Volatility Effects

How do stock index volatility and variance futures contracts compare with stock index put options as hedges against market crashes? In their August 2010 paper entitled “Using Volatility Instruments as Extreme Downside Hedges”, Bernard Lee and Yueh-Neng Lin investigate the effectiveness of stock index volatility and variance futures contracts as extreme downside hedges and compare this effectiveness to that of out-of-the-money index put options. Specifically, they compare the outcomes of hedging a long Standard & Poor’s Depository Receipts (SPY) position via 1-month and 3-month rolling positions in S&P 500 Volatility Index (VIX) futures contracts, S&P 500 3-month Variance Futures (VT) contracts and 10% out-of-the-money (OTM) S&P 500 Index put options with reasonable hedge trading frictions. Using price data for SPY, VIX and VT futures contracts and index put options spanning 6/10/04-10/14/09 for 1-month rolling hedges and 7/19/04-9/9/09 for 3-month rolling hedges, they find that: Keep Reading

Does average volatility of individual stock prices, as a measure of market risk, usefully predict stock market behavior? In the June 2010 draft of their paper entitled “Average Stock Variance and Market Returns: Evidence of Time-Varying Predictability at the Daily Frequency”, Jason Chen, Hernán Ortiz-Molina and Stacy Zhang investigate the ability of a daily measure of average stock variance to predict next-day market returns. Using daily market returns, daily returns and market capitalizations for a broad sample of individual stocks and daily yields for Treasury bills spanning 1926-2008, they find that: Keep Reading

Conventional theory holds that financial markets reward risk (volatility) with return. Do stocks with relatively high volatilities in fact generate relatively high returns? In his April 2010 paper entitled “Low Risk and High Returns: Evidence from the German Stock Market”, Stefan Koch examines the relationship between past idiosyncratic volatility and future returns for individual German stocks. Using daily stock return and firm characteristics data for a broad sample of German firms spanning 1974-2006, he concludes that: Keep Reading

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