Volatility Effects

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

Why does the widely cited and intuitive Capital Asset Pricing Model (CAPM) prediction that extra risk (beta) earns extra reward (rate of return) not work for stocks? In their May 2013 paper entitled “Explanations for the Volatility Effect: An Overview Based on the CAPM Assumptions”, David Blitz, Eric Falkenstein and Pim van Vliet organize research on potential explanations according to the following CAPM assumptions:

1. Investors are unconstrained regarding leverage, short selling and solvency (regulatory capital requirements).
2. Investors are risk-averse, focus on absolute return and care only about return mean and variance (such that returns are normally distributed).
3. There is only one return measurement interval and therefore no compounding effect (ignoring the difference between arithmetic and geometric means).
4. Investors have complete information and process it rationally.
5. Investors have no liquidity constraints, transaction costs or taxes.

Based on a review of research on potential explanation for the empirical failure of CAPM, they find that: Keep Reading

Can commodities still be useful for portfolio diversification, despite their recent poor aggregate return, high volatility and elevated return correlations with other asset classes? In the May 2013 version of their paper entitled “Strategic Allocation to Commodity Factor Premiums”, David Blitz and Wilma de Groot examine the performance and diversification power of the commodity market portfolio and of alternative commodity momentum, carry and low-risk (low-volatility) portfolios. They define the commodity market portfolio as the S&P GSCI (production-weighted aggregation of six energy, seven metal and 11 agricultural commodities). The commodity long-only (long-short) momentum portfolio is each month long the equally weighted 30% of commodities with the highest returns over the past 12 months (and short the 30% of commodities with the lowest returns). The commodity long-only (long-short) carry portfolio is each month long the equally weighted 30% of commodities with the highest annualized ratios of nearest to next-nearest futures contract price (and short the 30% of commodities with the lowest ratios). The commodity long-only (long-short) low-risk portfolio is each month long the equally weighted 30% of commodities with the lowest daily volatilities over the past three years (and short the 30% of commodities with the highest volatilities). They also consider a combination that equally weights the commodity momentum, carry and low-risk portfolios. For comparison to U.S. stocks, they use returns of long-only, equally weighted “big-momentum” and “big-value” (comparable to commodity carry) stock portfolios from Kenneth French, and a similarly constructed “big-low-risk” stock portfolio. For comparison with bonds, they use the total return of the JP Morgan U.S. government bond index. For all return series and allocation strategies, they ignore trading frictions. Using daily and monthly futures index levels and contract prices for the 24 commodities in the S&P GSCI as available during January 1979 through June 2012, along with contemporaneous returns for a broad sample of U.S. stocks, they find that: Keep Reading

Market volatility tends to rise as returns fall. Does adding a proxy for intermediate-term U.S. equity market volatility to a diversified portfolio improve its performance? To check, we add iPath S&P 500 VIX Mid-Term Futures (VXZ) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for VXZ and the average pairwise correlation of VXZ monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without VXZ. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for VXZ and the above nine asset class proxies from March 2009 (first return available for VXZ) through April 2013 (only 50 monthly returns), we find that: Keep Reading

Market volatility tends to rise as returns fall. Does adding a proxy for short-term U.S. equity market volatility to a diversified portfolio improve its performance? To check, we add iPath S&P 500 VIX Short Term Futures (VXX) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for VXX and the average pairwise correlation of VXX monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without VXX. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for VXX and the above nine asset class proxies from February 2009 (first return available for VXX) through April 2013 (only 51 monthly returns), we find that: Keep Reading

Should investors regard any of the exchange-traded products (ETP) based on S&P 500 Index option-implied volatility (VIX) futures as long-term holdings? In the May 2013 draft of his paper entitled “Trading Volatility: At What Cost?”, Robert Whaley describes these ETPs and evaluates them as buy-and-hold investments. VIX ETPs are based on VIX futures indexes with daily rebalancing, subject to management fees and expenses including commissions and trading fees, licensing fees and (for some ETPs) foregone interest income. Many of the ETPs are exchange-traded notes (ETN), secured not by underlying assets but rather only by the good faith and collateral of the issuer. Using daily price and trading data for VIX futures (starting March 2004) and options (starting February 2006) and for 30 ETPs based on VIX futures (starting January 2009) through March 2012, he finds that: Keep Reading

