@krzysztofsztompka/garch
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    Type Alias VarBacktestOptions

    varBacktest's specification: the model, the VaR level, the windows and how the VaR is read.

    type VarBacktestOptions = {
        dist?: Distribution;
        exogenous?: ArrayLike<number>;
        level: number;
        mean?: MeanModel;
        method?: "parametric" | "fhs";
        minTrain?: number;
        model?: GarchModel;
        step?: number;
        targetVariance?: boolean;
    }
    Index
    exogenous?: ArrayLike<number>

    GARCH-X: x aligned with the returns (garch and gjr); see FitOptions.exogenous. Forecasts hold it at its last value.

    level: number
    mean?: MeanModel

    A fitted constant (default) or zero; see FitOptions.mean.

    method?: "parametric" | "fhs"

    How each origin's VaR is read: parametric (default) from the fitted innovations' family (tailRisk); fhs, filtered historical simulation, from the empirical quantile of that fit's standardised residuals over its train window (filteredTailRisk).

    minTrain?: number
    model?: GarchModel
    step?: number
    targetVariance?: boolean

    Variance targeting (garch and gjr); see FitOptions.targetVariance.