Beta Skew-t GARCH models¶
Introduction¶
Beta Skew-t EGARCH models were proposed by Harvey and Chakravarty (2008). They extend on GARCH models through the use of a Skew-t conditional score to drive the conditional variance. This formulation allows for increased robustness to outliers. The basic formulation follows that of a Beta-t-EGARCH model. The Skew-t distribution employed originates from Fernandez and Steel (1998).
The \(\gamma\) latent variable represents the degree of skewness; for \(\gamma=1\), there is no skewness, for \(\gamma>1\) there is positive skewness, and for \(\gamma<1\) there is negative skewness.
Developer Note¶
- This model type has yet to be Cythonized so performance can be slow.
Example¶
First let us load some financial time series data from Yahoo Finance:
import numpy as np
import pyflux as pf
import pandas as pd
from pandas_datareader import DataReader
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline
jpm = DataReader('JPM', 'yahoo', datetime(2006,1,1), datetime(2016,3,10))
returns = pd.DataFrame(np.diff(np.log(jpm['Adj Close'].values)))
returns.index = jpm.index.values[1:jpm.index.values.shape[0]]
returns.columns = ['JPM Returns']
plt.figure(figsize=(15,5));
plt.plot(returns.index,returns);
plt.ylabel('Returns');
plt.title('JPM Returns');
One way to visualize the underlying volatility of the series is to plot the absolute returns \(\mid{y}\mid\):
plt.figure(figsize=(15,5))
plt.plot(returns.index, np.abs(returns))
plt.ylabel('Absolute Returns')
plt.title('JP Morgan Absolute Returns');
There appears to be some evidence of volatility clustering over this period. Let’s fit a \(Beta\) \(Skew-t\) \(EGARCH(1,1)\) model using a point mass estimate \(z^{MLE}\):
skewt_model = pf.SEGARCH(p=1, q=1, data=returns, target='JPM Returns')
x = skewt_model.fit()
x.summary()
SEGARCH(1,1)
======================================== =================================================
Dependent Variable: JPM Returns Method: MLE
Start Date: 2006-01-05 00:00:00 Log Likelihood: 6664.2692
End Date: 2016-03-10 00:00:00 AIC: -13316.5384
Number of observations: 2562 BIC: -13281.4472
==========================================================================================
Latent Variable Estimate Std Error z P>|z| 95% C.I.
========================= ========== ========== ======== ======== ========================
Vol Constant -0.0586 0.0249 -2.3589 0.0183 (-0.1073 | -0.0099)
p(1) 0.9932
q(1) 0.104
Skewness 0.9858
v 6.0465
Returns Constant 0.0015 0.0057 0.271 0.7864 (-0.0096 | 0.0127)
==========================================================================================
The standard errors are not shown for transformed variables. You can pass through a transformed=False argument to summary to obtain this information for untransformed variables.
We can plot the skewness latent variable \(\gamma\) with plot_z():
skewt_model.plot_z([3],figsize=(15,5))
So the series is slightly negatively skewed – which is consistent with the direction of skewness for most financial time series. We can plot the fit with plot_fit():
skewt_model.plot_fit(figsize=(15,5))
And plot predictions of future conditional volatility with plot_predict():
model.plot_predict(h=10)
If we had wanted predictions in dataframe form, we could have used predict(): instead.
We can also estimate a Beta-t-EGARCH model with leverage through add_leverage():
skewt_model = pf.SEGARCH(p=1,q=1,data=returns,target='JPM Returns')
skewt_model.add_leverage()
x = skewt_model.fit()
x.summary()
SEGARCH(1,1)
======================================== =================================================
Dependent Variable: JPM Returns Method: MLE
Start Date: 2006-01-05 00:00:00 Log Likelihood: 6684.9381
End Date: 2016-03-10 00:00:00 AIC: -13355.8762
Number of observations: 2562 BIC: -13314.9364
==========================================================================================
Latent Variable Estimate Std Error z P>|z| 95% C.I.
========================= ========== ========== ======== ======== ========================
Vol Constant -0.1203 0.0152 -7.898 0.0 (-0.1501 | -0.0904)
p(1) 0.9857
q(1) 0.1097
Leverage Term 0.0713 0.0095 7.5284 0.0 (0.0527 | 0.0899)
Skewness 0.9984
v 5.9741
Returns Constant 0.0004 0.0001 6.9425 0.0 (0.0003 | 0.0006)
==========================================================================================
We have a small leverage effect for the time series. We can plot the fit:
skewt_model.plot_fit(figsize=(15,5))
And we can plot ahead with the new model:
skewt_model.plot_predict(h=30,figsize=(15,5))
Class Description¶
-
class
SEGARCH(data, p, q, target)¶ Beta Skew-t EGARCH Models
Parameter Type Description data pd.DataFrame or np.ndarray Contains the univariate time series p int The number of autoregressive lags \(\sigma^{2}\) q int The number of ARCH terms \(\epsilon^{2}\) target string or int Which column of DataFrame/array to use. Attributes
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latent_variables¶ A pf.LatentVariables() object containing information on the model latent variables, prior settings. any fitted values, starting values, and other latent variable information. When a model is fitted, this is where the latent variables are updated/stored. Please see the documentation on Latent Variables for information on attributes within this object, as well as methods for accessing the latent variable information.
