stochvol

R package stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models. Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods.

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References in zbMATH (referenced in 25 articles , 1 standard article )

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  1. Barata, Raquel; Prado, Raquel; Sansó, Bruno: Fast inference for time-varying quantiles via flexible dynamic models with application to the characterization of atmospheric rivers (2022)
  2. Hosszejni, D.; Kastner, G: Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol (2021) not zbMATH
  3. Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen: Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021) not zbMATH
  4. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  5. Nordhausen, Klaus; Fischer, Gregor; Filzmoser, Peter: Blind source separation for compositional time series (2021)
  6. Ankargren, Sebastian; Unosson, Måns; Yang, Yukai: A flexible mixed-frequency vector autoregression with a steady-state prior (2020)
  7. Chavez, Gordon V.: Dynamic tail inference with log-Laplace volatility (2020)
  8. Kreuzer, Alexander; Czado, Claudia: Efficient Bayesian inference for nonlinear state space models with univariate autoregressive state equation (2020)
  9. Zens, Gregor; Böck, Maximilian; Zörner, Thomas O.: The heterogeneous impact of monetary policy on the US labor market (2020)
  10. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  11. Bitto, Angela; Frühwirth-Schnatter, Sylvia: Achieving shrinkage in a time-varying parameter model framework (2019)
  12. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  13. Hosszejni, Darjus; Kastner, Gregor: Approaches toward the Bayesian estimation of the stochastic volatility model with leverage (2019)
  14. Kastner, Gregor: Sparse Bayesian time-varying covariance estimation in many dimensions (2019)
  15. Bhattacharya, Arnab; Wilson, Simon P.: Sequential Bayesian inference for static parameters in dynamic state space models (2018)
  16. Chaim, Pedro; Laurini, Márcio P.: Volatility and return jumps in Bitcoin (2018)
  17. Chan, Joshua C. C.: Specification tests for time-varying parameter models with stochastic volatility (2018)
  18. De Luigi, Clara; Huber, Florian: Debt regimes and the effectiveness of monetary policy (2018)
  19. Hotz-Behofsits, Christian; Huber, Florian; Zörner, Thomas Otto: Predicting crypto-currencies using sparse non-Gaussian state space models (2018)
  20. Meng, Xiao-Li: Conducting highly principled data science: a statistician’s job and joy (2018)

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