Two Portfolios Can Have the Exact Same Volatility and Very Different Odds of a Catastrophic Month. Skewness Is Why
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5 min read
Say you are comparing two strategies. Both show a monthly volatility of about 5.3%, the standard deviation of their monthly returns, a common way to measure how much a return typically swings around its own average. On that one number, they look equally risky. But one of them has a real, measurable chance of a single month erasing more than a year of steady gains, and the other does not. Volatility alone cannot tell you which is which. A different number can: skewness.
Standard deviation treats a surprise the same way no matter which direction it points. A month that beats the average by 5% and a month that misses it by 5% count as an identical-sized surprise to this number. It tells you how spread out returns typically are, nothing about whether the rare, larger surprises tend to be good ones or bad ones.
NetWort has already covered how often extreme moves happen at all and why volatility alone doesn't capture how painful a real drawdown feels. Skewness answers a third, separate question: when a return distribution does throw a surprise, does it lean toward a rare big win or a rare big loss?
Skewness measures the lopsidedness of a return distribution around its average, using the cube of each return's distance from the mean rather than the square that standard deviation uses. Cubing keeps the sign, so it captures direction, not just size.
Skewness = average of ((return − mean)³) ÷ (standard deviation)³
Negative skewness describes a strategy that usually posts small, steady gains, then occasionally suffers a rare, severe loss. This is the classic shape of "picking up pennies in front of a steamroller": calm most of the time, brutal on the bad days.
Positive skewness describes the opposite shape: frequent small losses or flat stretches, punctuated by an occasional large gain. It usually feels worse to hold day to day, even when the long-run math works out fine or better.
A skewness of zero means the distribution is symmetric, gains and losses of a given size are equally likely. Real portfolios are rarely exactly zero; the question worth asking is which side they lean toward.
This is a constructed example, not real market data, built only to make the shape difference concrete.
| Strategy A | Strategy B | |
|---|---|---|
| Pattern | +1.5% in 23 of 24 months, then one month at -25% | -1.5% in 23 of 24 months, then one month at +25% |
| Average monthly return | +0.40% | -0.40% |
| Monthly volatility (standard deviation) | 5.30% | 5.30% |
| Skewness | -4.59 (negative) | +4.59 (positive) |
| Compounded return over the 24 months | +5.6% | -11.7% |
Both strategies carry the identical monthly volatility. Strategy A, negatively skewed, looks calmer almost every month and ends up ahead over this particular window, right up until its one bad month. Strategy B, positively skewed, looks worse almost every month, dragged down by small losses, and only its rare big month kept it from a much larger loss over this stretch. A volatility number alone would have called these two strategies identical. They are not.
Exchange-traded products that profited from calm markets by shorting VIX futures, the best known was Credit Suisse's XIV note, spent years posting small, steady gains as volatility stayed low. Then, on 5 February 2018, the VIX jumped roughly 116% in a single session, from around 17 to over 37, the largest one-day percentage and point move in the index's history, as Bloomberg reported. XIV lost about 96% of its value in that one session, according to a peer-reviewed study published in the CFA Institute's Financial Analysts Journal, a loss CNBC and a contemporaneous industry analysis separately put at roughly $2 billion erased in a day. Credit Suisse terminated the note days later. That is negative skewness playing out in public: years of small, boring gains, then one session that undid nearly all of it.
Trend-following managed futures funds, often called CTAs (commodity trading advisors), sit at the other end of the shape. They frequently sit flat or post small losses while waiting for a sustained price trend to develop, then capture outsized gains when one does. In 2008, while the S&P 500 fell 38.49% for the year, the Barclay CTA Index, which tracked 565 managed futures programs at the time, gained 14.09%, a divergence researcher Kathryn Kaminski later named "crisis alpha" after being asked by a pension fund to explain it, a finding later expanded into an academic study on trend-following and crisis performance. That is positive skewness in practice: a strategy that can feel unrewarding for long stretches, justified by the rare period where it pays off disproportionately.
NetWort does not currently show a skewness figure directly. What it does show, side by side on the Explore screener, is an asset's volatility and its max drawdown, the worst peak-to-trough loss in its history. Those two numbers together get you most of the way to the same question skewness answers: if two assets carry similar volatility but very different max drawdowns, the one with the deeper worst case is showing you a hint of negative skew, even without a formal skewness statistic attached. Bitcoin's own price history is a genuinely volatile, actively tracked example worth checking this way before sizing any position in it.