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Commodity Futures

These entries address investing and trading in commodities and commodity futures as an alternative asset class to equities.

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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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Exploitability of Deep Value across Asset Classes

Is value investing particularly profitable when the price spread between cheap and expensive assets (the value spread) is extremely large (deep value)? In their November 2017 paper entitled “Deep Value”, Clifford Asness, John Liew, Lasse Pedersen and Ashwin Thapar examine how the performance of value investing changes when the value spread is in its largest fifth (quintile). They consider value spreads for seven asset classes: individual stocks within each of four global regions (U.S., UK, continental Europe and Japan); equity index futures globally; currencies globally; and, bond futures globally. Their measures for value are:

  • Individual stocks – book value-to-market capitalization ratio (B/P).
  • Equity index futures – index-level B/P, aggregated using index weights.
  • Currencies – real exchange rate based on purchasing power parity.
  • Bonds – real bond yield (nominal bond yield minus forecasted inflation).

For each of the seven broad asset classes, they each month rank assets by value. They then for each class form a hedge portfolio that is long (short) the third of assets that are cheapest (most expensive). For stocks and equity indexes, they weight portfolio assets by market capitalization. For currencies and bond futures, they weight equally. To create more deep value episodes, they construct 515 sub-classes from the seven broad asset classes. For asset sub-classes, they use hedge portfolios when there are many assets (272 strategies) and pairs trading when there are few (243 strategies). They conduct both in-sample and out-of-sample deep value tests, the latter buying value when the value spread is within its top inception-to-date quintile and selling value when the value spread reverts to its inception-to-date median. Using data as specified and as available (starting as early as January 1926 for U.S. stocks and as late as January 1988 for continental Europe stocks) through September 2015, they find that:

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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

Asset Class Value Spreads

Do value strategy returns vary exploitably over time and across asset classes? In their October 2017 paper entitled “Value Timing: Risk and Return Across Asset Classes”, Fahiz Baba Yara, Martijn Boons and Andrea Tamoni examine the power of value spreads to predict returns for individual U.S. equities, global stock indexes, global government bonds, commodities and currencies. They measure value spreads as follows:

  • For individual stocks, they each month sort stocks into tenths (deciles) on book-to-market ratio and form a portfolio that is long (short) the value-weighted decile with the highest (lowest) ratios.
  • For global developed market equity indexes, they each month form a portfolio that is long (short) the equally weighted indexes with book-to-price ratio above (below) the median.
  • For each other asset class, they each month form a portfolio that is long (short) the equally weighted assets with 5-year past returns below (above) the median.

To quantify benefits of timing value spreads, they test monthly time series (in only when undervalued) and rotation (weighted by valuation) strategies across asset classes. To measure sources of value spread variation, they decompose value spreads into asset class-specific and common components. Using monthly data for liquid U.S. stocks during January 1972 through December 2014, spot prices for 28 commodities during January 1972 through December 2014, spot and forward exchange rates for 10 currencies during February 1976 through December 2014, modeled and 1-month futures prices for ten 10-year government bonds during January 1991 through May 2009, and levels and book-to-price ratios for 13 developed equity market indexes during January 1994 through December 2014, they find that:

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Trend-following Managed Futures to Make Retirement Safer?

Should retirement portfolios include an allocation to managed futures? In his October 2017 paper entitled “Using Trend-Following Managed Futures to Increase Expected Withdrawal Rates”, Andrew Miller compares seven 30-year retirement scenarios via backtests and modified backtests. Specifically, he compares maximum annual real withdrawal rates as a percentage of initial assets that do not exhaust any 30-year retirement portfolios starting each year during 1926-2012 (SAFEMAX). The seven scenarios, all rebalanced annually, are:

