Improving Momentum with a Volatility Regime Filter

Regime classification plays an important role in portfolio management. There are several approaches to detecting market regimes, ranging from simple to highly sophisticated methods. Reference [1] proposes a simple regime classification framework for momentum trading.

The paper utilizes an expanding-window percentile rule applied to realized market volatility. Realized volatility is computed from daily market returns as the rolling standard deviation over a 21-trading-day window. On each trading day, the prevailing volatility is compared with the 75th percentile of the expanding distribution of all realized volatility observations through the prior day. Days above the threshold are classified as turbulent, while the remainder are classified as calm.

In calm regimes, the strategy maintains full exposure to the momentum factor with a weight of one. In turbulent regimes, it reduces momentum exposure to zero and holds cash earning the risk-free rate on the full notional. There is no fractional exposure or interpolation between states.

The author pointed out,

The evidence points in a specific direction. On risk-adjusted return, the conditioned strategy holds an edge that is economically meaningful but statistically marginal, with an excess-return Sharpe ratio of 0.837 against 0.592 for the unconditional strategy, significant only at the 10% level. On tail risk, the evidence is far clearer. The conditioned strategy reduced maximum drawdowns sharply and consistently across five crisis episodes spanning five decades, with the widest gap during the Global Financial Crisis, where it lost 5.1% against the unconditional strategy's 57.1%. A subperiod decomposition locates the advantage almost entirely within crisis-containing periods. We read these findings together as showing that regime-conditioning behaves as a form of crash insurance. It carries a small cost during calm markets and pays off during turbulent ones, which explains why a full-sample average understates its value and why a measure as direct as maximum drawdown reveals it more clearly than the Sharpe ratio.

In short, the paper concludes that implementing this regime filter improves risk-adjusted returns, with a particularly significant reduction in drawdowns.

An interesting aspect of the paper is that the author interprets the volatility-managed momentum strategy as a form of crash insurance: it carries a small cost during calm periods and pays off during turbulent ones, rather than as an alpha-generating strategy, in contrast to some broad claims in the volatility-managed literature.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Fabian, S. W. (2026). Statistical Evaluation of Momentum, Mean Reversion, and Regime Dynamics in U.S. Equity Markets (Working paper). Eastern Illinois University.

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