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Displays quantiles of the posterior distribution of the volatilities over time as well as predictive distributions of future volatilities.
● Data Source:
CranContrib
● Keywords: hplot, ts
● Alias: volplot
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Displays a plot of iterations vs. sampled values the parameters mu, phi, sigma (and potentially nu), with a separate plot per variable.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: paratraceplot
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Creates or updates a summary of an svdraws object.
● Data Source:
CranContrib
● Keywords: utilities
● Alias: updatesummary
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svsample simulates from the joint posterior distribution of the SV parameters mu, phi, sigma (and potentially nu), along with the latent log-volatilities h_0,...,h_n and returns the MCMC draws. If a design matrix is provided, simple Bayesian regression can also be conducted.
● Data Source:
CranContrib
● Keywords: models, ts
● Alias: svsample
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svsim
(Package: stochvol) :
Simulating a Stochastic Volatility Process
svsim is used to produce realizations of a stochastic volatility (SV) process.
● Data Source:
CranContrib
● Keywords: datagen, ts
● Alias: svsim
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plot.svdraws generates some plots visualizing the posterior distribution and can also be used to display predictive distributions of future volatilities.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: plot.svdraws
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Displays a plot of the density estimate for the posterior distribution of the parameters mu, phi, sigma (and potentially nu), computed by the density function.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: paradensplot
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Small utlity function returning either diff(log(x)) in case the argument demean is set to FALSE, or diff(log(x)) - mean(diff(log(x))) in case that demean is TRUE.
● Data Source:
CranContrib
● Keywords: utilities
● Alias: logret
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svsample2 is a minimal overhead version of svsample with slightly different default arguments and a simplified return value structure. It is intended to be used mainly for one-step updates where speed is an issue, e.g., as a plug-in into other MCMC samplers. Note that absolutely no input checking is performed, thus this function is to be used with proper care!
● Data Source:
CranContrib
● Keywords: models, ts
● Alias: .svsample, svsample2
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This package provides an efficient algorithm for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods. Algorithmic details can be found in Kastner and Frühwirth-Schnatter (2014).
● Data Source:
CranContrib
● Keywords: models, package, ts
● Alias: stochvol, stochvol-package
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providing 
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