EWMA Volatility
Calculate the exponentially weighted moving average (EWMA) Volatility of an investment portfolio or asset’s daily returns, following the RiskMetrics methodology.
Unlike a fixed-window rolling Volatility, EWMA Volatility weights recent observations more heavily than older ones, so it reacts faster to changes in the underlying volatility regime. It is a simpler, more interpretable alternative to a full GARCH fit.
The formula is as follows:
\[\text{EWMA Variance} (t) = \lambda \cdot \text{EWMA Variance} (t-1) + (1 - \lambda) \cdot \operatorname{Return}(t-1) ^{2}\]Also known as: RiskMetrics volatility, exponentially weighted volatility.
EWMA Volatility in Python
get_ewma_volatility is part of the Risk module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_ewma_volatility as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.risk.get_ewma_volatility()
Which returns:
| Date | AMZN | TSLA | Benchmark |
|---|---|---|---|
| 2026-06-22 | 0.0229 | 0.0279 | 0.0099 |
| 2026-06-23 | 0.0223 | 0.0304 | 0.0103 |
| 2026-06-24 | 0.0216 | 0.0296 | 0.01 |
| 2026-06-25 | 0.022 | 0.0287 | 0.0097 |
| 2026-06-26 | 0.0225 | 0.0281 | 0.0096 |
| 2026-06-29 | 0.0234 | 0.0345 | 0.0101 |
| 2026-06-30 | 0.0228 | 0.0338 | 0.01 |
| 2026-07-01 | 0.0224 | 0.0328 | 0.0097 |
| 2026-07-02 | 0.0218 | 0.037 | 0.0094 |
| 2026-07-06 | 0.0211 | 0.0395 | 0.0093 |
Parameters
get_ewma_volatility accepts the following parameters:
- lambda_ (float, optional): The decay factor. Higher values weight the past more heavily (slower to react), lower values weight recent returns more heavily (faster to react). RiskMetrics uses 0.94 for daily data. Defaults to 0.94.
- 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 EWMA Volatility 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.