Quantile Regression

Fit a Quantile Regression of dependent_ticker on independent_tickers at quantile tau.

Also known as: QR.

For more information about the method, see regression_model.get_quantile_regression.

Quantile Regression in Python

Install the Finance Toolkit with:

pip install financetoolkit -U

Then call get_quantile_regression as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

# AAPL (the first ticker) is dependent; MSFT and Benchmark are independent
toolkit.econometrics.get_quantile_regression(
    independent_tickers=["MSFT", "Benchmark"], tau=0.5, period="weekly"
)

Which returns:

Parameters

The function accepts the following parameters:

• dependent_ticker (str | None, optional): The dependent (predicted) asset. Defaults to None, meaning the Toolkit instance’s first ticker.
• independent_tickers (str | list[str] | None, optional): The independent (predictor) asset(s). Defaults to None, meaning every other ticker in the Toolkit instance besides dependent_ticker.
• include_benchmark (bool, optional): Whether to include “Benchmark” in the default independent ticker(s) (has no effect when independent_tickers is given explicitly). Defaults to False.
• tau (float, optional): The quantile to fit, in (0, 1). Defaults to 0.5 (the median).
• period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
• column (str, optional): The historical data column to regress on. Defaults to “Return”.
• add_constant (bool, optional): Whether to include an intercept. Defaults to True.
• n_bootstrap (int, optional): The number of bootstrap resamples used for coefficient standard errors, overriding statsmodels’ default analytic (kernel density-based) standard errors. Defaults to 0 (use the analytic standard errors).
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Regression