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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.

Commodity Market Price Statistics

How do the daily price statistics of commodities differ, and how do they compare with those for equities? In their May 2011 paper entitled “The Dynamics of Commodity Prices”, Chris Brooks and Marcel Prokopczuk examine the daily price statistics for six major commodity markets (crude oil, gasoline, gold, silver, soybeans and wheat) individually and relative to each other and the equity market. Using daily spot prices for the commodities and daily levels of the S&P 500 Index for January 1985 through March 2010 (over 25 years), they find that: Keep Reading

Individual Stocks Versus Portfolios

Can portfolios exhibit properties not evident from, or even contrary to, average properties of their component assets? In the April 2011 draft of their paper entitled “The Sources of Portfolio Returns: Underlying Stock Returns and the Excess Growth Rate”, Jason Greene and David Rakowski provide a framework for distinguishing two sources of portfolio return: (1) weighted average growth rates of component assets; and, (2) portfolio “excess growth rate” derived from diversification (component return volatilities and correlations). They apply this framework to investigate equity portfolio equal-weighting versus value-weighting, and to isolate the sources of the size effect and the value premium. They establish consistency in return measurements by matching rebalancing frequency and return measurement interval. Using monthly returns and firm characteristics for a broad sample of U.S. stocks over the period 1960 through 2009, they find that: Keep Reading

Extracting a Volatility Premium with Equity Options?

Are options for volatile stocks overpriced? In the September 2010 version of their paper entitled “Cross-Section of Option Returns and Stock Volatility”, Jie Cao and Bing Han investigate the relationship between option return and price volatility of the underlying stock. The focus on delta-hedged positions in options and underlying stocks calibrated such that the combination is insensitive to stock price changes. For most analyses, they use the closing bid-ask midpoint as the option price. Using price and trading data for approximately at-the-money individual stock options about 1.5 months from expiration (filtered for reliability) on approximately 6,000 underlying U.S. stocks over the period January 1996 through October 2009 (about 200,000 observations each for puts and calls), they find that: Keep Reading

Interactions of Momentum, Valuation and Idiosyncratic Volatility

For what kind of stocks does momentum work best? In his March 2011 paper entitled “Growth Options, Idiosyncratic Volatility and Momentum”, Umut Celiker investigates the interactions among valuation (market to-book ratio, arguably a proxy for firm growth opportunities), valuation uncertainty (idiosyncratic volatility) and stock price momentum. For calendar-time analysis, he ranks stocks each month into quintiles by past six-month return, with a skip-month, and holds an equal-weighted hedge portfolio that is long the top (winner) quintile and short the bottom (loser) quintile for the next six months. For event analysis, he extends the holding interval to 60 months to explore momentum persistence/reversal. He computes stock idiosyncratic volatility relative to the S&P 500 Index over the prior 36 months. He defines the up (down) market state as the top 80% (bottom 20%) of months based on 60-month past value-weighted market returns averaged for each of the lagged six months. Most analysis focuses on the up market state. Using monthly firm accounting and stock price data for a broad sample of U.S. stocks over the period 1965 to 2008, he finds that: Keep Reading

Diversifying with Equity Volatility Exposure?

Can diversification via allocations to volatility-related securities enhance the absolute and risk-adjusted returns of equity portfolios? In other words, can investors construct useful asset classes from equity volatility? In their early 2010 paper entitled “Volatility Exposure for Strategic Asset Allocation”, Ombretta Signori, Marie Briere and Alexandre Burgues investigate potential benefits to long-term U.S. equity investors of including two volatility-related assets: (1) a rolling dynamic long position in VIX futures that is bigger when VIX is relatively low and smaller when it is relatively high; and, (2) a rolling short position in one-month variance swap contracts to exploit the tendency of option-implied volatility to exceed realized volatility (volatility risk premium). The former lowers the downside risk of holding equities, and the latter offers returns from selling “insurance” against volatility. Because the return distributions of such volatility investments are clearly non-normal, the authors employ a risk-return optimization approach that takes distribution skewness and kurtosis into account. Using S&P 500 Index, VIX, VIX futures and S&P 500 Index variance swap contract data as available over the period February 1990 through August 2008, they find that: Keep Reading

