Time Series Momentum, Volatility Scaling, And Crisis Alpha

If you couldn’t tell from our recent monster commodity futures post, we’ve been thinking a lot about futures recently. The futures research area is relatively “fresh,” and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer — buy cheap/qualitybuy strength, and embrace relative performance pain.

As part of our research education series on futures, we recently reviewed an engrossing paper, “Time Series Momentum and Volatility Scaling,” by Abby Y. Kim, Yiuman Tse, John K. Wald (KTW), which revisits the findings regarding another futures paper, “Time Series Momentum,” by Tobias J.Moskowitz, Yao Hua Ooi, Lasse Heje Pedersen (MOP).

We recap the core findings from KTW and MOP below:

Time Series Momentum Key Insights (by MOP)

The key elements from MOP are as follows:

  1. 58 future contracts cover the 1965-2009 period (24 commodities, 12 cross-rate currency pairs, 9 developed equity indexes and 13 developed government bonds).
  2. Strategy is based on time-series momentum, i.e, were past returns positive or negative? Positive = long; negative = short
  3. Positions are volatility weighted so that high-volatility assets will not dominate returns. This is similar to an equally weighted risk parity portfolio, in which the weights refer to risk. In MOP, target vol is around 40% versus average vol of 19%, effectively 2x leverage.
  4. A volatility-scaled TSMOM strategy (12 month lookback, 1 month hold) generates 1.09% excess returns monthly (w/t-stat of 5.4), after controlling for Asness et al. value/momentum “everywhere” factors for global stock returns, bond returns, currencies and commodities.
  5. TSMOM loads on Asness et al.’s cross-sectional momentum, but their everywhere factor does not explain it.

Time Series Momentum and Volatility Scaling Key Insights (by KTW)

The KTW paper revisits the analysis from MOP. Using 55 futures contracts cover the 1985-2009 period, the authors confirm the results from MOP. However, KTW identify the following results that appear to conflict with MOP:

  1. Using an unscaled, equal-weighted method, the alpha of a TSMOM portfolio drops to 0.39% per month, v.s. vol-scaled, TSMOM’s 1.08% monthly alpha.
  2. Without vol scaling, the portfolio alpha is similar to a buy-and-hold futures portfolio. Using the unscaled method, the alpha of a buy-and-hold strategy is 0.34% per month. Moreover, when buy-and-hold is scaled using the MOP method, it generates a 0.73% estimate of monthly alpha.
  3. KTW argue that the strong TSMOM returns identified by MOP were due to leveraging a strategy that happened to have a positive alpha estimate for a buy-and-hold “passive” strategy during this sample period.
  4. When one examines “unlevered” TSMOM (i.e., with no volatility scaling) it does not significantly outperform buy-and-hold.
  5. Bottom line: the outperformance of TSMOM is largely driven by vol-scaling, or leverage, not by abnormal returns associated with TSMOM.

Reconciling the Disagreement

We think MOP and KTW have inspired an intriguing debate. To get closer to understanding the “truth,” we conducted our own research into the question. Our sample covers the 1998-7/2016 period, and includes 38 future contracts for commodities, fixed income, and equities (22 commodities contracts, 7 developed bond contracts, 9 equity index contracts). We excluded currency contracts and a few other contacts because our ability to trade contracts is limited to what is available at Interactive Brokers.

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