Calculates sigma_2 forecasts based on the GJR-GARCH model.

For more information about the method, see the following paper:

  • Glosten, L.R., Jagannathan, R., and Runkle, D.E. (1993). “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” The Journal of Finance, 48(5), 1779-1801.

Also known as: volatility forecast, predicted volatility.

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Calculate the GJR-GARCH Volatility Forecast in Python

The GJR-GARCH Volatility Forecast is available in the Risk module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_gjr_garch_forecast as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_gjr_garch_forecast(period="quarterly")

Which returns:

  AMZN TSLA Benchmark
2026Q4 0.0258 0.1716 0.0046
2027Q1 0.0258 0.1716 0.0046
2027Q2 0.0264 0.1687 0.0071
2027Q3 0.0268 0.1681 0.0086
2027Q4 0.0272 0.168 0.0094
2028Q1 0.0274 0.168 0.0099
2028Q2 0.0276 0.168 0.0102
2028Q3 0.0277 0.168 0.0103
2028Q4 0.0278 0.168 0.0104
2029Q1 0.0279 0.168 0.0105

Parameters

get_gjr_garch_forecast accepts the following parameters:

  • period (str, optional): The data frequency for returns (daily, weekly, monthly, quarterly, or yearly). Defaults to “quarterly” if the Toolkit is initialised with quarterly=True, otherwise “yearly”.
  • time_steps (int, optional): Time steps to calculate GJR-GARCH and to forecast sigma_2 values for.
  • within_period (bool, optional): Whether to calculate GJR-GARCH within each specified period or all at once. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
  • growth (bool, optional): Whether to calculate the growth of the GJR-GARCH values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
Component Value at Risk (cVaR) CoVaR Value at Risk Backtest Acerbi-Szekely Test GARCH Parameters GJR-GARCH Volatility Model GJR-GARCH Parameters EGARCH Volatility Model EGARCH Volatility Forecast EGARCH Parameters HAR-RV Forecast Hill Estimator

The Risk module page introduces the module, and the sidebar lists all of its functions.

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