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).

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Calculate the VAR Forecast in Python

The VAR Forecast is available in 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.
ARIMA Forecast VECM Forecast Impulse Response Function (IRF) Variance Decomposition

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

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