Uses of Record Class
com.quantfinlib.volatility.GjrGarch11.Params

Packages that use GjrGarch11.Params

Package

Description

com.quantfinlib.volatility

Volatility models: EwmaVolatility (RiskMetrics exponentially-weighted variance, λ = 0.94), Garch11 (Gaussian MLE with variance targeting; conditional variances and mean-reverting k-step forecasts) and GjrGarch11 (the leverage-effect asymmetry equity indices demand — a down move raises tomorrow's variance by α + γ, an up move by only α; fitting γ ≈ 0 is itself the finding that the series is symmetric), Egarch11 (Nelson's log-variance dynamics: leverage as a SIGN — γ < 0 — with no positivity constraints by construction; one-step forecasts exact, multi-step deliberately refused since the log recursion forecasts the median, not the mean), and HarRv (Corsi's heterogeneous autoregressive realized-vol model — daily/weekly/monthly horizons by plain OLS, the forecasting benchmark GARCH papers have to beat; pair it with microstructure.JumpRobustVolatility's bipower variance to keep jumps out of the forecast).

Uses of GjrGarch11.Params in com.quantfinlib.volatility

Methods in com.quantfinlib.volatility that return GjrGarch11.Params

Modifier and Type	Method	Description
static GjrGarch11.Params	GjrGarch11.fit(double[] returns)	Fits GJR-GARCH(1,1) to (demeaned) returns by MLE with variance targeting.

Methods in com.quantfinlib.volatility with parameters of type GjrGarch11.Params

Modifier and Type	Method	Description
static double[]	GjrGarch11.conditionalVariances(double[] returns, GjrGarch11.Params params)	Conditional variance series under the fitted parameters (seeded at sample variance).
static double	GjrGarch11.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.