Creates a class used for vol forecasting and covariance matrix estimation NOTE: this method has a tendency to make decrease the volatility
simple_returns (QFDataFrame) – QFDataFrame of simple returns of the assets
calculate_covariance(vol_process, horizon, …)
Calculates the covariance matrix using foretasted volatility for each asset and rank correlation between the assets.
calculate_covariance(vol_process: arch.univariate.volatility.VolatilityProcess, horizon: int, method: str = 'analytic') → qf_lib.containers.dataframe.qf_dataframe.QFDataFrame[source]¶
Calculates the covariance matrix using foretasted volatility for each asset and rank correlation between the assets. Covariance matrix contains NOT annualised values. They are expressed in the frequency of the input returns
vol_process (VolatilityProcess) – volatility proces used for forecats calculation. For example EGARCH(p=p, o=o, q=q)
horizon (int) – horizon for the volatility forecast. It is expressed in the frequency of the returns provided
(optional) (method) – method of the volatility forecast calculation. Possible: ‘analytic’, ‘simulation’ or ‘bootstrap’ ‘analytic’ is the fastest but is not available for EGARCH and possibly some other models. For details check arch documentation
cov_matrix of the assets.
- Return type