VAR Forecast
Fit a Vector Autoregression (VAR) across every ticker in the Toolkit instance and forecast forecast_steps periods ahead.
Also known as: VAR model, vector autoregressive model.
A VAR jointly models every ticker’s series, regressing each of them on lags lagged values of ALL of them (including itself) – see time_series_model.get_var_forecast for the full formula and estimation method (equation-by-equation OLS, reusing regression_model.get_ols).
VAR Forecast in Python
get_var_forecast is part of the Econometrics module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_var_forecast as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.econometrics.get_var_forecast(period="quarterly")
Which returns:
| Step | AAPL | MSFT |
|---|---|---|
| 1 | 0.1271 | 0.1046 |
| 2 | 0.1069 | 0.0640 |
| 3 | 0.0704 | 0.0428 |
| 4 | 0.0661 | 0.0454 |
| 5 | 0.0716 | 0.0496 |
Parameters
get_var_forecast accepts the following parameters:
- period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
- column (str, optional): The historical data column to model. Defaults to “Return”.
- lags (int, optional): The VAR order. Defaults to 1.
- forecast_steps (int, optional): The number of periods ahead to forecast. Defaults to 5.
- include_benchmark (bool, optional): Whether to include “Benchmark” among the tickers modeled jointly. Defaults to False.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.