Volatility Effects

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

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

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

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

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

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

Does a willingness to pay more or less for options than indicated by recent actual levels of stock return volatility reflect the current level of investor/trader risk aversion? In other words, does the gap between option-implied and historical stock return volatilities provide a tradable measure of fearfulness? In the September 2006 draft of their paper entitled “Expected Stock Returns and Variance Risk Premia”, Tim Bollerslev, George Tauchen and Hao Zhou investigate the predictive power of the implied-historical volatility gap for future stock returns. Using monthly data for the S&P 500 index (VIX for implied volatility and a summation of five-minute squared returns for historical volatility) for the period 1990-2005, they find that: Keep Reading

Do short sellers avoid highly volatile stocks, and thereby leave them overvalued? If so, when short sellers do attack volatile stocks, is the level of overvaluation therefore compelling? In the August 2006 update of their paper entitled “Costly Arbitrage and Idiosyncratic Risk: Evidence from Short Sellers”, Ying Duan, Gang Hu and David McLean test the hypothesis that short sellers tend to avoid stocks with high idiosyncratic risk because of the high cost of hedging such risk. Using data for stock prices, short interest levels and other factors spanning 1988-2003, they find that: Keep Reading

Can you trade on the CBOE Volatility Index (VIX), the “investor fear gauge,” or not? If so, what should you trade and should your trades be short-term or long-term? In their September 2005 paper entitled “VIX Signaled Switching for Style-Differential and Size-Differential Short-term Stock Investing”, Dean Leistikow and Susana Yu test the usefulness of VIX level as a signal for short-term switching between: (1) value and growth stock indexes; and, (2) small-capitalization and large-capitalization stock indexes. They note that “…VIX can be viewed as a market-determined forecast of short-term market volatility that, by construction, has a constant one-month forecast horizon.” They determine signals according to whether VIX is high or low compared to its 75-day moving average. They examine index returns for 1 day and 5 days after a VIX signal. Using data for the VIX and for various Standard & Poor’s and Russell stock indexes from the early 1990s through 2004, they find that: Keep Reading

Conventional wisdom holds that: (1) risk begets reward; and, (2) volatility is a manifestation of risk. Exceptionally high volatility in individual stock prices should, therefore, indicate future excess returns in those stocks. In their May 2006 paper entitled “The Relation between Time-Series and Cross-Sectional Effects of Idiosyncratic Variance on Stock Returns in G7 Countries”, Hui Guo and Robert Savickas investigate why the realized idiosyncratic volatility (beta) of individual stocks correlates negatively with future returns — why there is a penalty instead of a reward for this apparent risk. Using two sets of U.S. data (1926-2005 and 1963-2005) and one set of international data (1973-2003), they conclude that: Keep Reading