Beta-t-EGARCH-in-mean regression 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
data = # some dataframe with financial time series
model = pf.EGARCHMReg(formula="returns~x+z", data=data, p=1, q=1)
# here returns is the return series; x and z are exogenous variables to include
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Class Arguments¶
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class
EGARCHMReg
(data, formula, p, q)¶ -
data
¶ pd.DataFrame : the time-series data for the regression
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formula
¶ str: patsy notation specifying the regresion; the LHS variable should be the return series which the volatility model will be estimated on; the RHS variabls are predictors that will be used in the returns and volatility equations.
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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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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, oos_data, **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. oos_data is a DataFrame with the same structure as the original data, but for the out-of-sample period.
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, oos_data)¶ Returns DataFrame of model predictions. h is an int of how many steps ahead to predict. oos_data is a DataFrame with the same structure as the original data, but for the out-of-sample period.
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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.