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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.

Best Investment Risk-Return Measure?

In their September 2011 paper entitled “The Impact of Asymmetry on Expected Stock Returns: An Investigation of Alternative Risk Measures”. Stephen Huffman and Cliff Moll investigate the relation between various measures of lagged total, downside and upside risk and future daily stock returns. Specifically, they consider the following 12 alternative risk measured over rolling intervals of the past 100 trading days: standard deviation, semi-variance, semi-deviation, skewness, kurtosis, downside risk below zero, upside risk above zero, mean absolute deviation and lower partial moments for four investor types (extremely risk averse, risk averse, risk neutral and risk seeking). Using daily returns and quarterly market valuation and firm accounting data for a broad sample of U.S. stocks over the period 1988 through 2009, they find that: Keep Reading

VIX After Big Change Days

What happens to the S&P 500 Implied Volatility Index (VIX) after days when it changes dramatically? To ensure that a trader could have identified the days selected in real time and to accommodate volatility regime changes, we define a dramatic change as an advance or decline of at least four standard deviations of the daily VIX changes over the preceding four years (1,008 trading days). Using daily closes for VIX from January 2, 1990 through August 10, 2011, we find that: Keep Reading

Stock Market Volatility by Bull-Bear Regime

“Overview of Financial Market Regime Change” states that researchers often use return volatility to discriminate financial market regimes (intervals of persistent behavior). Investors often use some variation of simple moving average (SMA) crossovers to determine market regime. Do these perspectives intersect? To investigate, we examine realized volatility (standard deviation of daily returns) and frequency of days with extreme returns during bull and bear regimes as defined by the S&P 500 Index being above or below its 200-day SMA. We define extreme days based on standard deviations from the mean daily return over the prior 1000 trading days (about four years). These definitions avoid look-ahead bias. Using daily S&P 500 Index closes (excluding dividends) for January 1950 through July 2011 (with the first four years used only to set initial thresholds for extreme days), we find that: Keep Reading

VIX-signaled Trading Strategy

Does the Chicago Board Options Exchange Market Volatility Index (VIX), a measure of investor expectations for stock market volatility (and arguably of current level of fearfulness), exploitably predict stock market direction? In their April 2007 paper entitled “Can the VIX Signal Market’s Direction? An Asymmetric Dynamic Strategy”, flagged by a reader, Alessandro Cipollini and Antonio Manzini investigate the relationship between VIX and future S&P 500 Index returns at a three-month horizon. To accommodate long-term variation in VIX (regime changes), they relate VIX to its 24-month rolling historical average. To accommodate non-linearity in the relationship between VIX and future returns, they segment the rolling history into 22 percentiles and assign the current VIX to one of 23 classes ranging from 0 (a 24-month low) to 22. They use 13 years of their sample for in-sample testing and two years for out-of-sample testing. Using daily closing levels of VIX and the S&P 500 index during January 1990 through early January 2007, they find that: Keep Reading

Index Versus ETF Option Pricing

Are there differences in implied volatilities (option pricing) between major indexes and the exchange-traded funds (ETF) that track them? In their 2011 paper entitled “The Implied Volatility of ETF and Index Options”, Stoyu Ivanov, Jeff Whitworth and Yi Zhang compare implied volatilities of SPDR Dow Jones Industrial Average (DIA), SPDR S&P 500 (SPY) and PowerShares QQQ (QQQ) to those of the Dow Jones Industrial Average (DJIA), the S&P 500 Index and the NASDAQ 100 Index, respectively. They note that ETF prices may deviate from underlying index levels because: (1) ETFs incorporate trading frictions from rebalancing and management fees; (2) ETF composition may differ slightly from that of the underlying index due to trading cost constraints; (3) ETFs accumulate dividends in a non-interest bearing account for periodic lump sum distribution; and, (4) ETFs trade until 4:15 p.m., while indexes close at 4:00 p.m. Also, index options are European, while ETF options are American. Using index levels at the close and ETF prices within one second of 4:00 p.m. during 3/10/99 through 12/29/06, and associated ETF and index near-to-expiration options price data filtered for reliability during 2003 through 2006, they find that: Keep Reading

Notes on Variability of Stock Market Returns

How should the variability of stock market returns shape the outlooks of short-term traders and long-term investors? How strong is the tailwind of the general drift upward in stock prices? How powerful is the turbulence of variability? Does the tailwind ever overpower the turbulence? Using weekly closes for the S&P 500 Index during for January 1950 through May 2011 (3,204 weeks or about 61 years), we find that: Keep Reading

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

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