How can investors use exchange-traded products to exploit equity market volatility? In the April 2013 version of his paper entitled “Easy Volatility Investing” (the National Association of Active Investment Managers’ 2013 Wagner Award runner-up), Tony Cooper explores the rewards and risks of five volatility trading strategies including simple buy-and-hold, price momentum, futures roll yield capture, volatility risk premium capture and dynamic hedging. He focuses on four exchange-traded notes (ETN) as trading vehicles:

•  iPath S&P 500 VIX Short-Term Futures ETN (VXX) – inception January 30, 2009.
• VelocityShares Daily Inverse VIX Short-Term ETN (XIV) – inception November 30, 2010.
• iPath S&P 500 VIX Medium-Term Futures ETN (VXZ) – inception February 20, 2009.
• VelocityShares Daily Inverse VIX Medium-Term ETN (ZIV) – inception November 30, 2010.

He extends the histories for these ETNs back to 2004 by simulating their prices using historical VIX futures data. For signaling, he considers two indexes:

• S&P 500 1-Month Implied Volatility Index (VIX)
• S&P 500 3-Month Implied Volatility Index (VXV)

He ignores trading frictions triggered by strategy trades and portfolio rebalancing, and ignores return on cash when not invested. Using levels of VIX and VXV, VIX futures prices and ETN prices as available during 2004 through mid-February 2013, he finds that: Keep Reading

Analyses in “Shorting VXX with Crash Protection” suggest that one-month momentum may be a useful signal for trading in and out of a short position in iPath S&P 500 VIX Short-Term Futures ETN (VXX). A subscriber inquired whether a short-term version of this signal is effective. Specifically, how useful is a strategy that goes short VXX (to cash) at the close when the same-day VXX return is negative (positive)? To test this daily momentum signal, we consider basic daily return statistics and two VXX shorting scenarios: (1) shorting an initial amount of VXX and letting this position ride indefinitely (Let It Ride); and, (2) shorting a fixed amount of VXX and resetting this fixed position daily (Fixed Reset). For tractability, we ignore shorting costs/fees, but we do consider the trading frictions associated with entering and exiting a short position in VXX based on the daily momentum signal. Using daily reverse split-adjusted closing prices for VXX from the end of January 2009 through mid-April 2013, we find that: Keep Reading

Is there a good short-cut for constructing a low-beta portfolio? In their March 2013 paper entitled “Country and Sector Drive Low-Volatility Investing in Global Equity Markets”, Sanne de Boer, Janet Campagna and James Norman investigate the role of country and sector effects in low-volatility investing across global stock markets. They construct country-sector capitalization-weighted sub-indexes (for example, U.S. Utilities) from stocks in the MSCI World Index. They then compare performances of a portfolio of these sub-indexes and a portfolio of individual stocks, both reformed every six months (ends of May and November) to minimize future volatility as predicted by a proprietary model (Axioma’s Global Risk Model). All portfolios are long-only with no leverage. Using monthly returns and market (free-float) capitalizations of MSCI World Index stocks during 1999 through 2012, they find that: Keep Reading

Do industries exhibit the market beta, value and momentum anomalies overall and in recent data? In his August 2012 paper entitled “The Failure of the Capital Asset Pricing Model (CAPM): An Update and Discussion”, Graham Bornholt examines the beta, value and momentum anomalies using returns for 48 U.S. industries. Each month, he forms three groups of eight equally weighted portfolios of industries ranked separately by: (1) beta based on rolling regressions of industry returns versus value-weighted market returns over the past 60 months; (2) value based on the latest available industry book-to-market ratios (value-weighted composites of component firm book-to-market ratios, updated annually); and, momentum based on lagged six-month industry returns. There are therefore six industries in each portfolio. Using monthly industry returns from Kenneth French’s website, monthly returns for the value-weighted U.S. stock market in excess of the one-month U.S. Treasury bill yield, and industry component book-to-market ratios during July 1963 through December 2009 he finds that: Keep Reading