  1. Historical Returns 50-50: uses actual annual returns for a 50% allocation to large-capitalization U.S. stocks and a 50% allocation to intermediate-term U.S. Treasuries.
  2. Historical Returns 50-40-10: same as Scenario 1, except shifts 10% of the Treasuries allocation to a trend-following managed futures strategy that is long and short 67 stocks, bonds, currencies and commodities futures series based on equally weighted 1-month, 3-month and 12-month past returns with a 10% annual volatility target.
  3. Lower Historical Returns 50-50: same as Scenario 1, but reduces monthly returns for stocks and Treasuries by 0.19%, reflecting end-of-2016 valuations.
  4. Lower Historical Returns 50-40-10: same as Scenario 2, but reduces monthly returns for stocks, Treasuries and managed futures by 0.19%.
  5. Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 2, but reduces the Sharpe ratio for managed futures from an historical level to 0.5.
  6. Lower Historical Returns/Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 4, but reduces Sharpe ratio for managed futures to 0.5.
  7. Historical Returns 50-50 with Trend Following for Stocks: same as Scenario 1, but each month puts the stocks allocation into stocks (30-day U.S. Treasury bills) when the return on stocks is positive (negative) over the prior 12 months.

He ignores all trading frictions, fees and taxes. Using monthly asset class returns as specified and monthly inflation data during January 1926 through December 2012, he finds that: Keep Reading

Are Managed Futures ETFs Working?

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider three managed futures ETFs, all currently available:

  1. WisdomTree Managed Futures Strategy (WDTI).
  2. First Trust Morningstar Managed Futures Strategy (FMF).
  3. ProShares Managed Futures Strategy (FUT).

We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and correlation of returns with those of SPDR S&P 500 (SPY) as key performance statistics. We use Eurekahedge CTA/Managed Futures Hedge Fund Index (the index) as a benchmark. Using monthly returns for the three funds as available through August 2017, and contemporaneous monthly returns for the benchmark index and SPY, we find that: Keep Reading

Crude Oil Seasonality

Does crude oil exhibit an exploitable price seasonality? To check, we examine three monthly series:

  1. Spot prices for West Texas Intermediate (WTI) Cushing, Oklahoma crude oil since the beginning of 1986 (31+ years).
  2. Nearest expiration futures prices for crude oil since April 1983 (34+ years).
  3. Prices for United States Oil (USO), an exchange-traded implementation of short-term crude oil futures since April 2006 (11+ years).

We focus on average monthly changes/returns by calendar month and variabilities of same. Using monthly prices from respective inceptions of these series through August 2017, we find that: Keep Reading

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
Vanguard REIT ETF (VNQ)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)

We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily or monthly returns:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions at daily and monthly frequencies.
  2. Performance during S&P 500 Index bear markets as defined by the index being below its 10-month simple moving average (SMA10) at the end of the prior month.

Using daily and monthly dividend-adjusted closing prices for the 12 ETFs from their respective inceptions through June 2017, and contemporaneous daily and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through June 2017, we find that: Keep Reading

Average Past Return Sign Momentum

Does average sign of recent returns work as well as recent cumulative return as a momentum metric? In their May 2017 paper entitled “Returns Signal Momentum”, Fotis Papailias, Jiadong Liu and Dimitrios Thomakos introduce and test a momentum strategy (RSM) based on the equally weighted average signs (1 for positive and 0 for negative) of past returns over a given lookback interval. This metric employs each of the past returns during the lookback interval, not a single cumulative return as in times series (intrinsic or absolute) momentum. It considers only signs of past returns, not their magnitudes as in conventional relative momentum. They focus on monthly returns over a lookback interval of 12 months. They test RSM on a universe of 55 of the most liquid futures/forwards: 24 commodities; 9 currency exchange rates versus the U.S. dollar; 9 developed country equity indexes; and, 13 government bonds of various maturities from six developed countries. Their strategy is each month long (short) a contract series when average sign of its last 12 monthly returns is above (below) a threshold. They consider two types of thresholds: (1) fixed over the test period, with the featured optimal value selected by experimentation; and, (2) time-varying, each month choosing the best-performing value (from among 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8) over the prior 24 months. Using returns for the 55 futures/forwards series as available to support a strategy test period of January 1985 through March 2015, they find that:
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