factorstochvol (version 0.8.1)

factorstochvol-package: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models through MCMC

Description

This packages provides a Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loadings matrix.

Details

In recent years, multivariate factor stochastic volatility (SV) models have been increasingly used to analyze financial and economic time series because they can capture joint (co-)volatility dynamics by a small number of latent time-varying factors. The main advantage of such a model is its parsimony, as all variances and covariances of a time series vector are governed by a low-dimensional common factor with the components following independent SV models. For problems of this kind, MCMC is a very efficient estimation method, it is however associated with a considerable computational burden when the number of assets is moderate to large. To overcome this, the latent volatility states are drawn "all without a loop" (AWOL), ancillarity-sufficiency interweaving strategies (ASIS) are applied to sample the univariate components as well as the factor loadings. Thus, this package can be applied directly estimate time-varying covariance and correlation matrices for medium-and high-dimensional time series. To guarantee sparsity, a hierarchical Normal-Gamma prior can be used for the factor loadings matrix which shrinks the unnecessary factor loadings towards zero.

References

Kastner, G., Frühwirth-Schnatter, S., Lopes, H. F. (2016). Efficient Bayesian inference for multivariate factor stochastic volatility models. Report 128, Research Report Series of the Institute of Statistics and Mathematics, WU Vienna University of Economics and Business, http://epub.wu.ac.at/4875/

Kastner, G. (2016). Sparse Bayesian time-varying covariance estimation in many dimensions. Working Paper

Kastner, G. and Frühwirth-Schnatter, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics and Data Analysis, http://dx.doi.org/10.1016/j.csda.2013.01.002.

See Also

stochvol

Examples

## Not run: 
# set.seed(1)
# 
# # simulate data from a (small) factor SV model:
# sim <- fsvsim(series = 5, factors = 2)
# 
# # estimate the model (CAVEAT: only few draws!)
# res <- fsvsample(sim$y, factors = 2, draws = 2000,
#                  burnin = 500, runningstore = 6)
# 
# # plot implied volas overtime:
# voltimeplot(res)
# 
# # plot correlation matrix at some points in time:
# par(mfrow = c(2,2))
# corimageplot(res, seq(1, nrow(sim$y), length.out = 4),
#              fsvsimobj = sim, plotCI = 'circle',
#              plotdatedist = -2)
# 
# 
# # plot (certain) covariances and correlations over time
# par(mfrow = c(2,1))
# covtimeplot(res, 1)
# cortimeplot(res, 1)
# 
# # plot (all) correlations over time
# corplot(res, fsvsimobj = sim, these = 1:10)
# 
# # plot factor loadings
# par(mfrow = c(1,1))
# facloadpointplot(res, fsvsimobj = sim)
# facloadpairplot(res)
# facloadcredplot(res)
# facloaddensplot(res, fsvsimobj = sim)
# 
# # plot latent log variances
# logvartimeplot(res, fsvsimobj = sim, show = "fac")
# logvartimeplot(res, fsvsimobj = sim, show = "idi")
# 
# # plot communalities over time
# comtimeplot(res, fsvsimobj = sim, show = 'joint')
# comtimeplot(res, fsvsimobj = sim, show = 'series')
# ## End(Not run)