Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert Freitas Lopes
arXiv 26 Feb 2016 · Statistics — Computation
arXiv:1602.08154 · PDF · DOI · OpenAlex · Extracted main text
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for 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 | Chib, S., F. Nardari, and N. Shephard (2006) Analysis of high dimensional multivariate stochastic volatility models | 1.000 | 5 | 3 | 100% |
| 2 | Yu, Y. and X.-L. Meng (2011) To center or not to center: that is not the question–-an ancillarity-suffiency interweaving strategy (ASIS) for boosting MCMC ef… | 1.000 | 5 | 3 | 100% |
| 3 | Kastner, G. and S. Frühwirth-Schnatter (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models self | 0.909 | 8 | 3 | 75% |
| 4 | Frühwirth-Schnatter, S. and H. F. Lopes (2017) Parsimonious Bayesian factor analysis when the number of factors is unknown | 0.843 | 3 | 3 | 100% |
| 5 | Kim, S., N. Shephard, and S. Chib (1998) Stochastic volatility: Likelihood inference and comparison with ARCH models | 0.737 | 3 | 3 | 67% |
| 6 | Aguilar, O. and M. West (2000) Bayesian dynamic factor models and portfolio allocation | 0.737 | 3 | 2 | 100% |
| 7 | Han, Y (2006) Asset allocation with a high dimensional latent factor stochastic volatility model | 0.737 | 3 | 2 | 100% |
| 8 | Zhou, X., J. Nakajima, and M. West (2014) Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models | 0.644 | 2 | 2 | 100% |
| 9 | Jacquier, E., N. G. Polson, and P. E. Rossi (1994) Bayesian analysis of stochastic volatility models | 0.511 | 2 | 2 | 50% |
| 10 | Kastner, G (2016) Dealing with stochastic volatility in time series using the R package stochvol self | 0.511 | 2 | 2 | 50% |
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