Beta-t-EGARCH-in-mean regression models

Example

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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

Class Arguments

class EGARCHMReg(data, formula, p, q)
data

pd.DataFrame : the time-series data for the regression

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.

p

int : the number of GARCH terms

q

int : the number of score terms

Class Methods

add_leverage()

Adds a leverage term to the model to account for the asymmetric effect of new information.

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():

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import pyflux as pf

# model = ... (specify a model)
model.list_priors()
model.adjust_prior(2, pf.Normal(0,1))
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():

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import pyflux as pf

# model = ... (specify a model)
model.fit("M-H", nsims=20000)
plot_fit(**kwargs)

Graphs the fit of the model.

Optional arguments include figsize - the dimensions of the figure to plot.

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.

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.

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.

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.

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.