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.

Page 2 of 1812345678910...Last »

Multi-year Performance of Leveraged ETFs

An array of leveraged exchange-traded funds (ETF) track short-term (daily) changes in popular indexes. Over longer holding periods, these ETFs tend to veer off track. The cumulative veer can be large. How do leveraged ETFs actually perform over a multi-year period? What factors contribute to their failure to achieve targeted leverage versus underlying indexes? To investigate, we consider:

  • 46 ProShares 2X and -2X leveraged equity index ETFs (23 matched long-short pairs), with start date 4/23/07 (determined by the youngest of these funds), encompassing broad indexes, style indexes and sector indexes.
  • 10 ProShares 3X and -3X leveraged equity index ETFs (five matched long-short pairs), with start date 2/11/10, encompassing broad indexes only.

We measure achieved average daily leverage by comparing the average daily return of each leveraged ETF to the average daily return of a 1X ETF designed to track the same index. We measure achieved long-term leverage by comparing the terminal return of each leveraged ETF to the terminal return of a 1X ETF designed to track the same index. Using daily adjusted prices for all these funds through 10/31/13, we find that: Keep Reading

Stock Return-Implied Volatility Two-way Feedback

Is there exploitable feedback between stock returns and behaviors of associated options due to concentration of informed traders in one market or the other? In the October 2013 version of their paper entitled “The Joint Cross Section of Stocks and Options”, Byeong-Je An, Andrew Ang, Turan Baliand and Nusret Cakici investigate lead-lag relationships between stock returns and changes in associated option-implied volatilities. In case there is some asymmetry, they examine call option and put option implied volatilities separately. They focus on near-term options with delta of 0.5 and expiration in 30 days. Using daily stock returns and associated call and put option implied volatilities (available from OptionMetrics), firm fundamentals and risk adjustment factors during January 1996 through December 2011, they find that: Keep Reading

Agile Portfolio Theory?

Has Modern Portfolio Theory failed to deliver over the past decade because users employ long-term averages for expected returns, volatilities and correlations that do not respond to changing market environments? Do short-term estimates of these key inputs work better? In their May 2012 paper entitled “Adaptive Asset Allocation: A Primer”, Adam Butler, Michael Philbrick and Rodrigo Gordillo backtest a progression of strategies culminating in an Adaptive Asset Allocation (AAA) strategy that incorporates return predictability from relative momentum (last 120 trading days, about six months), volatility predictability from recent volatility (last 60 trading days) and pairwise correlation predictability from recent correlations (last 250 trading days). Their tests employ nine asset class indexes (U.S. stocks, European stocks, Japanese stocks, U.S. real estate investment trusts (REIT), International REITs, intermediate-term U.S. Treasuries, long-term U.S. Treasuries and commodities) and a spot gold price series. They reform portfolios monthly based on evolving return, volatility and correlation forecasts. They ignore trading frictions as negligible for “intelligent retail or institutional investors” via mutual funds or Exchange Traded Funds. Using daily returns for the nine indexes and spot gold) to test six strategies during January 1995 through March 2012, they find that: Keep Reading

Low-risk Bonds Are Best (in the Future)?

Do low-risk bonds, like low-risk stocks, tend to outperform their high-risk counterparts? In their September 2013 paper entitled “Low-Risk Anomalies in Global Fixed Income: Evidence from Major Broad Markets”, Raul Leote de Carvalho, Patrick Dugnolle, Xiao Lu and Pierre Moulin investigate whether low-risk beats high-risk for different measures of risk and different bond segments. They consider only measures of risk that account for the fact that the risk of a bond inherently decreases as it approaches maturity, emphasizing duration-times-yield (yield elasticity). They focus on corporate investment grade bonds denominated in dollars, euros, pounds or yen, but also consider government and high-yield corporate bonds worldwide. Each month, they rank a selected category of bonds by risk into fifths (quintile portfolios). For calculation of monthly quintile returns, they weight individual bond returns by market capitalization. They reinvest coupons the end of the month. They focus on quintile portfolio Sharpe ratios to test the risk-performance relationship. Using monthly risk data and returns for 85,442 individual bonds during January 1997 through December 2012 (192 months), they find that: Keep Reading

Volatility of Volatility as Stock Market Return Predictor

Some experts interpret stock market return volatility as an indicator of investor sentiment, with high (low) volatility indicating ascendancy of fear (greed). Volatility of volatility (VoV) would thus indicate uncertainty in investor sentiment. Does the risk associated with this uncertainty depress stock prices and thereby predict relatively high future stock market returns? To investigate, we consider two measures of U.S. stock market volatility: (1) realized volatility, calculated as the standard deviation of daily S&P 500 Index return over the last 21 trading days (annualized); and, (2) implied volatility as measured by the Chicago Board Options Exchange Market Volatility Index (VIX). For both, we calculate VoV as the standard deviation of volatility over the past 21 trading days and test the ability of VoV to predict SPDR S&P 500 (SPY) returns. To avoid overlap in volatility and VoV calculations, we focus on monthly return intervals. Using daily values of the S&P 500 Index since December 1989 and VIX since inception in January 1990, and monthly dividend-adjusted SPY closes since inception in January 1993, all through July 2013, we find that: Keep Reading