Institutional Ownership, Idiosyncratic Volatility and Stock Returns

Is the number of institutional owners of a stock, arguably a proxy for general investor awareness and demand, an important factor in current and future pricing of the stock? In their February 2011 paper entitled “What Makes Stock Prices Move? Fundamentals vs. Investor Recognition”, Scott Richardson, Richard Sloan and Haifeng You investigate the role of institutional ownership breadth in size-adjusted stock price dynamics. They focus on institutional investors with greater than $100 million in equity holdings, as reported quarterly to the SEC via Form 13F. They measure institutional ownership breadth as the number of institutions holding a particular stock relative to the number of institutions holding any given stock. They measure firm size based on total assets. They impose a three-month lag on data to ensure calculations use only publicly available information. Using stock returns, institutional ownership data and accounting data for a broad sample of U.S. firms over the period 1986 through 2008 (35,526 firm years), they find that: Keep Reading

Combining Tail Risk Management and Modern Portfolio Theory

Does combining avoidance of fat tail losses with a traditional portfolio optimization strategy enhance performance? In her January 2011 paper entitled “The Economic Value of Controlling for Large Losses in Portfolio Selection”, Alexandra Dias investigates the effectiveness of combining tail loss risk management with minimum variance efficiency. This approach essentially seeks to add avoidance of Black Swans to the benefit of diversification. The investigation consists of testing four long-only strategies using 224 months of rolling historical returns on all possible combinations of three Dow Jones Industrial Average (DJIA) stocks by choosing each month: (1) the minimum variance portfolio with the smallest variance (benchmark strategy); (2) the minimum variance portfolio with the smallest probability of a large loss; (3) the minimum variance portfolio with the thinnest losses tail; and, (4) the minimum Value at Risk (VaR) portfolio with the smallest VaR. Strategies (2), (3) and (4) are alternatives for managing return distribution tail risk. Using monthly returns for the 24 DJIA stocks for which which prices are available during February 1973 through June 2010 (allowing 2,024 combinations of three stocks), she finds that: Keep Reading

Feasibility of Diversifying Equities with Volatility Futures

Can investors straightforwardly diversify equity portfolios with volatility futures? In the January 2011 draft of their paper entitled “The Hazards of Volatility Diversification”, Carol Alexander and Dimitris Korovilas explore the potential benefits and costs of combining ‘buy-and-hold’ positions in volatility futures with a long-term equity portfolio. Specifically, they examine diversification of long exposure to the S&P 500 Index via S&P Depository Receipts (SPY) with a rolling long position in VIX futures. They distinguish “diversification” from “hedging” based on permanence of positions. They consider three VIX futures strategies using one-month-to-expiration, three-month-to-expiration or longest-maturity-available series, with rollover either at or five business days before expiration. Using daily trading data for VIX futures, SPY and the 1-month Treasury bills from March 26, 2004 through December 31, 2010 (about 69 months), they find that: Keep Reading

Predicting Individual Stock Extreme Returns

Are there stock/firm characteristics that usefully predict which stocks will exhibit extreme returns? In their January 2011 paper entitled “Predicting Extreme Returns and Portfolio Management Implications”, Kevin Krieger, Andy Fodor, Nathan Mauck and Greg Stevenson investigate the predictability of extreme returns for individual stocks and the practical import of such predictability for investment portfolios. The define stocks with extreme returns by year as those within the highest 3% or lowest 2.5% of equity returns, or those delisted for bankruptcy or bankruptcy-like reasons. Using implied volatility, stock price/return, trading volume, firm age, accounting and analyst forecast data for the period is 1996 through 2008 (1,100 to 1,500 firms per year with sufficient data), they find that: Keep Reading

Systematic Overpricing of High-beta Assets?

Is there a reliable and exploitable cross-sectional relationship between beta and future returns? In the October 2010 draft of their paper entitled “Betting Against Beta”, Andrea Frazzini and Lasse Pedersen investigate exploitability of historical beta within U.S. equities and 19 other stock markets, across 20 global equity markets, and for Treasuries, corporate bonds and commodity futures. They argue that leverage-inhibited investors overweight high-beta assets as a leverage substitute (preferring unleveraged risky assets to leveraged safe assets), and this demand results in overpricing of high-beta assets relative to low-beta assets. Their principal investigatory tool is a market-neutral “betting-against-beta” portfolio that is long low-beta assets (leveraged up to an aggregate beta of one) and short high-beta assets (de-leveraged to an aggregate beta of one), reformed monthly. Calculations of historical beta vary somewhat depending on the data available. Using asset return data as available through 2009 for a variety of assets and asset classes (for example, 1926-2009 for individual U.S. stocks), they find that: Keep Reading

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