Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampli...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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Taylor and Francis
2006
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| _version_ | 1826286400964657152 |
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| author | Bos, C Shephard, N |
| author_facet | Bos, C Shephard, N |
| author_sort | Bos, C |
| collection | OXFORD |
| description | In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampling the unobserved variance series directly, we sample in the space of the the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain. Using our reparameterized MCMC sampler, it is possible to estimate an unobserved factor model for exchange rates between a group of n countries. The underlying n + 1 country-specific currency strength factors and the n + 1 currency volatility factors can be extracted using the new methodology. With the factors, a more detailed image of the events around the 1992 EMS crisis is obtained. We assess the fit of competitive models on the panels of exchange rates with an effective particle filter and find that indeed the factor model is strongly preferred by the data. |
| first_indexed | 2024-03-07T01:43:12Z |
| format | Journal article |
| id | oxford-uuid:978a2b57-61d3-47b9-871d-b19f319da0d8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:43:12Z |
| publishDate | 2006 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | oxford-uuid:978a2b57-61d3-47b9-871d-b19f319da0d82022-03-27T00:00:32ZInference for adaptive time series models: stochastic volatility and conditionally Gaussian state space formJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:978a2b57-61d3-47b9-871d-b19f319da0d8EconometricsEconomicsEnglishOxford University Research Archive - ValetTaylor and Francis2006Bos, CShephard, NIn this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampling the unobserved variance series directly, we sample in the space of the the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain. Using our reparameterized MCMC sampler, it is possible to estimate an unobserved factor model for exchange rates between a group of n countries. The underlying n + 1 country-specific currency strength factors and the n + 1 currency volatility factors can be extracted using the new methodology. With the factors, a more detailed image of the events around the 1992 EMS crisis is obtained. We assess the fit of competitive models on the panels of exchange rates with an effective particle filter and find that indeed the factor model is strongly preferred by the data. |
| spellingShingle | Econometrics Economics Bos, C Shephard, N Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title | Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title_full | Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title_fullStr | Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title_full_unstemmed | Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title_short | Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form |
| title_sort | inference for adaptive time series models stochastic volatility and conditionally gaussian state space form |
| topic | Econometrics Economics |
| work_keys_str_mv | AT bosc inferenceforadaptivetimeseriesmodelsstochasticvolatilityandconditionallygaussianstatespaceform AT shephardn inferenceforadaptivetimeseriesmodelsstochasticvolatilityandconditionallygaussianstatespaceform |