Volatility Effects

Is there a distinct systematic asset risk, as measured by its market beta, associated with each return measurement interval (frequency, such as daily, monthly or annually)? In other words, is return measurement frequency a risk factor? In their October 2018 paper entitled “Measuring Horizon-Specific Systematic Risk via Spectral Betas”, Federico Bandi, Shomesh Chaudhuri, Andrew Lo and Andrea Tamoni  introduce spectral beta, an asset’s market beta for a given return measurement frequency, as a way to assess this frequency as a source of systematic investment risk. They specify how to combine spectral betas into an overall beta and explore ways to interpret and exploit spectral betas. Using mathematical derivations and samples of monthly and daily returns for broad samples of U.S. stocks and stock portfolios, they find that: Keep Reading

What is the best way to construct equity multifactor portfolios? In the November 2018 revision of their paper entitled “Equity Multi-Factor Approaches: Sum of Factors vs. Multi-Factor Ranking”, Farouk Jivraj, David Haefliger, Zein Khan and Benedict Redmond compare two approaches for forming long-only equity multifactor portfolios. They first specify ranking rules for four equity factors: value, momentum, low volatility and quality. They then, each month:

• Sum of factor portfolios (SoF): For each factor, rank all stocks and form a factor portfolio of the equally weighted top 50 stocks (adjusted to prevent more than 20% exposure to any sector). Then form a multifactor portfolio by equally weighting the four factor portfolios.
• Multifactor ranking (MFR): Rank all stocks by each factor, average the ranks for each stock and form an equally weighted portfolio of those stocks with the highest average ranks, equal in number of stocks to the SoF portfolio (again adjusted to prevent more than 20% exposure to any sector).

They consider variations in number of stocks selected for individual factor portfolios from 25 to 200, with comparable adjustments to the MFR portfolio. They assume trading frictions of 0.05% of turnover. Using monthly data required to rank the specified factors for a broad sample of U.S. common stocks and monthly returns for those stocks and the S&P 500 Total Return Index (S&P 500 TR) during January 2003 through July 2016, they find that: Keep Reading

Are there long-term positions in leveraged index exchange-traded funds (ETF) that beat buying and holding the underlying index? In his October 2018 paper entitled “Leveraged ETF Pairs: An Empirical Evaluation of Portfolio Performance”, Stanley Peterburgsky examines the performance of simple strategies involving leveraged and inverse leveraged ETFs. Specifically, he tests whether the following leveraged ETF portfolios are likely to outperform underlying total return indexes:

1. A long position in SSO or UPRO, compared to the S&P 500 Index.
2. 1/3 short UPRO (URTY) and 2/3 short SPXU (SRTY), compared to the S&P 500 (Russell 2000) Index.
3. 1/4 short SSO (UWM) and 3/4 short SDS (TWM), compared to the S&P 500 (Russell 2000) Index.
4. Short SH (RWM), compared to the S&P 500 (Russell 2000) Index.

All short positions have matching long positions in 1-month U.S. Treasury bills that drive some trading. For example, at the end of each trading day, if the UPRO/SRTY portfolio value is less than 90% (more than 110%) of the short balance, the strategy buys (shorts additional) shares of UPRO and SPXU in equal proportions to restore long-short balance. In addition, strategies 2 and 3 require occasional rebalancing of ETF pairs. Baseline strategies allows pair members to drift up to 20% apart before rebalancing. Sensitivity tests evaluate effects of tightening the rebalancing threshold to 10%. Key performance metrics are average annualized return, average annualized standard deviation of daily returns and average annualized Sharpe ratio. Using daily total returns for the specified leveraged ETFs and underlying indexes during 2010 (2/9/2010 for Russell 2000-based funds) through 2016, he finds that:

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Quantitative investing involves disciplined rule-based approaches to help investors structure optimal portfolios that balance return and risk. How has such investing evolved? In their June 2018 paper entitled “The Current State of Quantitative Equity Investing”, Ying Becker and Marc Reinganum summarize key developments in the history of quantitative equity investing. Based on the body of research, they conclude that: Keep Reading

Does focusing on downside risk (volatility or beta) consistently produce more accurate forecasts of asset returns? In their July 2018 paper entitled “Tail Risk in the Cross Section of Alternative Risk Premium Strategies”, Bernd Scherer and Nick Baltas investigate how well downside risk explains cross-sectional returns of 260 risk factor strategies spanning asset classes and investment styles from six global investment banks. Their main model is a two-pass regression that distinguishes between conventional market beta and market downside beta. For corroboration, they consider four other indicators of downside risk (return skewness, correlation of tail returns with equity market returns, TED spread and economic policy uncertainty as measured by relative VIX level). Using weekly data risk factor returns and downside risk indicators during February 2008 through January 2018, they find that: Keep Reading

