Skew-t EGARCH models¶
Example¶
1 2 3 4 5 6 7 8 | import numpy as np
import pyflux as pf
from pandas.io.data import DataReader
from datetime import datetime
ibm = DataReader('IBM', 'yahoo', datetime(2000,1,1), datetime(2016,3,10))
ibm['Logged Open'] = np.log(ibm['Open'].values)
model = pf.SEGARCH(np.diff(ibm['Logged Open']), p=1, q=1)
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Class Arguments¶
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class
SEGARCH
(data, p, q, target)¶ -
data
¶ pd.DataFrame or array-like : the time-series data
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p
¶ int : the number of GARCH terms
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q
¶ int : the number of score terms
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target
¶ string (data is DataFrame) or int (data is np.array) : which column to use as the time series. If None, the first column will be chosen as the data.
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Class Methods¶
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add_leverage
()¶ Adds a leverage term to the model to account for the asymmetric effect of new information.
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adjust_prior
(index, prior)¶ Adjusts the priors of the model. index can be an int or a list. prior is a prior object, such as Normal(0,3).
Here is example usage for adjust_prior()
:
1 2 3 4 5 | import pyflux as pf
# model = ... (specify a model)
model.list_priors()
model.adjust_prior(2, pf.Normal(0,1))
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fit
(method, **kwargs)¶ Estimates latent variables for the model. Returns a Results object. method is an inference/estimation option; see Bayesian Inference and Classical Inference sections for options. If no method is provided then a default will be used.
Optional arguments are specific to the method you choose - see the documentation for these methods for more detail.
Here is example usage for fit()
:
1 2 3 4 | import pyflux as pf
# model = ... (specify a model)
model.fit("M-H", nsims=20000)
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plot_fit
(**kwargs)¶ Graphs the fit of the model.
Optional arguments include figsize - the dimensions of the figure to plot.
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plot_z
(indices, figsize)¶ Returns a plot of the latent variables and their associated uncertainty. indices is a list referring to the parameter indices that you want to plot. Figsize specifies how big the plot will be.
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plot_predict
(h, past_values, intervals, **kwargs)¶ Plots predictions of the model. h is an int of how many steps ahead to predict. past_values is an int of how many past values of the series to plot. intervals is a bool on whether to include confidence/credibility intervals or not.
Optional arguments include figsize - the dimensions of the figure to plot.
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plot_predict_is
(h, past_values, intervals, **kwargs)¶ Plots in-sample rolling predictions for the model. h is an int of how many previous steps to simulate performance on. past_values is an int of how many past values of the series to plot. intervals is a bool on whether to include confidence/credibility intervals or not.
Optional arguments include figsize - the dimensions of the figure to plot.
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predict
(h)¶ Returns DataFrame of model predictions. h is an int of how many steps ahead to predict.
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predict_is
(h)¶ Returns DataFrame of in-sample rolling predictions for the model. h is an int of how many previous steps to simulate performance on.