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arch.univariate.GARCH.backcast
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    • Univariate Volatility Models
      • Introduction
      • Examples
      • Forecasting
      • Volatility Forecasting
      • Value-at-Risk Forecasting
      • Forecasting Scenarios
      • Forecasting with Exogenous Variables
      • Mean Models
      • Volatility Processes
        • arch.univariate.ConstantVariance
        • arch.univariate.GARCH
          • C arch.univariate.GARCH
            • arch.univariate.GARCH.backcast
              • M GARCH.backcast
                • Parameters
                  • p resids
                • Returns
                • Return type
            • arch.univariate.GARCH.backcast_transform
            • arch.univariate.GARCH.bounds
            • arch.univariate.GARCH.compute_variance
            • arch.univariate.GARCH.constraints
            • arch.univariate.GARCH.forecast
            • arch.univariate.GARCH.parameter_names
            • arch.univariate.GARCH.simulate
            • arch.univariate.GARCH.starting_values
            • arch.univariate.GARCH.update
            • arch.univariate.GARCH.variance_bounds
            • arch.univariate.GARCH.name
            • arch.univariate.GARCH.num_params
            • arch.univariate.GARCH.start
            • arch.univariate.GARCH.stop
            • arch.univariate.GARCH.updateable
            • arch.univariate.GARCH.volatility_updater
        • arch.univariate.FIGARCH
        • arch.univariate.EGARCH
        • arch.univariate.HARCH
        • arch.univariate.MIDASHyperbolic
        • arch.univariate.ARCH
        • arch.univariate.APARCH
        • Parameterless Variance Processes
        • FixedVariance
        • Writing New Volatility Processes
      • Using the Fixed Variance Process
      • Distributions
      • Results
      • Utilities
      • Background and References
    • Bootstrapping
    • Multiple Comparison Problems
    • Unit Root Testing
    • Cointegration Analysis
    • Long-run Covariance Estimation
    • API Reference
    • Common Type Definitions
    • Change Log
    • M GARCH.backcast
      • Parameters
        • p resids
      • Returns
      • Return type

    arch.univariate.GARCH.backcast¶

    GARCH.backcast(resids: ndarray[tuple[int], dtype[float64]] | Series) → float | ndarray[tuple[int], dtype[float64]][source]¶

    Construct values for backcasting to start the recursion

    Parameters:¶
    resids: ndarray[tuple[int], dtype[float64]] | Series¶

    Vector of (approximate) residuals

    Returns:¶

    backcast – Value to use in backcasting in the volatility recursion

    Return type:¶

    float

    © Copyright 2021, Kevin Sheppard.
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