EGARCH Parameters
Fits an EGARCH(1, 1) model to the historical returns and returns the estimated Omega, Alpha, Gamma and Beta parameters for each asset.
A negative Gamma indicates the presence of a leverage effect (negative shocks raise volatility by more than positive ones of the same size).
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: EGARCH weights, EGARCH coefficients, leverage parameters.
EGARCH Parameters in Python
get_egarch_parameters is part of the Risk module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_egarch_parameters as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.risk.get_egarch_parameters(period="quarterly")
Which returns:
| AMZN | TSLA | Benchmark | |
|---|---|---|---|
| Omega | -3.5971 | -1.7701 | -4.6755 |
| Alpha | -0.0115 | -0.0296 | 0.485 |
| Gamma | 0.0286 | 0.3887 | -0.4054 |
| Beta | 0.0196 | 0.0844 | 0.0651 |
Parameters
get_egarch_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 initialized 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.