Methods
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add_leverage()¶ Adds a leverage term to the model, meaning volatility can respond differently to the sign of the news; see Harvey and Succarrat (2013). Conditional volatility will now follow:
\[\lambda_{t\mid{t-1}} = \alpha_{0} + \sum^{p}_{i=1}\alpha_{i}\lambda_{t-i} + \sum^{q}_{j=1}\beta_{j}u_{t-j} + \kappa\left(\text{sgn}\left(-\epsilon_{t-1}\right)(u_{t-1}+1)\right)\]
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adjust_prior(index, prior)¶ Adjusts the priors for the model latent variables. The latent variables and their indices can be viewed by printing the
latent_variablesattribute attached to the model instance.Parameter Type Description index int Index of the latent variable to change prior pf.Family instance Prior distribution, e.g. pf.Normal()Returns: void - changes the model
latent_variablesattribute
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fit(method, **kwargs)¶ Estimates latent variables for the model. User chooses an inference option and the method returns a results object, as well as updating the model’s
latent_variablesattribute.Parameter Type Description method str Inference option: e.g. ‘M-H’ or ‘MLE’ See Bayesian Inference and Classical Inference sections of the documentation for the full list of inference options. Optional parameters can be entered that are relevant to the particular mode of inference chosen.
Returns: pf.Results instance with information for the estimated latent variables
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plot_fit(**kwargs)¶ Plots the fit of the model against the data. Optional arguments include figsize, the dimensions of the figure to plot.
Returns : void - shows a matplotlib plot
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plot_ppc(T, nsims)¶ Plots a histogram for a posterior predictive check with a discrepancy measure of the user’s choosing. This method only works if you have fitted using Bayesian inference.
Parameter Type Description T function Discrepancy, e.g. np.meanornp.maxnsims int How many simulations for the PPC Returns: void - shows a matplotlib plot
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plot_predict(h, past_values, intervals, **kwargs)¶ Plots predictions of the model, along with intervals.
Parameter Type Description h int How many steps to forecast ahead past_values int How many past datapoints to plot intervals boolean Whether to plot intervals or not Optional arguments include figsize - the dimensions of the figure to plot. Please note that if you use Maximum Likelihood or Variational Inference, the intervals shown will not reflect latent variable uncertainty. Only Metropolis-Hastings will give you fully Bayesian prediction intervals. Bayesian intervals with variational inference are not shown because of the limitation of mean-field inference in not accounting for posterior correlations.
Returns : void - shows a matplotlib plot
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plot_predict_is(h, fit_once, fit_method, **kwargs)¶ Plots in-sample rolling predictions for the model. This means that the user pretends a last subsection of data is out-of-sample, and forecasts after each period and assesses how well they did. The user can choose whether to fit parameters once at the beginning or every time step.
Parameter Type Description h int How many previous timesteps to use fit_once boolean Whether to fit once, or every timestep fit_method str Which inference option, e.g. ‘MLE’ Optional arguments include figsize - the dimensions of the figure to plot. h is an int of how many previous steps to simulate performance on.
Returns : void - shows a matplotlib plot
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plot_sample(nsims, plot_data=True)¶ Plots samples from the posterior predictive density of the model. This method only works if you fitted the model using Bayesian inference.
Parameter Type Description nsims int How many samples to draw plot_data boolean Whether to plot the real data as well Returns : void - shows a matplotlib plot
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plot_z(indices, figsize)¶ Returns a plot of the latent variables and their associated uncertainty.
Parameter Type Description indices int or list Which latent variable indices to plot figsize tuple Size of the matplotlib figure Returns : void - shows a matplotlib plot
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ppc(T, nsims)¶ Returns a p-value for a posterior predictive check. This method only works if you have fitted using Bayesian inference.
Parameter Type Description T function Discrepancy, e.g. np.meanornp.maxnsims int How many simulations for the PPC Returns: int - the p-value for the discrepancy test
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predict(h, intervals=False)¶ Returns a DataFrame of model predictions.
Parameter Type Description h int How many steps to forecast ahead intervals boolean Whether to return prediction intervals Please note that if you use Maximum Likelihood or Variational Inference, the intervals shown will not reflect latent variable uncertainty. Only Metropolis-Hastings will give you fully Bayesian prediction intervals. Bayesian intervals with variational inference are not shown because of the limitation of mean-field inference in not accounting for posterior correlations.
Returns : pd.DataFrame - the model predictions
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predict_is(h, fit_once, fit_method)¶ Returns DataFrame of in-sample rolling predictions for the model.
Parameter Type Description h int How many previous timesteps to use fit_once boolean Whether to fit once, or every timestep fit_method str Which inference option, e.g. ‘MLE’ Returns : pd.DataFrame - the model predictions
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sample(nsims)¶ Returns np.ndarray of draws of the data from the posterior predictive density. This method only works if you have fitted the model using Bayesian inference.
Parameter Type Description nsims int How many posterior draws to take Returns : np.ndarray - samples from the posterior predictive density.
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References¶
Black, F. (1976) Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the American Statistical Association. pp. 171–181.
Fernandez, C., & Steel, M. F. J. (1998a). On Bayesian Modeling of Fat Tails and Skewness. Journal of the American Statistical Association, 93, 359–371.
Harvey, A.C. & Chakravarty, T. (2008) Beta-t-(E)GARCH. Cambridge Working Papers in Economics 0840, Faculty of Economics, University of Cambridge, 2008. [p137]
Harvey, A.C. & Sucarrat, G. (2013) EGARCH models with fat tails, skewness and leverage. Computational Statistics and Data Analysis, Forthcoming, 2013. URL http://dx.doi.org/10.1016/j.csda.2013.09. 022. [p138, 139, 140, 143]
Nelson, D. B. (1991), ‘Conditional heteroskedasticity in asset returns: A new approach’, Econometrica 59, 347—370.