strategy varies with portfolio firm-level risk. They use six measures of firm-level risk: (1) size (market capitalization); (2) age (since listing); (3) number of analysts following; (4) dispersion of analyst earnings forecasts; (5) realized volatility of weekly stock returns over the past year; and, (6) cash flow volatility. Using stock return, firm fundamentals and analyst earnings forecast data for a broad sample of U.S. stocks spanning 1983-2006, they conclude that: Keep Reading

A reader asked whether “good” or “bad” days are more likely to occur during times of high or low volatility. One measure of (realized) volatility is standard deviation of returns, such as the standard deviation of daily returns over the past 21 trading days. Using daily returns for the S&P 500 Index over the period January 1950 through April 2010, we find that: Keep Reading

Is fear of rare stock market plunges a major factor in the pricing of equities? In the March 2010 version of their paper entitled “Tails, Fears and Risk Premia”, Tim Bollerslev and Viktor Todorov apply highly empirical methods to examine the distribution of large rare events and the effects of these events on the equity risk premium and the volatility risk premium. Specifically, they define and measure an Investors Fears index driven by: (1) slow variation in investment opportunities (for example, due to economic and demographic influences); and, (2) large rare events (for example, due to economic and political crises). Using high-frequency (five-minute) prices for S&P 500 Index futures prices during 1990-2008 and prices for near-to-expiration out-of-the-money S&P 500 Index options during 1996-2008, they conclude that: Keep Reading

Do stocks exhibit predictable volatility behavior near their 52-week highs and lows? In their March 2010 paper entitled “How the 52-Week High and Low Affect Beta and Volatility”, Joost Driessen, Tse-Chun Lin and Otto Van Hemert analyze whether a stock’s beta, return volatility and implied volatility change as its price approaches a 52-week high or low and after its price breaches this high or low. Using price data for a broad sample of U.S. stocks for July 1963 through December 2008 and option price data for January 1996 through September 30, they find that: Keep Reading

In the February 2010 version of their paper entitled “Long-run Idiosyncratic Volatilities and Cross-sectional Stock Returns”, Xuying Cao, and Yexiao Xu decompose idiosyncratic volatility into long-run (trend derived from monthly data) and short-run (residual noise derived from daily data) components to investigate why some studies find that idiosyncratic volatility and future stock return relate negatively and others find they relate positively. Using daily stock return data for a broad sample of U.S. stocks spanning January 1963 through June 2008, they conclude that: Keep Reading

A reader commented and asked: “It seems there are indeed systems in Collective2 that make money on a walk-forward basis even considering trading frictions. VT26 is one of them (for autotrading at Collective2, you can assume on average $8.50 per contract per round turn). How does this stand versus your skeptical approach?” The author of VT26 describes it briefly as a 100% automated volatility breakout system with priorities on steady profits and small drawdowns. He provides further description of the strategy in “Report on Systematic Portfolio VT26.” Using trade-by-trade data for VT26 for the period 4/9/08 through 3/8/10 (2,279 closed trades over 23 months across ten futures markets), we find that: Keep Reading

Does positive feedback trading, indicated by an adjusted measure of return autocorrelation, enhance momentum profitability? In the February 2010 version of their paper entitled “Positive Feedback Trading Activities and Momentum Profits” [apparently removed from SSRN, thus casting doubt on its credibility], Thomas Chiang, Xiaoli Liang and Jian Shi examine the relationship between positive feedback trading and profitability of momentum strategies. The momentum parameters for their investigation are a six-month ranking interval followed by a six-month holding interval. Measurement of positive feedback trading is for a six-month window coinciding with the momentum ranking interval. Using daily stock return data for a broad sample of U.S. stocks spanning 1985-2005, they conclude that: Keep Reading

How much of the variation in stock returns flows from actual (realized or backward-looking) and implied (forward-looking) market volatilities? In the January 2010 version of his paper entitled “Option Implied Volatility Factors and the Cross-Section of Market Risk Premia”, Junye Li investigates the effectiveness of a three-factor model of stock returns based on market return (beta), diffusion volatility (moderate and persistent component) and jump volatility (large and mean-reverting component). The author also examines how the value premium and size effect relate to the two volatility factors and how relying only on realized market volatility affects results. Using weekly (Wednesday) data for the S&P 500 Index, S&P 500 Index options (filtering out options with extremely long/short durations, extreme moneyness and low activity) and the S&P 500 Volatility Index (VIX) spanning January 1997 through September 2008 (608 weeks), he concludes that: Keep Reading