Bayesian Analysis of Stochastic Volatility Models
Datos Bibliográficos
| ID | 19420217 |
|---|---|
| Autores | Eric Jacquier (Cornell University), Nicholas G Polson (University of Chicago), Peter E Rossi (University of Chicago) |
| Año | 1994 |
| Volumen | 12 |
| Número | 4 |
| Páginas | 371-389 |
| Fecha de publicación | 1994-10-01 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Business and Economic Statistics (JOURNAL) |
| Identificadores de la revista | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1994.10524553 |
| OpenAlex | W4251281949 |
| Idioma | EN |
| Citas recibidas | 28 |
| Referencias citadas | 23 |
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches
Autoregressive model · Bayes factor · Bayes' theorem · Bayesian probability · Econometrics · Estimator · Markov chain · Markov chain Monte Carlo · Statistics · Stochastic volatility · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Statistical Methods and Inference · Stochastic processes and financial applications
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| Obras citantes distintas | 28 |
|---|---|
| Citas por año | 0,9 |
| Intervalo de citas | 1995 - 2026 (32) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 28 |