atelier_quant 0.0.12

Quantitative Finance Tools & Models for the atelier-rs engine
Documentation 
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//! Self-exciting Hawkes point-process model.
//!
//! This module provides:
//!
//! - [`HawkesProcess`] — simulation via Ogata's modified thinning
//!   algorithm (see [`core`]).
//! - [`kernel`] — [`ExcitationKernel`](kernel::ExcitationKernel) trait
//!   and [`ExponentialKernel`](kernel::ExponentialKernel) implementation.
//! - [`quadrature`] — adaptive Simpson's rule for conditional-mean
//!   forecasting.
//! - [`estimation`] — maximum-likelihood estimation with gradient
//!   ascent, goodness-of-fit diagnostics (AIC / BIC), and
//!   time-rescaling residuals.
//! - [`errors`] — error types for invalid parameters and convergence
//!   failures.

pub mod kernel;
pub mod quadrature;
pub mod core;
pub use core::HawkesProcess;
pub mod errors;
pub mod estimation;