Calculates volatility forecasts based on the EGARCH model.

EGARCH models the log of the conditional variance, which avoids having to constrain the parameters to keep the variance positive and, like GJR-GARCH, lets negative and positive shocks of the same size have a different impact on volatility (the leverage effect).

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

  • Nelson, D.B. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach.” Econometrica, 59(2), 347-370.

Also known as: exponential GARCH, log-GARCH.

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Calculate the EGARCH Volatility Model in Python

The EGARCH Volatility Model is available in the Risk module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_egarch as shown below.

from financetoolkit import Toolkit

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

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

Which returns:

Date AMZN TSLA Benchmark
2024Q2 0.0263 0.1078 0.005
2024Q3 0.0259 0.1658 0.0047
2024Q4 0.0255 0.1991 0.0048
2025Q1 0.0262 0.2357 0.0046
2025Q2 0.0249 0.1134 0.0078
2025Q3 0.0262 0.1846 0.0051
2025Q4 0.0257 0.2112 0.0049
2026Q1 0.0259 0.1543 0.0046
2026Q2 0.0251 0.1238 0.0079
2026Q3 0.0261 0.1672 0.0053

Parameters

get_egarch 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 EGARCH for.
  • optimization_t (int, optional): Time steps to optimize EGARCH for. It is only used if no weights are given.
  • within_period (bool, optional): Whether to calculate EGARCH within the specified period or for the entire period. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the EGARCH 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.
Value at Risk Backtest Acerbi-Szekely Test GARCH Parameters GJR-GARCH Volatility Model GJR-GARCH Volatility Forecast GJR-GARCH Parameters EGARCH Volatility Forecast EGARCH Parameters HAR-RV Forecast Hill Estimator Best Fitting Copula Copula Parameters

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

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