Sorting Out the Idiosyncratic Volatility Anomaly

Does exceptional (idiosyncratic) stock volatility exploitably predict future returns? In her April 2013 paper entitled “Revisiting Idiosyncratic Volatility and Stock Returns”, Fatma Sonmez re-examines the relationship between idiosyncratic volatility and future stock returns. She defines idiosyncratic volatility as the standard deviation of daily residuals from monthly regressions of returns (in excess of the risk-free rate) for each stock versus Fama-French model factors. Using daily returns and contemporaneous market, size and book-to-market factors for U.S. listed stocks during 1963 through 2008, she finds that: Keep Reading

Volatility Risk Premium an Exploitable Stock Market Predictor?

Prior research (see “The Implied-Realized Volatility Gap as Return Predictor” and “Variance Risk Premium Predictive Power Worldwide”) indicates that the stock market volatility (or variance) risk premium, measured as the difference between the volatility implied by prices of stock index options and some measure of recent historical index volatility, significantly predicts future stock market returns. Does this finding support a trading strategy? To investigate, we consider a simple volatility risk premium (VRP) measured as S&P 500 Index option-implied volatility (VIX) minus S&P 500 Index historical volatility (standard deviation of daily returns over the past 21 trading days). Since VIX is an annualized percentage, we annualize historical daily volatility by multiplying by the square root of 252. We then relate this simple VRP to future S&P 500 Index returns and apply a VRP-related signal to time SPDR S&P 500 (SPY). Using daily levels of the S&P 500 Index and VIX since January 1990, and daily values of SPY and the 13-week U.S. Treasury bill (T-bill) yield as return on cash since the end of January 1993, all through mid-July 2013, we find that: Keep Reading

Leveraged ETF Pair Shorting Strategies

“Shorting Leveraged ETF Pairs” looks at shorting leveraged long/short pairs of exchange-traded funds (ETF) and letting the short positions “melt away” over long holding periods. Findings suggest that the approach may be profitable, with most of the gain coming when market volatility is high. What about more active strategies of continually renewed short positions? To investigate, we consider monthly renewal of short positions in the ProShares Ultra S&P500 (SSO) / ProShares UltraShort S&P500 (SDS) 2X/-2X pair and the ProShares UltraPro S&P500 (UPRO)ProShares UltraPro Short S&P500 (SPXU) 3X/-3X pair. Using monthly adjusted closes for these ETFs and for the S&P 500 Volatility Index (VIX) from respective inceptions through June 2013, we find that: Keep Reading

VIX Calendar Effects

Does the S&P 500 implied volatility index (VIX) exhibit systematic behaviors by day of the week, month of the year, turn-of-the-month (TOTM) or options expiration (OE)? If so, are the behaviors exploitable? Using daily closing levels of VIX since January 1990, daily opening levels of VIX since September 2003 and daily opening and closing levels of iPath S&P 500 VIX Short-Term Futures ETN (VXX) since February 2009 (excluding effects of price resets on 11/9/10 and 10/5/12), all through mid-July 2013, we find that: Keep Reading

Which Kind of Equity Risk Gets Compensated?

Does the market pay a premium to equity funds with relatively high “bad” (left tail) volatility? In their May 2013 paper entitled “Volatility vs. Tail Risk: Which One is Compensated in Equity Funds?”, James Xiong, Thomas Idzorek and Roger Ibbotson compare return premiums for conventional volatility (standard deviation of total returns) and tail risk (value-at-risk) across U.S. and non-U.S. equity mutual funds. Each month, they use the previous five years of monthly net total returns to sort funds into fifths (quintiles) based on volatility and on excess (relative to a normal distribution) value-at-risk for the worst 5% of returns. They estimate premiums for these two risk measures as the difference in average (arithmetic mean) returns between the riskiest and least risky quintiles in excess of the Treasury bill (T-bill) yield. Using monthly returns for the oldest share class for a broad sample of alive and dead open-end equity mutual funds (3,389 U.S. and 1,055 non-U.S.), and the contemporaneous T-bill yield, during January 1980 through September 2011, they find that: Keep Reading

Page 2 of 1812345678910...Last »
Current Momentum Winners

ETF Momentum Signal
for April 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
217% 197%
Top 3 ETFs SPY
197% 68%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts