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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.

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

The BGSV Portfolio

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

  • Diversifying (based on pairwise correlations) by combining: (1) Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins; (2) VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold; and, (3) ProShares Short VIX Short-Term Futures (SVXY), to capture the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures.
  • Capturing upside volatility and managing portfolio drawdown via monthly rebalancing and gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets and $0 to cash. If the risky assets have a month-end combined value less than the combined initial allocations, we rebalance them to equal weights for next month. If the risky assets have a combined month-end value greater than the combined initial allocations, we rebalance to the initial allocations and move the excess permanently (skim) to cash. We assume monthly portfolio reformation frictions of 2% of month-end combined values of risky assets. We assume accrued, skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly adjusted values of GBTC, GDXJ and SVXY and contemporaneous T-bill yield during May 2015 (limited by GBTC) through mid-February 2018, we find that:

Keep Reading

Using SVXY to Capture the Volatility Risk Premium

In response to “Shorting VXX with Crash Protection”, which investigates shorting iPath S&P 500 VIX Short-Term Futures (VXX) with crash protection to capture the equity volatility risk premium safely, a subscriber asked about instead using a long position in ProShares Short VIX Short-Term Futures (SVXY). To investigate, we consider three scenarios based on monthly measurements:

  1. Buy and Hold – buying an initial amount of SVXY and letting this position ride indefinitely.
  2. Monthly Skim – buying the same initial amount of SVXY and transferring to cash any month-end gains exceeding the initial amount (so the beginning-of-month SVXY position may become smaller, but not larger, than the initial investment).
  3. Prior Month Positive – starting with the same initial amount each month, holding SVXY (cash) when the prior-month SVXY return is positive (negative).

We assume trading frictions of 0.25% for movements of funds between SVXY and cash in scenarios 1 and 2. We assume return on cash is the 3-month U.S. Treasury bill (T-bill) yield. Using monthly split-adjusted closing prices for SVXY and contemporaneous T-bill yield during October 2011 through early February 2018 (only 78 months, with February 2018 partial), we find that: Keep Reading

Shorting VXX with Crash Protection

Does shorting the iPath S&P 500 VIX Short-Term Futures ETN (VXX) with crash protection (to capture the equity volatility risk premium safely) work? To investigate, we apply crash protection rules to three VXX shorting scenarios:

  1. Let It Ride – shorting an initial amount of VXX and letting this position ride indefinitely.
  2. Fixed Reset – shorting a fixed amount of VXX and continually resetting this fixed position (so the short position does not become very small or very large).
  3. Gain/Loss Adjusted – shorting an initial amount of VXX and adjusting the size of the short position according to periodic gains/losses.

We consider two simple monthly crash protection rules based on the assumption that volatility changes are somewhat persistent, as follows:

  • Prior Month Positive Rule – short VXX (go to cash) when the prior-month short VXX return is positive (negative).
  • Prior Week Positive Rule – short VXX (go to cash) when the prior-week short VXX return is positive (negative).

For tractability, we ignore trading frictions, costs of shorting and return on retained cash from shorting gains. Using monthly closes for the S&P 500 Volatility Index (VIX) and monthly and weekly reverse split-adjusted closing prices for VXX from February 2009 through early February 2018 (110 months), we 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:

Keep Reading

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:

Keep Reading

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

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