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Heavy-Tailed Innovations in the R Package stochvol

Kastner, Gregor (2015) Heavy-Tailed Innovations in the R Package stochvol. WU Vienna University of Economics and Business, Vienna.

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We document how sampling from a conditional Student's t distribution is implemented in stochvol. Moreover, a simple example using EUR/CHF exchange rates illustrates how to use the augmented sampler. We conclude with results and implications. (author's abstract)

Item Type: Paper
Keywords: Student's t distribution / data augmentation / EUR/CHF exchange rates
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Gertraud Novotny
Date Deposited: 08 Mar 2016 13:57
Last Modified: 08 Mar 2016 15:44
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/71932/
URI: http://epub.wu.ac.at/id/eprint/4918


Chib S, Greenberg E (1994). "Bayes Inference in Regression Models with ARMA(p; q) Errors." Journal of Econometrics, 64, 183-206. doi:10.1016/0304-4076(94)90063-9.

Chib S, Nardari F, Shephard N (2002). "Markov Chain Monte Carlo Methods for Stochastic Volatility Models." Journal of Econometrics, 108, 281-316. doi:10.1016/S0304-4076(01)00137-3.

Delatola EI, Griffin JE (2011). "Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility." 6, 901-926. doi:10.1214/11-BA632.

Dovern J, Feldkircher M, Huber F (2015). "Does Joint Modeling of the World Economy Pay Off? Evaluating GVAR Forecasts from a Multivariate Perspective." Discussion Paper Series 590, University of Heidelberg, Department of Economics. URL http://www.ub.uni-heidelberg.de/archiv/18586.

Frühwirth-Schnatter S, Pyne S (2010). "Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions." Biostatistics, 11(2), 317-336. doi:10.1093/biostatistics/kxp062.

Harvey AC, Ruiz E, Shephard N (1994). "Multivariate Stochastic Variance Models." The Review of Economic Studies, 61(2), 247-264. doi:10.2307/2297980.

Huber F (2014). "Density Forecasting using Bayesian Global Vector Autoregressions with Common Stochastic Volatility." Department of Economics Working Paper Series 179, WU Vienna University of Economics and Business. URL http://epub.wu.ac.at/id/eprint/4280.

Jensen MJ, Maheu JM (2010). "Bayesian semiparametric stochastic volatility modeling." Journal of Econometrics, 157(2), 306-316. doi:10.1016/j.jeconom.2010.01.014.

Kastner G (2016a). "Dealing with Stochastic Volatility in Time Series Using the R Package stochvol." Journal of Statistical Software, 69(5), 1-30. doi:10.18637/jss.v069.i05.

Kastner G (2016b). stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models. R package version 1.2.3, URL http://CRAN.R-project.org/package=stochvol.

Kastner G, Frühwirth-Schnatter S (2014). "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models." Computational Statistics & Data Analysis, 76, 408-423. doi:10.1016/j.csda.2013.01.002.

Kastner G, Frühwirth-Schnatter S, Lopes HF (2014). "Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models." In E Lanzarone, F Ieva (eds.), The Contribution of Young Researchers to Bayesian Statistics { Proceedings of BAYSM2013, volume 63 of Springer Proceedings in Mathematics & Statistics, pp. 181-185. Springer- Verlag. doi:10.1007/978-3-319-02084-6_35.

Nakajima J, Omori Y (2012). "Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution." 56(11), 3690-3704. doi: 10.1016/j.csda.2010.07.012.

R Core Team (2016). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

Silva RS, Lopes HF, Migon HS (2006). "The Extended Generalized Inverse Gaussian Distribution for Log-Linear and Stochastic Volatility Models." Brazilian Journal of Probability and Statistics, 20(1), 67-91.


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