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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.

Does a Long-Term Moving Average Indicator Predict Big Days?

A reader offered the following observation and question: “For many market observers, the 200-day moving average is the point of being in or out of the market. Does being above or below the 200-day moving average make a material difference with respect to missing the the best/worst 10, 20 or 100 days?” To check, we return to the data set for our “Trend Implications of Big Up and Down Days”, which identifies the 40 biggest up days (daily return > 3.50%) and the 40 biggest down days (daily return < -3.09%) for the S&P 500 index during January 1950 through November 2007. Calculating the 200-day moving average (MA) at the close for each day just before these 80 biggest up/down days, we find that: Keep Reading

Trend Implications of Big Up and Down Days

A reader asked:”Do big up days tend to occur during down trends as counter-move rallies (meaning that big down days and big up days tend to cluster during downtrends)?” To check for clustering, we compare the dates of big up and down days for U.S. stock market averages. To check whether these dates tend to occur during downtrends, we examine returns during the 63 trading days before and the 63 trading days after these dates. Using daily returns for the Dow Jones Industrial Average (DJIA) during October 1928 through November 2007 and the S&P 500 index during January 1950 through November 2007, we find that: Keep Reading

Misunderestimating Volatility?

Are “intuitive statistics” good enough for investing? In their brief March 2007 paper entitled “We Don’t Quite Know What We Are Talking About When We Talk About Volatility”, Daniel Goldstein and Nassim Taleb report the results of a simple test of the ability of portfolio managers, traders, quantitative analysts and financial engineering graduate students to distinguish between two widely used measures of volatility: mean absolute deviation and standard deviation. Based on responses from 87 individuals to a survey question giving the mean absolute deviation for a normal distribution of stock returns and asking for the standard deviation, they find that: Keep Reading

Sources of Volatility’s Predictive Power for Stock Returns

Past research finds that stocks with low (high) short-term historical volatility tend to outperform (underperform). What causes this relationship? In the November 2007 update of their paper entitled “Volatility Spreads and Expected Stock Returns”, Turan Bali and Armen Hovakimian examine the similarities and differences between realized (historical) volatility and implied volatility in the context of power to predict stock returns. Using stock price/fundamentals data for a broad range of stocks and volatilities implied by associated options with near-term expiration dates over the period January 1996-January 2005, they find that: Keep Reading

(Low) Volatility as an Indicator of Persistent Hedge Fund Outperformance

Market conditions vary considerably across the business cycle, presumably affecting the opportunity set for a given investing style/strategy. What are the return characteristics that predict which hedge funds can best navigate changing economic conditions? In his 2007 paper entitled “The Sustainability of Hedge Fund Performance: New Insights”, Daniel Capocci decomposes hedge fund returns to determine how investors can reliably identify funds that outperform equity and bond indexes in both bull and bear markets. Using monthly return data for the 1994-2002 business cycle from two sources (3,060 individual funds and 907 funds of funds) to investigate 14 potentially useful persistence discriminators, he concludes that: Keep Reading

The Long and Short of Beta

Beta measures the volatility of a stock with respect to the broad market. However, after accounting for the value premium and size effect, the generally accepted beta has no predictive power for future stock returns. Is that all there is to beta? In their May 2007 paper entitled “Long-Term and Short-Term Market Betas in Securities Prices”, Gerard Hoberg and Ivo Welch decompose beta into short-term (the last 12 months) and long-term (one to ten years ago) components and investigate whether these components can separately forecast stock returns. Using daily stock prices and financial data for a large sample of companies (an average of over 3,300 firms per month) over the period 1962-2005, they find that: Keep Reading

Low Risk and High Return?

Stocks with high historical volatility should produce high returns as reward for extra risk. Shouldn’t they? In the April 2007 version of their paper entitled “The Volatility Effect: Lower Risk without Lower Return”, David Blitz and Pim van Vliet examine the relationship between long-term (past three years) historical return volatility and risk-adjusted return for stocks worldwide. Ranking stocks based on historical volatility has some similarity to ranking them based on beta. Using monthly price and fundamental data for a large number of large-capitalization stocks over the period December 1985 through January 2006, they find that: Keep Reading

Fear Factor?

In one of the financial markets alternate universes, anchored on the Fama-French three-factor model, the central explanatory theme is reward-for-risk derived from market (equity) premium , the value premium and the size effect. Within this model, each factor presents to investors an opportunity to boost mean return in exchange for bearing more violent variation of return. The Carhart four-factor model adds a momentum effect as an additional risk factor. Should implied market volatility (the “investor fear gauge”), as measured by such variables as the CBOE Volatility Index (VIX) be a fifth risk factor? In their February 2007 paper entitled “Fear and the Fama-French Factors”, Robert Durand, Dominic Lim and Kenton Zumwalt examine the case for adding investor expectations for overall market volatility (a “fear factor”) to establish a five-factor model of equity market behavior. Using daily data for the period 2/93-12/03, they find that: Keep Reading

Screening for Fear When Portfolio Building

Implied idiosyncratic volatility is the “investor fear gauge” or perceived risk for an individual stock based on the pricing of its associated options, as contrasted with: (1) overall stock market volatility as measured by variables such as the CBOE Volatility Index (VIX); and, (2) realized idiosyncratic volatility based on variation of the stock’s historical price. Can investors use the return due this perceived risk in an individual stock as a building block in constructing outperforming portfolios? In their December 2006 paper entitled “Idiosyncratic Implied Volatility and the Cross-Section of Stock Returns”, Dean Diavatopoulos, James Doran and David Peterson examine the relationship between idiosyncratic implied volatility and 30-day, 60-day and 91-day future returns for different kinds of equities. Using daily data on 240 stocks with actively traded options for the period January 1996 to June 2005, they find that: Keep Reading

Making Money with Options Based on Superior Volatility Forecasts

Are there systematic errors in market expectations about the future volatilities of individual stock prices? If so, what reliable strategy could a trader use to exploit these errors? In their August 2006 paper entitled “Option Returns and the Cross-Sectional Predictability of Implied Volatility”, Amit Goyal and Alessio Saretto examine the complete range of implied stock price volatilities for all U.S. equity options to devise an volatility forecasting model more efficient than that inherent in the market. They then test the model’s ability (out of sample) to identify outperforming options trading strategies that exploit this market inefficiency. Using daily data for all U.S. equity options over the period January 1996 to May 2005, they conclude that: Keep Reading

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