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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.

Simple Volatility-Payout-Momentum Stock Strategy

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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Monthly Rebalanced Shorting of Leveraged ETF Pairs

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

Managing Volatility to Suppress U.S. Stock Market Tail Risk

Do strategies that seek to exploit return volatility persistence by adjusting stock market exposure inversely with recent market volatility relative to some target (including exposures greater than 100%) produce obvious benefits for investors? In their November 2017 paper entitled “Tail Risk Mitigation with Managed Volatility Strategies”, Anna Dreyer and Stefan Hubrich examine usefulness of managing volatility in this way as applied to the S&P 500 Index over a long sample period and across a range of performance measurements. They use daily index returns in excess of the return on cash and rebalance stock index-cash test portfolios daily. Their target volatility is variable, set as the inception-to-date realized daily excess return volatility. They assess robustness across different sample subperiods, past volatility measurement intervals and portfolio holding intervals. They measure portfolio performance conventionally (Sharpe ratio), via effects on portfolio return distribution skewness and kurtosis (as an indicator of tail risk) and with investor utility metrics. Using daily excess returns for the S&P 500 Index during July 1926 through November 2016, they find that:

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Categorization of Risk Premiums

What is the best way to think about reliabilities and risks of various anomaly premiums commonly that investors believe to be available for exploitation? In their December 2017 paper entitled “A Framework for Risk Premia Investing”, Kari Vatanen and Antti Suhonen present a framework for categorizing widely accepted anomaly premiums to facilitate construction of balanced investment strategies. They first categorize each premium as fundamental, behavioral or structural based on its robustness as indicated by clarity, economic rationale and capacity. They then designate each premium in each category as either defensive or offensive depending on whether it is feasible as long-only or requires short-selling and leverage, and on its return skewness and tail risk. Based on expected robustness and riskiness of selected premiums as described in the body of research, they conclude that: Keep Reading

Volatility Scaling for Momentum Strategies?

What is the best way to implement futures momentum and manage its risk? In their November 2017 paper entitled “Risk Adjusted Momentum Strategies: A Comparison between Constant and Dynamic Volatility Scaling Approaches”, Minyou Fan, Youwei Li and Jiadong Liu compare performances of five futures momentum strategies and two benchmarks:

  1. Cross-sectional, or relative, momentum (XSMOM) – each month long (short) the equally weighted tenth of futures contract series with the highest (lowest) returns over the past six months.
  2. XSMOM with constant volatility scaling (CVS) – each month scales the XSMOM portfolio by the ratio of a 12% target volatility to annualized realized standard deviation of daily XSMOM portfolio returns over the past six months.
  3. XSMOM with dynamic volatility scaling (DVS) – each month scales the XSMOM portfolio by the the ratio of next-month expected market return (a function of realized portfolio volatility and whether MSCI return over the last 24 months is positive or negative) to realized variance of XSMOM portfolio daily returns over the past six months.
  4. Time-series, or intrinsic, momentum (TSMOM) – each month long (short) the equally weighted futures contract series with positive (negative) returns over the past six months.
  5. TSMOM with time-varying volatility scaling (TSMOM Scaled) – each month scales the TSMOM portfolio by the ratio of 22.6% (the volatility of an equally weighted portfolio of all future series) to annualized exponentially weighted variance of TSMOM returns over the past six months.
  6. Equally weighted, monthly rebalanced portfolio of all futures contract series (Buy-and-Hold).
  7. Buy-and-Hold with time-varying volatility scaling (Buy-and-Hold Scaled) – each month scales the Buy-and-Hold portfolio as for TSMOM Scaled.

They test these strategies on a multi-class universe of 55 global liquid futures contract series, starting when at least 45 series are available in November 1991. They focus on average annualized gross return, annualized volatility, annualized gross Sharpe ratio, cumulative return and maximum (peak-to-trough) drawdown (MaxDD) as comparison metrics. Using monthly prices for the 55 futures contract series (24 commodities, 13 government bonds, 9 currencies and 9 equity indexes) during June 1986 through May 2017, they find that:

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Smartest Beta?

