Stochastic volatility: likelihood inference and comparison with ARCH models.
Stochastic volatility models present a natural way of working with time-varying volatility. However the difficulty involved in estimating these types of models has prevented their wide-spread use in empirical applications. In this paper we exploit Gibbs sampling to provide a likelihood framework for...
| Những tác giả chính: | , |
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| Định dạng: | Working paper |
| Ngôn ngữ: | English |
| Được phát hành: |
Nuffield College (University of Oxford)
1994
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| _version_ | 1826278742912139264 |
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| author | Kim, S Shephard, N |
| author_facet | Kim, S Shephard, N |
| author_sort | Kim, S |
| collection | OXFORD |
| description | Stochastic volatility models present a natural way of working with time-varying volatility. However the difficulty involved in estimating these types of models has prevented their wide-spread use in empirical applications. In this paper we exploit Gibbs sampling to provide a likelihood framework for the analysis of stochastic volatility models, demonstrating how to perform either maximum likelihood or Bayesian estimation. The paper includes an extensive Monte Carlo experiment which compares the efficiency of the maximum likelihood estimator with that of quasi-likelihood and Bayesian estimators proposed in the literature. We also compare the fit of the stochastic volatility model to that of ARCH models using the likelihood criterion to illustrate the flexibility of the framework presented. |
| first_indexed | 2024-03-06T23:48:30Z |
| format | Working paper |
| id | oxford-uuid:71c8f1d2-0e7c-4727-a453-c064bc2f03ce |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:48:30Z |
| publishDate | 1994 |
| publisher | Nuffield College (University of Oxford) |
| record_format | dspace |
| spelling | oxford-uuid:71c8f1d2-0e7c-4727-a453-c064bc2f03ce2022-03-26T19:45:49ZStochastic volatility: likelihood inference and comparison with ARCH models.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:71c8f1d2-0e7c-4727-a453-c064bc2f03ceEnglishDepartment of Economics - ePrintsNuffield College (University of Oxford)1994Kim, SShephard, NStochastic volatility models present a natural way of working with time-varying volatility. However the difficulty involved in estimating these types of models has prevented their wide-spread use in empirical applications. In this paper we exploit Gibbs sampling to provide a likelihood framework for the analysis of stochastic volatility models, demonstrating how to perform either maximum likelihood or Bayesian estimation. The paper includes an extensive Monte Carlo experiment which compares the efficiency of the maximum likelihood estimator with that of quasi-likelihood and Bayesian estimators proposed in the literature. We also compare the fit of the stochastic volatility model to that of ARCH models using the likelihood criterion to illustrate the flexibility of the framework presented. |
| spellingShingle | Kim, S Shephard, N Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title | Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title_full | Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title_fullStr | Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title_full_unstemmed | Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title_short | Stochastic volatility: likelihood inference and comparison with ARCH models. |
| title_sort | stochastic volatility likelihood inference and comparison with arch models |
| work_keys_str_mv | AT kims stochasticvolatilitylikelihoodinferenceandcomparisonwitharchmodels AT shephardn stochasticvolatilitylikelihoodinferenceandcomparisonwitharchmodels |