Darjus Hosszejni, Gregor Kastner
arXiv 31 Jan 2019 · Statistics — Computation · publishedSpringer proceedings in mathematics & statistics (2019) · 13 citations (OpenAlex)
arXiv:1901.11491 · PDF · DOI · OpenAlex · Extracted main text
The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. We derive novel algorithms for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage, where the return process and innovations of the volatility process are allowed to correlate. Moreover, based on the idea of ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers in order to guarantee stable sampling efficiency irrespective of the baseline parameterization.We carry out an extensive comparison to already existing sampling methods for this model using simulated as well as real world data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gregor Kastner and Sylvia Frühwirth-Schnatter (2013) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models self | 1.000 | 5 | 3 | 100% |
| 2 | Yasuhiro Omori, Siddhartha Chib, Neil Shephard, and Jouchi Nakajima (2006) Stochastic volatility with leverage: Fast and efficient likelihood inference | 0.811 | 4 | 2 | 100% |
| 3 | Martyn Plummer (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling | 0.511 | 2 | 1 | 100% |
| 4 | Bob Carpenter, Andrew Gelman, Matthew Hoffman, Daniel Lee, Ben Goodr… (2017) Stan: A probabilistic programming language | 0.511 | 2 | 1 | 100% |
| 5 | Eric Jacquier, Nicholas G. Polson, and Peter E. Rossi (2003) Bayesian analysis of stochastic volatility models with fat-tails and correlated errors | 0.405 | 1 | 1 | 100% |
| 6 | Jun S. Liu (1994) The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem | 0.405 | 1 | 1 | 100% |
| 7 | William J. McCausland, Shirley Miller, and Denis Pelletier (2010) Simulation smoothing for state-space models: A computational efficiency analysis | 0.405 | 1 | 1 | 100% |
| 8 | Jouchi Nakajima and Yasuhiro Omori (2008) Leverage, heavy-tails and correlated jumps in stochastic volatility models | 0.405 | 1 | 1 | 100% |
| 9 | Yaming Yu and Xiao-Li Meng (2011) To center or not to center: That is not the question–-An ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC… | 0.405 | 1 | 1 | 100% |
| 10 | Chris K Carter and Robert Kohn (1994) On Gibbs sampling for state space models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.