Skew Kurtosis

Published 2026-01-26. Last updated 2026-04-17. Editorial review: Know Your PMS editorial standards. By Abhimanyu Kucheria for Know Your PMS.

Topic cluster: Risk & Return Metrics

Headline CAGR hides the journey. This cluster explains drawdowns, volatility, rolling returns, capture ratios, and risk-adjusted measures — with Indian PMS factsheet context.

Pillar guide: Max Drawdown Explained

More in this cluster:

• Max Drawdown Explained
• Volatility Explained
• Rolling Returns Guide
• Calmar Ratio Guide
• Information Ratio Guide

What it means (plain English)

Skew measures asymmetry of return distribution—negative skew means frequent small gains and occasional large losses (lottery reversed). Kurtosis measures fat tails—more extreme moves than normal distribution predicts.

Indian equity PMS often show negative skew in concentrated growth—smooth climbs, cliff drops on bad news. Sharpe can look fine until kurtosis bites.

Skew/kurtosis rarely appear on retail factsheets—compute from monthly return series or ask quants. High kurtosis warns tail risk beyond max drawdown sample.

Pair with worst month, profit factor, and tail risk guides. Negative skew mandates need smaller sizing.

Worked example (Indian PMS scenario)

Monthly return distribution: mean +1.2%, std dev 5%, negative skew from occasional −9% months (policy shocks), fat tails (kurtosis > 3) from one +11% month on small-cap rally. Sharpe looks fine; tail risk is ugly.

On ₹1 crore, a −9% month is ₹9 lakh—three such months in five years dominate memory more than twelve +2% months. Positive skew (lottery-like) helps aggressive investors; negative skew hurts retirees.

Ask managers for monthly return histogram or skew stats. Momentum small-cap PMS often shows positive skew in bulls, negative in bears—know which regime you're entering.

Why it matters for PMS scheme selection

Volatility averages away the shape of risk—skew and kurtosis show whether crashes hide inside 'okay' Sharpe ratios.

See the complete PMS evaluation framework

• Detects hidden crash risk in smooth CAGR
• Explains Sharpe breakdown in stress
• Supports sizing for negatively skewed managers
• Differentiates Gaussian assumptions in models
• Complements max drawdown with distribution view

How to interpret it (practical checklist)

1. Compute skew/kurtosis on 36+ monthly returns
2. List worst 3 months alongside statistics
3. Compare to benchmark distribution shape
4. Check if negative skew rising over time
5. Read concentration with skew lens
6. Avoid models assuming normal returns only
7. Stress test with fat-tail scenarios

Explore related metrics · Compare PMS schemes · Skewness

Common pitfalls (how this gets misused)

Read our methodology for assumptions and limitations.

• Sharpe-only risk assessment
• Skew on too few monthly observations
• Ignoring positive skew lottery dependence
• Assuming past tail won't repeat
• Confusing skew with beta
• Kurtosis from one outlier month only

Related metrics to review together

Use this guide alongside these metrics to avoid one-number decision-making:

Browse all metrics

• Skewness
• Kurtosis
• Cvar
• Tail Risk Score

Related guides

• PMS Sharpe Vs Sortino
• Max Drawdown Explained
• Rolling Returns Guide
• Volatility Explained
• Alpha Beta Explained

See also

• Tail risk basics
• Volatility explained
• Worst month best month
• PMS Sharpe vs Sortino

FAQs

What skew is concerning for PMS?

Strong negative skew with high kurtosis in concentrated mandates warrants caution—expect occasional large losses. Context with max drawdown history.

Do Indian PMS publish skew?

Uncommon in retail factsheets. Derive from monthly performance or request in quant due diligence.

Skew vs Sortino?

Sortino penalizes downside volatility; skew describes distribution shape. Both downside-focused—use together.

Next: How to compare PMS schemes · Compare schemes · All guides