Volatility Effects

Studies of leveraged exchange-traded funds (ETF), such as those summarized in “The Unintended Characteristics of Leveraged and Inverse ETFs” and “The Performance of Leveraged ETFs over Extended Holding Periods”, find that the frequent rebalancing actions necessary to maintain targeted leverage substantially affect long-term performance. A reader observed:

“I’ve read so many articles about how the leveraged ETFs are screwy, and they chew up both sides of the market due to their rebalancing, etc. So I’ve been shorting equal amounts of the long and short double ETFs. I’m short the QID and the QLD, short the TWM and UWM, short the UGL and the GLL, and short the DIG and DUG. I figure, if they are bad longs, they must be good shorts. My thinking is that in a STRONGLY trending market, the position may lose some ground, at least temporarily. But in a weakly trending market, or sideways, both will decay nicely. When I look back on the ones that are a few years old, they just melt away (one side more than the other).”

Does this reverse thinking work? To check, we examine the inception-to-date performance of paired short positions for Ultra S&P500 ProShares (SSO) / UltraShort S&P500 ProShares (SDS) and Ultra QQQ ProShares (QLD) / UltraShort QQQ ProShares (QID). Using daily adjusted closes for these 2X and -2X ETFs for the period 7/13/06 (the first date prices for all four are available) through 10/13/11 (about 63 months), we find that: Keep Reading

Can the forward-looking aspect of the S&P 500 Volatility Index (VIX) amplify technical analysis? In their September 2011 paper entitled “Using VIX Data to Enhance Technical Trading Signals”, James Kozyra and Camillo Lento apply nine simple technical trading rules (three each moving average crossovers, filters and trading range breakouts) to VIX to generate daily trading signals for the S&P 500 Index, the NASDAQ index and the Dow Jones Industrial Average. They reason that a relatively high (low) level of VIX indicates strong (weak) future stock index returns, so technical rules that separate daily levels of VIX into high and low regimes should aid trading. They compare results for VIX rule signals to those for signals generated by applying the rules to the indexes themselves. In all 27 cases (nine rules times three indexes), rule implementation assumes going long (short) an index on the day after buy (sell) signals. Estimated trading friction accounts for the bid-ask spread and a broker fee at the time of each trade. Using daily closes for VIX and the three indexes for January 1999 through July 2009, they find that: Keep Reading

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

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

“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

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

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

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

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

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