Fits a GJR-GARCH(1, 1, 1) model to the historical returns and returns the estimated Omega, Alpha, Gamma and Beta parameters for each asset.

A positive Gamma indicates the presence of a leverage effect (negative shocks raise volatility by more than positive ones of the same size), which symmetric GARCH cannot represent.

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: GJR-GARCH weights, GJR-GARCH coefficients, leverage parameters.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the GJR-GARCH Parameters for you. Just ask in plain English.

Calculate the GJR-GARCH Parameters in Python

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

pip install financetoolkit -U

Then call get_gjr_garch_parameters as shown below.

from financetoolkit import Toolkit

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

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

Which returns:

  AMZN TSLA Benchmark
Omega 0.0074 0.138 0.0045
Alpha 0 0.1492 0.0699
Gamma 0.0428 -0.0828 1
Beta 0.7156 0.0711 0

Parameters

get_gjr_garch_parameters 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”.
  • optimization_t (int, optional): Time steps of the returns series to use for the optimization. Defaults to the full length of the returns series.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
CoVaR Value at Risk Backtest Acerbi-Szekely Test GARCH Parameters GJR-GARCH Volatility Model GJR-GARCH Volatility Forecast EGARCH Volatility Model EGARCH Volatility Forecast EGARCH Parameters HAR-RV Forecast Hill Estimator Best Fitting Copula

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

Share