BEKK results¶
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class
bekk.bekk_results.BEKKResults(innov=None, hvar=None, var_target=None, model=None, use_target=None, restriction=None, cfree=None, method=None, cython=None, time_delta=None, param_start=None, param_final=None, opt_out=None)[source]¶ Estimation results.
Attributes
innov Return innovations hvar Filtered variance matrices var_target Estimated varinace target param_start Starting parameters param_final Estimated parameters model Specific model to estimate restriction Restriction on parameters use_target Variance targeting flag method Optimization method. See scipy.optimize.minimize cython Whether to use Cython optimizations (True) or not (False) Methods
loss_var_ratio([kind])Ratio of realized and predicted variance. portf_evar([kind])Portfolio predicted variance. portf_mvar([kind])Portfolio mean variance. portf_rvar([kind])Portfolio predicted variance. weights([kind])Portfolio weights. weights_equal()Equal weights. weights_minvar()Minimum variance weights. -
loss_var_ratio(kind='equal')[source]¶ Ratio of realized and predicted variance.
Parameters: kind : str
Either ‘equal’ or ‘minvar’ (minimum variance).
Returns: (nobs, ) array
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portf_evar(kind='equal')[source]¶ Portfolio predicted variance.
Parameters: kind : str
Either ‘equal’ or ‘minvar’ (minimum variance).
Returns: (nobs, ) array
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portf_mvar(kind='equal')[source]¶ Portfolio mean variance.
Parameters: kind : str
Either ‘equal’ or ‘minvar’ (minimum variance).
Returns: float
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portf_rvar(kind='equal')[source]¶ Portfolio predicted variance.
Parameters: kind : str
Either ‘equal’ or ‘minvar’ (minimum variance).
Returns: (nobs, ) array
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