Class GjrGarch11

java.lang.Object

com.quantfinlib.volatility.GjrGarch11

public final class GjrGarch11
extends Object

GJR-GARCH(1,1,1) — GARCH with the LEVERAGE term equity markets demand:

  h_t = ω + (α + γ·1{r<0}) r_{t-1}² + β h_{t-1}

A down move raises tomorrow's variance by α + γ; an equal up move by only α. That asymmetry (Glosten-Jagannathan-Runkle, 1993) is not a refinement — on equity indices γ is typically LARGER than α, i.e. most of the ARCH effect is the leverage effect, and a symmetric Garch11 systematically underestimates post-selloff volatility. Fitting a GJR and finding γ ≈ 0 is itself information: the series has no asymmetry and the simpler model suffices (FX pairs often land there; equity indices rarely do).

Estimation mirrors Garch11: Gaussian MLE with variance targeting (ω = σ̄²(1 − α − γ/2 − β), since negative returns carry half the mass under symmetry) and a coarse-to-fine grid over (α, γ, β) — derivative-free, deterministic, robust. Same 100-return minimum, same conditional-variance and mean-reverting forecast accessors, with persistence α + γ/2 + β. Research lane.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	GjrGarch11.Params

Method Summary

Modifier and Type	Method	Description
static double[]	conditionalVariances(double[] returns, GjrGarch11.Params params)	Conditional variance series under the fitted parameters (seeded at sample variance).
static GjrGarch11.Params	fit(double[] returns)	Fits GJR-GARCH(1,1) to (demeaned) returns by MLE with variance targeting.
static double	forecastVariance(double[] returns, GjrGarch11.Params params, int horizon)	k-step-ahead variance forecast — mean-reverts to the unconditional variance at the persistence rate, exactly as Garch11 but with the asymmetric first step.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

fit

public static GjrGarch11.Params fit(double[] returns)

Fits GJR-GARCH(1,1) to (demeaned) returns by MLE with variance targeting.

conditionalVariances

public static double[] conditionalVariances(double[] returns,
 GjrGarch11.Params params)

Conditional variance series under the fitted parameters (seeded at sample variance).

forecastVariance

public static double forecastVariance(double[] returns,
 GjrGarch11.Params params,
 int horizon)

k-step-ahead variance forecast — mean-reverts to the unconditional variance at the persistence rate, exactly as Garch11 but with the asymmetric first step.