Autoregressive conditional heteroskedasticity (ARCH) is a statistical model for time series data that describes the variance of the current error term as a function of the sizes of previous periods' error terms. It was introduced by Robert F. Engle in 1982 in his work modeling the variance of United Kingdom inflation. ARCH models capture volatility clustering, the tendency of financial time series to alternate between periods of large swings and periods of relative calm, and they are widely used to model time-varying volatility in financial and economic data. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/
Facts
Connections
Is Kind Of Object
Entity-backed identity for the object-kind enum value this mathematical object already carries, resolved to a mathematics concept by an explicit value-to-entity map (phase 3 bucket conversion, docs\design_entity_backed_browse_buckets_20260928.md). The object-kind fact itself stays on the object unchanged.
Sources
1. Autoregressive Conditional Heteroskedasticity (Wikipedia)
Reader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.
Sign in to dispute this or suggest a correction.