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Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
Open accessAbstract
Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show deficiencies for highly persistent latent variable series. The novel approach of ancillarity-sufficiency interweaving has recently been shown to aid in overcoming these issues for a broad class of multilevel models. It is demonstrated how such an interweaving strategy can be applied to stochastic volatility models in order to greatly improve sampling efficiency for all parameters and throughout the entire parameter range. Moreover, this method of ''combining best of different worlds'' allows for inference for parameter constellations that have previously been infeasible to estimate without the need to select a particular parameterization beforehand.
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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Anderson, E., Bai, Z., Bischof, C., Blackford, L. S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., et al. (1999) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719604 · Cited by 4,435
The Pricing of Options on Assets with Stochastic Volatilities
HULL, J., & WHITE, A. (1987) · The Journal of Finance · doi:10.1111/j.1540-6261.1987.tb02568.x · Cited by 3,807
CODA: convergence diagnosis and output analysis for MCMC
Plummer, M., Best, N., Cowles, K., & Vines, K. (2006) · Open Research Online (The Open University) · Cited by 3,317
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
Kim, S., Shepherd, N., & Chib, S. (1998) · Review of Economic Studies · doi:10.1111/1467-937x.00050 · Cited by 2,261
On Gibbs sampling for state space models
CARTER, C. K., & KOHN, R. (1994) · Biometrika · doi:10.1093/biomet/81.3.541 · Cited by 1,917
Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation
Gabriel, E., Fagg, G. E., Bosilca, G., Angskun, T., Dongarra, J. J., Squyres, J. M., Sahay, V., Kambadur, P., Barrett, B., Lumsdaine, A., et al. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30218-6_19 · Cited by 1,591
Bayesian Analysis of Stochastic Volatility Models
Jacquier, E., Polson, N. G., & Rossi, P. E. (2002) · Journal of Business & Economic Statistics · doi:10.1198/073500102753410408 · Cited by 1,362
Rcpp: Seamless R and C++Integration
Eddelbuettel, D., & François, R. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i08 · Cited by 1,318
DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
Frühwirth‐Schnatter, S. (1994) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1994.tb00184.x · Cited by 808
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Metadata
- Authors
- Kastner, G., & Frühwirth-Schnatter, S.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 76
- Pages
- 408-423
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2053974175
- Cited by
- 287
- References
- 29
- FWCI
- 12.62
- Type
- journal article
- Language
- en
- Open access
- green
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- Kastner, G., & Frühwirth-Schnatter, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics & Data Analysis. https://doi.org/10.1016/j.csda.2013.01.002
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