Package com.quantfinlib.volatility

@NullMarked package com.quantfinlib.volatility

Volatility models: EWMA, GARCH-family, HAR-RV, a model-free volatility index and a systematic/idiosyncratic decomposition. EwmaVolatility (RiskMetrics exponentially-weighted variance, λ = 0.94), Garch11 (Gaussian MLE with variance targeting; conditional variances and mean-reverting k-step forecasts) and GjrGarch11 (Glosten, Jagannathan and Runkle, 1993: a down move raises next-period variance by α + γ, an up move by α; a fitted γ near 0 indicates a symmetric series), Egarch11 (Nelson, 1991: log-variance dynamics, leverage as γ < 0, no positivity constraints needed; one-step forecasts exact, multi-step refused since the log recursion forecasts the median, not the mean), and HarRv (Corsi, 2009: heterogeneous autoregressive realized-volatility model, daily, weekly and monthly horizons fitted by OLS; pair it with microstructure.JumpRobustVolatility's bipower variance to keep jumps out of the forecast). Also: VolatilityIndex (a VIX-style index computed model-free from an option chain by variance-swap replication; it includes the smile, so it usually sits above ATM implied vol) and VolatilityDecomposition (systematic versus idiosyncratic: the OLS split Var(asset) = β²·Var(market) + Var(residual), separating what an index hedge removes from what needs diversification or single-name hedges). Historical volatility lives in risk.RiskMetrics.annualizedVolatility and EwmaVolatility; implied volatility in pricing.BlackScholes.impliedVol, pricing.Black76, and the two vol surfaces. Feed the outputs to parametric VaR, vol targeting, or option pricing; stochastic volatility lives in pricing.Heston.

Class	Description
Egarch11	EGARCH(1,1) — Nelson's exponential GARCH, the log-variance dynamics the plain family cannot express:
Egarch11.Fit
Egarch11.Params
EwmaVolatility	Exponentially weighted moving average variance (RiskMetrics Technical Document, J.P.
Garch11	GARCH(1,1) volatility model (Bollerslev 1986, J.
Garch11.Fit	A fitted model with its uncertainty.
Garch11.Params
GjrGarch11	GJR-GARCH(1,1,1) — GARCH with the leverage term equity markets demand:
GjrGarch11.Fit
GjrGarch11.Params
HarRv	HAR-RV (Corsi's Heterogeneous AutoRegressive realized-volatility model) — the forecasting benchmark GARCH papers have to beat, and it is three regressors and an intercept:
HarRv.Fit	Fitted coefficients with Newey-West standard errors (order: intercept, daily, weekly, monthly), residual variance and the regression size.
HarRv.Params	Fitted coefficients: rv⁺ = c + βd·d + βw·w + βm·m.
InformationCriteria	AIC and BIC information criteria for model selection.
Innovation	Distribution of the standardised innovation z_t = r_t / √h_t in the GARCH-family likelihoods.
RangeVolatility	Range-based volatility estimators — the free lunch hiding inside every OHLC bar: the high-low range carries far more information about the day's variance than the close alone, so a range estimator reaches a given precision with several times fewer bars than close-to-close.
VolatilityDecomposition	Systematic vs idiosyncratic volatility — the decomposition behind "how much of this stock's risk is the market, and how much is the company?"
VolatilityDecomposition.Decomposition	Systematic and idiosyncratic split of an asset's variance.
VolatilityIndex	A VIX-style market volatility index — the "fear gauge": the market's own 30-day volatility expectation, read model-free out of an option chain.