What is the smartest way (having the lowest prediction errors) to estimate market beta across stocks for the purpose of portfolio construction? In their November 2017 paper entitled “How to Estimate Beta?”, Fabian Hollstein, Marcel Prokopczuk and Chardin Simen test effects of different return sampling frequencies, forecast adjustments and model combinations on market beta prediction accuracy across the universe of U.S. stocks. Their primary goal is to identify optimal choices. They focus on a beta prediction horizon of six months. They consider past beta estimation (lookback) windows of 1, 3, 6, 12, 24, 36 and 60 months for daily data, 12, 36 and 60 months for monthly data and 120 months for quarterly data. They measure beta prediction accuracy based on average root mean squared error (RMSE) across stocks. Using returns for a broad sample of U.S. stocks during January 1963 through December 2015, they find that: Keep Reading

Correlated Unwind of Short Volatility?

Is volatility dangerously oversold? In their November 2017 paper entitled “Everybody’s Doing it: Short Volatility Strategies and Shadow Financial Insurers”, Vineer Bhansali and Lawrence Harris survey strategies that directly or indirectly short volatility, including:

  • Relevant strategies (selling options, buying and selling products linked to volatility indexes, risk parity, risk premium harvesting and volatility targeting).
  • Types of investors that use them.
  • Commonalities among them.
  • Implications of commonalities (correlated unwinding).

Based on the properties of these strategies, they conclude that: Keep Reading

Shorting Equity Options to Automate Portfolio Rebalancing

Can investors refine portfolio rebalancing while capturing a volatility risk premium (VRP) by systematically shorting options matched to target allocations of the underlying asset? In their October 2017 paper entitled “An Alternative Option to Portfolio Rebalancing”, Roni Israelov and Harsha Tummala explore multi-asset class portfolio rebalancing via an option selling overlay. The overlay sells out-of-the-money options such that, if stocks rise (fall), counterparties exercise call (put) options and the portfolio must sell (buy) shares. They intend their approach to counter short-term momentum exposure between rebalancings (when the portfolio is overweight winners and underweight losers) with short-term reversal exposure inherent in short options. For testing, they assume: (1) a simple 60%-40% stocks-bonds portfolio; (2) bond returns are small compared to stock returns (so only the stock allocation requires rebalancing); and, (3) option settlement via share transfer, as for SPDR S&P 500 (SPY) as the stock/option positions. They each month sell nearest out-of-the-money S&P 500 Index  call and put options across multiple economically priced strikes and update the overlay intramonth if new economically priced strikes become available. Once sold, they hold the options to expiration. Using daily S&P 500 Total Return Index returns, Barclays US Aggregate Bond Index returns and closing bid/ask quotes for S&P 500 Index options equity options (with returns calculated in excess of the risk-free rate) during 1996 through 2015, they find that:

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Average Call-Put Implied Volatility Spread and Future Stock Market Return

Does relative demand for call and put options on individual stocks, as measured by average difference in implied volatilities of at-the-money calls and puts (aggregate implied volatility spread), predict stock market returns? In their September 2017 paper entitled “Aggregate Implied Volatility Spread and Stock Market Returns”, Bing Han and Gang Li test aggregate implied volatility spread as a U.S. stock market return predictor. They focus on monthly measurements, but test the daily series in robustness test. They calculate monthly implied volatility spread for each stock with at least 12 daily at-the-money call and put option prices during the month as an average over the last five trading days. They then eliminate outliers by excluding the top and bottom 0.1% of all stock implied volatility spreads before averaging across stocks to calculate aggregate implied volatility spread. They compare the predictive power of aggregate implied volatility spread to those of 22 other predictors from prior research. Using daily at-the-money call and put implied volatilities for U.S. stocks, data for other U.S. stock market predictors and U.S. stock market returns during January 1996 through December 2015, they find that:

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How Best to Diversify Smart Betas

Is it better to build equity multifactor portfolios by holding distinct single-factor sub-portfolios, or by picking only stocks that satisfy multiple factor criteria? In their September 2017 paper entitled “Smart Beta Multi-Factor Construction Methodology: Mixing vs. Integrating”, Tzee-man Chow, Feifei Li and Yoseop Shim compare long-only multifactor portfolios constructed in two ways:

  1. Integrated – each quarter, pick the 20% of stocks with the highest average standardized factor scores and weight by market capitalization.
  2. Mixed – each quarter, hold an equal-weighted combination of single-factor portfolios, each comprised of the capitalization-weighted 20% of stocks with the highest expected returns for that factor. 

They consider five factors: value (book-to-market ratio), momentum (return from 12 months ago to one month ago), operating profitability, investment (asset growth) and low-beta. They reform factor portfolios annually for all except momentum and low-beta, which they reform quarterly. Using firm data required for factor calculations and associated stock returns for a broad sample of U.S. stocks during June 1968 through December 2016, they find that: Keep Reading

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