Struct JumpDiffusionPriorResult 

#[non_exhaustive]
pub struct JumpDiffusionPriorResult {

    pub prior: JumpDiffusionPrior,
    pub window_years: f64,
    pub annualization_factor: f64,
    pub prior_jump_variance: f64,
    pub realized_total_variance: f64,
    pub realized_diffusive_variance: f64,
    pub realized_jump_variance: f64,
    pub cross_check_relative_gap: f64,
    pub diffusion_corrected: bool,
    pub raw_event_count: usize,
    pub max_test_statistic: f64,
    pub noise_scale: JumpPriorNoiseScale,
    pub truncation_corrected: bool,
    pub detection_probability: Option<f64>,
    pub detection_cutoff: Option<f64>,
}

Parametric jump-diffusion prior payload: the JumpDiffusionPrior anchor plus the realized-variance legs a consumer needs to cross-check it.

The realized legs use the same conventions as FerroRisk’s realized_jump_split (undemeaned quadratic variation Σr²/n, bipower variation (π/2)·Σ|rᵢ||rᵢ₋₁|/(n−1) over close-to-close log returns, both annualized by the profile factor), so prior_jump_variance vs realized_jump_variance is exactly the comparison FerroRisk’s jump_prior_cross_check performs.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

prior: JumpDiffusionPrior

The physical-measure Merton triple with uncertainty.

window_years: f64

Window length in years (valid returns / annualization factor).

annualization_factor: f64

Annualization factor used for λ and the realized legs (e.g. 252).

prior_jump_variance: f64

Prior-implied annualized jump variance λ·(μ_J² + σ_J²).

realized_total_variance: f64

Annualized undemeaned quadratic variation of the window’s log returns.

realized_diffusive_variance: f64

Annualized bipower variation (jump-robust diffusive leg).

realized_jump_variance: f64

Annualized realized jump variance max(total − diffusive, 0).

cross_check_relative_gap: f64

Symmetric relative gap between prior_jump_variance and realized_jump_variance: |prior − realized| / max(prior, realized) (0 when both are 0), matching FerroRisk’s cross-check statistic.

diffusion_corrected: bool

Whether the per-period bipower diffusive variance was subtracted from the raw jump-size variance before taking σ_J.

raw_event_count: usize

Raw scale-level threshold breaches before consolidation, after the periodic-boundary wrap region has been excluded (see the boundary_candidates_excluded diagnostic).

max_test_statistic: f64

Maximum jump test statistic observed across the detected events.

noise_scale: JumpPriorNoiseScale

Noise scale the detector’s test statistic was standardized with.

truncation_corrected: bool

Whether the truncation correction (#86) was applied: λ inflated by the fitted 1/p̂_det, μ/σ from the outer-truncated-normal EM fit. false means the detection-conditional triple was published (see the truncation_correction_skipped diagnostic for why).

detection_probability: Option<f64>

Effective detection probability: N / (λ_total·T), so intensity = (N/T) / detection_probability holds exactly. Equals the fitted truncated-normal p̂ when every detected size is outside the modeled cutoff; interior detections (threshold blur / multi-return channel), which enter λ at face value, pull it toward 1. Some iff truncation_corrected.

detection_cutoff: Option<f64>

Level-1 detection cutoff in simple-return units (|r| > cutoff is detectable). Some iff truncation_corrected.

Trait Implementations

impl Clone for JumpDiffusionPriorResult

fn clone(&self) -> JumpDiffusionPriorResult

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for JumpDiffusionPriorResult

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for JumpDiffusionPriorResult

fn eq(&self, other: &JumpDiffusionPriorResult) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for JumpDiffusionPriorResult