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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.

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Separate vs. Integrated Equity Factor Portfolios

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

Leveraged ETF Pairs Performance

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:

Keep Reading

U.S. Stock Market Returns after Extreme Up and Down Days

What happens after extreme up days or extreme down days for the U.S. stock market? To investigate, we define extreme up or down days as those with daily returns at least X standard deviations above or below the mean (average) return over the past four years (the U.S. political cycle, about 1,000 trading days). This methodology allows identification of extreme days starting in January 1954. Focusing on three standard deviations, we then look at average returns and return variabilities over the next 63 trading days (three months). Using daily closes for the S&P 500 Index during January 1950 through late October 2018, we find that:

Keep Reading

Are Equity Multifactor ETFs Working?

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available (in order of decreasing assets):

  • Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility.
  • iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks.
  • John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors.

Because available sample periods are very short, we focus on daily return statistics, along with cumulative returns. We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex US (ACWX), SPDR S&P MidCap 400 (MDY) and iShares Russell 1000 (IWB). Using daily returns for the seven equity multifactor ETFs and benchmarks as available through September 2018, we find that: Keep Reading

Evolution of Quantitative Stock Investing

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

Downside Risk Premiums

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

Are Low Volatility Stock ETFs Working?

Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through June 2018, we find that: Keep Reading

Betting Against Beta, Plus Market Momentum

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

Benefits of Volatility Targeting Across Asset Classes

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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Revisiting VIX as Stock Return Predictor

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

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