Calculate the (Delta-)CoVaR of ticker conditional on conditioning_ticker being in its own distress state.

Ordinary Value at Risk treats each asset in isolation, which misses systemic risk – the fact that one asset’s distress can spill over and worsen another’s risk. CoVaR directly measures that spillover: it is the VaR of ticker, conditional on conditioning_ticker itself being at its own alpha-VaR, estimated via a linear Quantile Regression of ticker’s returns on conditioning_ticker’s returns at quantile alpha. The Delta-CoVaR isolates the marginal, distress-specific contribution by subtracting the same construction evaluated in the “normal” (median) state instead.

For more information about the method, see the following paper:

  • Adrian, T., & Brunnermeier, M.K. (2016). “CoVaR.” American Economic Review, 106(7), 1705-1741.

Also known as: Conditional Value at Risk (systemic risk sense), Delta-CoVaR.

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Calculate the CoVaR in Python

The CoVaR is available in the Risk module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_covar as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_covar("AAPL", "MSFT", period="weekly")

Which returns:

Metric Value
CoVaR -0.1077
Delta-CoVaR -0.1134
Quantile Regression Slope 0.9214
Quantile Regression Intercept -0.0508
Observations 157

Parameters

get_covar accepts the following parameters:

  • ticker (str): The asset whose conditional VaR is being measured.
  • conditioning_ticker (str): The asset (or e.g. a benchmark/index) whose distress ticker is conditioned on.
  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”, since a dependence estimate needs far more observations than a lower frequency provides – at “yearly” a decade of history is only ten observations.
  • column (str, optional): The historical data column to use. Defaults to “Return”.
  • alpha (float, optional): The confidence level for both the tail quantile regression and the VaR of conditioning_ticker (e.g., 0.05 for 95% confidence). Defaults to 0.05.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Coefficient of Variation (CV) EWMA Volatility Autocorrelation (ACF) Hurst Exponent Marginal Value at Risk (mVaR) Component Value at Risk (cVaR) Value at Risk Backtest Acerbi-Szekely Test GARCH Parameters GJR-GARCH Volatility Model GJR-GARCH Volatility Forecast GJR-GARCH Parameters

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

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