The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
Open accessAbstract
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.
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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Ripley, B. D. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01615.x · Cited by 2,511
Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M., & Calderhead, B. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2010.00765.x · Cited by 1,473
Forecasting Volatility in Financial Markets: A Review
Poon, S., & Granger, C. W. J. (2003) · Journal of Economic Literature · doi:10.1257/002205103765762743 · Cited by 1,315
The econometrics of financial markets
Pagan, A. (1996) · Journal of Empirical Finance · doi:10.1016/0927-5398(95)00020-8 · Cited by 873
Automatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv · doi:10.48550/arxiv.1603.00788 · Cited by 515
Metadata
- Authors
- Kim, S., Shepherd, N., & Chib, S.
- Venue
- Review of Economic Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-07
- Volume
- 65
- Issue
- 3
- Pages
- 361-393
- ISSN
- 0034-6527 (ISSN-L)
- 1467-937X
- OpenAlex
- W2079672223
- Cited by
- 2,261
- References
- 4
- FWCI
- 75.38
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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