What is skewness in a portfolio?

Skewness measures the asymmetry of a return distribution around its mean. Positive skewness means occasional large gains with more frequent small losses. Negative skewness means occasional large losses with more frequent small gains. Most equity portfolios show slight negative skewness, which standard deviation alone does not capture.

What is kurtosis in investing?

Kurtosis measures the 'fatness' of the tails of a return distribution compared to a normal distribution. High kurtosis (leptokurtic) means extreme events occur more often than a normal distribution would predict. A normal distribution has kurtosis of 3; excess kurtosis above 0 signals heavier tails and higher tail risk.

Why do skewness and kurtosis matter for risk management?

Mean and standard deviation assume a normal distribution, which underestimates the frequency of extreme returns. Negative skewness and high kurtosis together indicate a portfolio that suffers large, sudden losses more often than expected, which is not captured by VaR based on normality assumptions. Tail risk metrics like CVaR work better in this context.

What is a good skewness value for a portfolio?

There is no universally 'good' skewness, but investors generally prefer positive skewness (more frequent small losses, occasional large gains) over negative skewness (more frequent small gains, occasional large losses). In practice, most diversified equity portfolios have slightly negative skewness due to the asymmetric nature of market crashes.

Skewness and Kurtosis: practical guide for portfolio risk

Why skewness and kurtosis matter

Average return and volatility are not enough to describe portfolio risk. Skewness and kurtosis help read distribution shape and tail behavior.

Skewness: asymmetry of returns

Positive skewness: more right-tail outcomes (occasional large gains).
Negative skewness: more left-tail outcomes (occasional large losses).

For many strategies, negative skewness means losses can arrive suddenly and be larger than expected by normal assumptions.

Kurtosis: thickness of tails

Kurtosis captures how heavy tails are relative to a normal distribution.

Higher kurtosis means more extreme events in both tails.
Lower kurtosis means fewer extremes.

In practice, high kurtosis signals that tail risk controls (VaR/CVaR, limits, sizing) are critical.

Practical interpretation

Similar volatility, different skewness: downside risk may still differ a lot.
Similar VaR, different kurtosis: tail events can still be more severe in one portfolio.

Use skewness and kurtosis together with VaR, CVaR, and drawdown.

Next step

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Read related guides: VaR 95%/99% , CVaR 95%/99% , Maximum Drawdown

Disclaimer

This article is not financial advice but an example based on studies, research and analysis conducted by our team.

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Skewness and Kurtosis: practical guide for portfolio risk

Why skewness and kurtosis matter

Skewness: asymmetry of returns

Kurtosis: thickness of tails

Practical interpretation

Next step

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Monte Carlo Simulation for your portfolio: a practical guide

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Next step

Skewness and Kurtosis: practical guide for portfolio risk

Why skewness and kurtosis matter

Skewness: asymmetry of returns

Kurtosis: thickness of tails

Practical interpretation

Next step

Related guides

Portfolio rebalancing: when to do it and why it matters

Monte Carlo Simulation for your portfolio: a practical guide

TWR vs MWRR: which return metric should you trust?

Next step

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