Simulated likelihood inference for stochastic volatility models using continuous particle filtering
Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. Fir...
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| Format: | Journal article |
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2014
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| _version_ | 1797105945092816896 |
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| author | Pitt, M Malik, S Doucet, A |
| author_facet | Pitt, M Malik, S Doucet, A |
| author_sort | Pitt, M |
| collection | OXFORD |
| description | Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. First, we propose a new SV model, namely SV-GARCH, which bridges the gap between SV and GARCH models: it has the attractive feature of inheriting unconditional properties similar to the standard GARCH model but being conditionally heavier tailed. Second, we propose a likelihood-based inference technique for a large class of SV models relying on the recently introduced continuous particle filter. The approach is robust and simple to implement. The technique is applied to daily returns data for SandP 500 and Dow Jones stock price indices for various spans. © 2014 The Institute of Statistical Mathematics, Tokyo. |
| first_indexed | 2024-03-07T06:54:38Z |
| format | Journal article |
| id | oxford-uuid:fdb5c2fe-d5b8-428f-9e6c-05593fa543cb |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:54:38Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:fdb5c2fe-d5b8-428f-9e6c-05593fa543cb2022-03-27T13:30:54ZSimulated likelihood inference for stochastic volatility models using continuous particle filteringJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fdb5c2fe-d5b8-428f-9e6c-05593fa543cbSymplectic Elements at Oxford2014Pitt, MMalik, SDoucet, ADiscrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. First, we propose a new SV model, namely SV-GARCH, which bridges the gap between SV and GARCH models: it has the attractive feature of inheriting unconditional properties similar to the standard GARCH model but being conditionally heavier tailed. Second, we propose a likelihood-based inference technique for a large class of SV models relying on the recently introduced continuous particle filter. The approach is robust and simple to implement. The technique is applied to daily returns data for SandP 500 and Dow Jones stock price indices for various spans. © 2014 The Institute of Statistical Mathematics, Tokyo. |
| spellingShingle | Pitt, M Malik, S Doucet, A Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title | Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title_full | Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title_fullStr | Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title_full_unstemmed | Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title_short | Simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| title_sort | simulated likelihood inference for stochastic volatility models using continuous particle filtering |
| work_keys_str_mv | AT pittm simulatedlikelihoodinferenceforstochasticvolatilitymodelsusingcontinuousparticlefiltering AT maliks simulatedlikelihoodinferenceforstochasticvolatilitymodelsusingcontinuousparticlefiltering AT douceta simulatedlikelihoodinferenceforstochasticvolatilitymodelsusingcontinuousparticlefiltering |