Rolling Kurtosis Pass Node
Rolling Kurtosis — Series Input
Overview
The Rolling Kurtosis Pass Node computes excess kurtosis over a rolling window — measuring the "fat-tailedness" of a return distribution relative to a normal distribution.
Excess kurtosis = 0 for a normal distribution. Positive excess kurtosis (leptokurtic) means more extreme outliers than expected; negative (platykurtic) means thinner tails. Financial returns typically show positive excess kurtosis.
Formula
Parameters
| Parameter | Default | Description |
|---|---|---|
| period | 20 | Rolling window in bars |
Inputs & Outputs
| Slot | Direction | Type | Description |
|---|---|---|---|
| input | Input | { values, timestamps } | Any upstream numeric series |
| values | Output | (number | null)[] | Excess kurtosis per bar; nulls during warm-up |
| timestamps | Output | number[] | Unix timestamps aligned to input |
Use Cases
Tail Risk Detection
Rising kurtosis signals increasing tail risk — consider tightening position sizing or using CVaR-based risk management.
Regime Classification
High kurtosis often accompanies volatile or crisis regimes. Use alongside skewness to classify the current distribution shape.
Options Pricing Context
Fat tails (positive kurtosis) imply options are mispriced by Black-Scholes. Use kurtosis to adjust model inputs or identify mispricings.
Tips & Best Practices
Use Long Enough Windows
Kurtosis estimates are unreliable with small samples. Use period ≥ 30 for meaningful estimates; period ≥ 60 for robust ones.
Combine with Skewness
Kurtosis alone doesn't tell you the direction of the tail risk. Pair with Rolling Skewness Pass to understand whether fat tails lean negative or positive.
Apply to Returns, Not Prices
Feed log-returns or simple returns — not raw prices — into this node. Price-level kurtosis is dominated by trend and scale effects.