Concept
Jump Detection
Jump Detection, also known as bipower variation, is a Volatility concept. The Library holds 1 implementations, each one a working definition you can pull into Quant.
Top Jump Detection indicators
1 total
What is Jump Detection?
Jump detection separates the discontinuous part of price movement, sudden repricings and gaps typically tied to news, from the continuous diffusion-like part. The canonical high-frequency tool is bipower variation, due to Barndorff-Nielsen and Shephard: realized variance, the sum of squared intraday returns behind realized volatility, captures continuous variation plus jumps, while bipower variation, built from products of absolute adjacent returns, is robust to jumps and estimates the continuous part alone. A statistically large gap between the two flags a jump.
Chart-level implementations are simpler: returns beyond a volatility-scaled threshold, CUSUM-style tests for abrupt level shifts, or plain gap detection. However it is measured, the point is the same: jumps are a different risk from ordinary volatility, because price can move through a stop without ever trading at the prices in between.
How traders use it
- To clean volatility estimates: excluding or down-weighting identified jump days keeps one-off shocks from inflating trailing volatility and everything sized off it.
- As an event flag: detected jumps usually coincide with identifiable catalysts, so they can trigger a regime reassessment or pause systems tuned to normal conditions; see event-driven volatility.
- For risk budgeting: markets that jump frequently warrant smaller size at a given measured volatility, since stops cannot be relied on to cap loss through a gap.
Related concepts · Volatility estimators
Concept family
Volatility
56 concepts mapped · 43 in the Library
Jump Detection FAQ
What is bipower variation?
An estimator of the continuous part of return variance. Instead of summing squared intraday returns, it sums products of absolute adjacent returns (with a scaling constant). A single outsized return enters each product next to an ordinary neighbor, so jumps barely move it, while they dominate squared-return measures. The difference between realized variance and bipower variation therefore estimates the jump contribution.
Why do jumps matter for volatility estimates?
Because squaring returns lets single shocks dominate: one large jump inside a lookback window can inflate a volatility estimate for as long as it remains in the window, misrepresenting typical conditions. Separating jumps gives a cleaner estimate of ordinary volatility plus an honest count of gap events, which matter on their own because gaps can move price straight through protective stops.
Build Jump Detection your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.