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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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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

Exploiting the Volatility Risk Premium with ETNs

“Identifying VXX/XIV Tendencies” finds that the Volatility Risk Premium (VRP), estimated as the difference between the current level of the S&P 500 implied volatility index (VIX) and the annualized standard deviation of S&P 500 Index daily returns over the previous 21 trading days (multiplying by the square root of 250 to annualize), may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare performance data for:

  1. XIV B&H – buying and holding XIV.
  2. XIV-Cash – holding XIV (cash) when prior-day roll when VRP is relatively high (low).
  3. XIV-VXX – holding XIV (VXX) when prior-day VRP is relatively high (low).

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics. Using daily closes for XIV, VXX, VIX and the S&P 500 Index from XIV inception (end of November 2010) through mid-November 2017, we find that: Keep Reading

Exploiting VIX Futures Roll Return with ETNs

“Identifying VXX/XIV Tendencies” finds that S&P 500 implied volatility index (VIX) futures roll return, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare performances of:

  1. XIV B&H – buying and holding XIV.
  2. XIV-Cash – holding XIV (cash) when prior-day roll return is non-positive (positive).
  3. XIV-VXX – holding XIV (VXX) when prior-day roll return is non-positive (positive).

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics. Using daily closing prices for XIV and VXX and daily settlement prices for VIX futures from XIV inception (end of November 2010) through mid-November 2017, we find that:

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Identifying VXX/XIV Tendencies

A subscriber inquired about strategies for trading exchange-traded notes (ETN) constructed from near-term S&P 500 Volatility Index (VIX) futures: iPath S&P 500 VIX Short-Term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-Term (XIV), available since 1/30/09 and 11/30/10, respectively. The managers of these securities buy and sell VIX futures daily to maintain a constant maturity of one month (long for VXX and short for XIV), continually rolling partial positions from the nearest term contract to the next nearest. We consider five potential predictors of the price behavior of these ETNs:

  1. Level of VIX, in case a high (low) level indicates a future decrease (increase) in VIX that might affect VXX and XIV.
  2. Change in VIX (VIX “return”), in case there is some predictable reversion or momentum for VIX that might affect VXX and XIV.
  3. Implied volatility of VIX (VVIX), in case uncertainty in the expected level of VIX might affect VXX and XIV.
  4. Term structure of VIX futures (roll return) underlying VXX and XIV, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, indicating a price headwind or tailwind for a fund manager continually rolling from one to the other. VIX roll return is usually negative (contango), but occasionally positive (backwardation).
  5. Volatility Risk Premium (VRP), estimated as the difference between VIX and the annualized standard deviation of daily S&P 500 Index returns over the past 21 trading days (multiplying by the square root of 250 to annualize), in case this difference between expectations and recent experience indicates the direction of future change in VIX.

We measure predictive power of each in two ways:

  • Correlations between daily VXX and XIV returns over the next 21 trading days to daily values of each indicator.
  • Average next-day XIV returns by ranked tenth (decile) of daily values of each indicator.

Using daily levels of VIX and VVIX, settlement prices for VIX futures contracts, levels of the S&P 500 Index and split-adjusted prices for VXX and XIV from inceptions of the ETNs through mid-November 2017, we find 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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Comparing Ivy 5 Allocation Strategy Variations

A subscriber requested comparison of four variations of an “Ivy 5” asset class allocation strategy, as follows:

  1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
  2. Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
  3. Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
  4. Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond (IEF)
SPDR S&P 500 (SPY)
Vanguard REIT ETF (VNQ)
iShares MSCI EAFE Index (EFA)
PowerShares DB Commodity Index Tracking (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and the yield on 13-week U.S. Treasury bills (T-bills) as a proxy for return on cash during February 2006 through October 2017 (141 months), we find that: Keep Reading

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