A betting against beta (BAB) portfolio is long low-beta assets and short high-beta assets, with each side leveraged to a beta of one. Do strong past stock market returns (when investors tend to overweight high-beta stocks) predict an increase in BAB returns? In his June 2018 paper entitled “Time-Varying Leverage Demand and Predictability of Betting-Against-Beta”, Esben Hedegaard tests the prediction that BAB performs better in times and in countries after high past stock market returns in three ways: (1) regression of BAB returns versus past market returns; (2) sorts of BAB returns into fifths (quintiles) based on past market returns; and, (3) a timing strategy that is long BAB half the time and short BAB half the time based on detrended inception-to-date past market returns, scaled to 10% annualized volatility for comparability. Using daily and monthly data, including monthly BAB returns, for U.S. common stocks and the U.S. stock market since 1931 and for 23 other countries from as early as 1988, all through January 2018, he finds that: Keep Reading

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

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Does implied stock market volatility (IV) predict stock market returns? In their March 2018 paper entitled “Implied Volatility Measures As Indicators of Future Market Returns”, Roberto Bandelli and Wenye Wang analyze the relationship between S&P 500 Index IV and future S&P 500 Index returns. They consider volatilities implied either by S&P 500 Index options (VIX) or by 30-day at-the-money S&P 500 Index straddles. Specifically, they each day:

1. Rank current S&P 500 Index IV according to ranked tenth (decile) of its daily distribution over the past two years. If current IV is higher than any value of IV over the past two years, its rank is 11.
2. Calculate S&P 500 Index returns over the next one, five and 20 trading days.
3. Relate these returns to IV rank.

They calculate statistical significance based on the difference between the average IV-ranked log returns and log returns over all intervals of the same length. Using daily data for the selected variables during December 1991 through November 2017, they find that: Keep Reading

Is there an easy way for investors to capture jointly the most reliable stock return factor premiums? In their March 2018 paper entitled “The Conservative Formula: Quantitative Investing Made Easy”, Pim van Vliet and David Blitz propose a stock selection strategy based on low return volatility, high net payout yield and strong price momentum. Specifically, at the end of each quarter they:

1. Segment the then-current 1,000 largest stocks into 500 with the lowest and 500 with the highest 36-month return volatilities.
2. Within each segment, rank stocks based on total net payout yield (NPY), calculated as dividend yield minus change in shares outstanding divided by its 24-month moving average.
3. Within each segment, rank stocks based on return from 12 months ago to one month ago (with the skip-month intended to avoid return reversals).
4. Within the low-volatility segment, average the momentum and NPY ranks for each stock and equally weight the top 100 to reform the Conservative Formula portfolio.
5. Within the high-volatility segment, average the momentum and NPY ranks for each stock and equally weight the bottom 100 to reform the Speculative Formula portfolio.

Limiting the stock universe to the top 1,000 based on market capitalization suppresses liquidity risk. Limiting screening parameters to three intensely studied factors that require no accounting data mitigates data snooping and data availability risks. They focus on the 1,000 largest U.S. stocks to test a long sample, but also consider the next 1,000 U.S. stocks (mid-caps) and the 1,000 largest stocks from each of Europe, Japan and emerging markets. They further examine: (1) sensitivity to economic conditions doe the long U.S. sample; and, (2) impact of trading frictions in the range 0.1%-0.3% for developed markets and 0.2%-0.6% for emerging markets. Using quarterly prices, dividends and shares outstanding for the contemporaneously largest 1,000 U.S. stocks since 1926, European and Japanese stocks since 1986 and emerging markets stocks since 1991, all through 2016, they find that:

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Is shorting pairs of leveraged exchange-traded funds (ETF) reliably profitable? In their December 2017 paper entitled “Shorting Leveraged ETF Pairs”, Christopher Hessel, Jouahn Nam, Jun Wang, Xing Cunyu and Ge Zhang examine monthly returns from shorting a pair of leveraged and inverse leveraged ETFs for the same index. They first investigate what circumstances make this strategy profitable. They then test the strategy on each of the triple/inverse triple (3X/-3X) pairs associated with the following six base ETFs: Financial Select Sector SPDR (XLF: FAS/FAZ), Powershares QQQ (QQQ: TQQQ/SQQQ), iShares Russell 2000 Index (IWM: TNA/TZA), SPDR S&P 500 (SPY: UPRO/SPXU), VanEck Vectors Junior Gold Miners ETF (GDXJ: JNUG/JDST) and Energy Select Sector SPDR (XLE: ERX/ERY). Their analysis assumes rebalancing pair short positions to equal value at the end of each month and holding them to the end of the next month. Using monthly data for the selected leveraged ETFs from the end of 2007 (except the end of November 2009 for the leveraged versions of GDXJ) through the end of December 2016, they find that: Keep Reading