VolatilityResult#

class impulso.results.VolatilityResult(*, idata, series_name, index)[source]#

Bases: VARResultBase

Result from univariate SV fit — posterior of conditional SD.

Conditional SD is sigma_t = exp(h_t / 2), where h_t is the posterior log-volatility path.

Parameters:
idata#

InferenceData with ‘h’ in posterior.

series_name#

Name of the fitted series.

Type:

str

index#

DatetimeIndex aligned with the fitted series.

Type:

pandas.DatetimeIndex

hdi(prob=0.89)[source]#

Highest-density interval for the conditional SD path.

Parameters:

prob (float)

Return type:

HDIResult

median()[source]#

Posterior median of the conditional SD path.

Return type:

DataFrame

model_config = {'arbitrary_types_allowed': True, 'frozen': True}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

plot()[source]#

Plot the posterior volatility path with HDI bands.

Return type:

Figure

to_dataframe()[source]#

Conditional SD posterior median as a DataFrame.

Return type:

DataFrame