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Volatility Effects

Reward goes with risk, and volatility represents risk. Therefore, volatility means reward; investors/traders get paid for riding roller coasters. Right? These blog entries relate to volatility effects.

Applying Beta to Portfolios of ETFs

Is beta an effective tool for selecting exchange-traded funds (ETF)? In their October 2010 paper entitled “Black Swans, Beta, Risk, and Return”, Javier Estrada and Mari­a Vargas investigate the usefulness of beta as a metric for constructing portfolios of country and industry ETFs. They use the MSCI world market index (consisting of developed markets only before 1988 and both developed and emerging markets thereafter) as the beta reference. They calculate beta based on a rolling historical window of the most recent 36-60 months (depending on data availability). They arbitrarily define black swan months as those with a world market index return of at least 5% down or up. Using monthly total returns of MSCI indexes for 47 countries (23 developed and 24 emerging) and 57 industries from the earliest month available for each (1970 for the oldest) through December 2009, they find that: Keep Reading

Volatility and Valuation with High-frequency Trading

Does high-frequency trading amplify noise and thereby reduce the signal-to-noise ratio in stock returns? In his August 2010 paper entitled “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Frank Zhang examines the effect of high-frequency trading on stock price volatility and on incorporation of fundamental news into price. He defines high-frequency trading as that driven by fully automated trading strategies with very high trading volume and extremely short holding periods ranging from milliseconds to minutes and possibly hours (typically not overnight). He estimates the volume of high-frequency trading as the residual after accounting for institutional and individual investor activities. Using price, trading and institutional holdings data for a broad sample of U.S. stocks from the first quarter of 1985 through the second quarter of 2009, he finds that: Keep Reading

Leverage Stock Investments While Young?

Should long-term investors view their retirement portfolios more like houses than savings plans? In other words, should they start out with considerable leverage and draw the leverage down gradually over time? In their October 2010 paper entitled “Diversification Across Time”, Ian Ayres and Barry Nalebuff investigate the effects of initially implementing and then gradually phasing out leverage for long-term (retirement) equity investment. This strategy exploits the large present value of investments made early in life, while protecting accumulated wealth from equity market volatility late in life. Tests limit initial leverage to 200%. Using return data for U.S. stocks and margin interest rate estimates over the period 1871 through 2009, they conclude that: Keep Reading

Hedging Crashes: Volatility Futures vs. Index Puts

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

Average Stock Variance as a Market Indicator

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

Negative Idiosyncratic Risk Premium?

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

Volatility Concentrations Are Bearish?

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

Exploiting the Predictability of Volatility

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

Momentum and Portfolio Risk

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

Does Volatility Selectively Filter Good and Bad Days?

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

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