Measuring and forecasting financial variability using realised variance with and without a model.
We use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known character...
| Main Authors: | , , , |
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| 格式: | Working paper |
| 語言: | English |
| 出版: |
Nuffield College (University of Oxford)
2002
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| _version_ | 1826294634884628480 |
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| author | Barndorff-Nielsen, O Nielsen, B Shephard, N Ysusi, C |
| author_facet | Barndorff-Nielsen, O Nielsen, B Shephard, N Ysusi, C |
| author_sort | Barndorff-Nielsen, O |
| collection | OXFORD |
| description | We use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known characteristics. Hence filtering, smoothing and forecasting ideas can be used to improve our estimates of variability by exploiting the time series structure of the realised variances. This can be carried out based on a model or without a model. A comparison is made between these two methods. |
| first_indexed | 2024-03-07T03:48:43Z |
| format | Working paper |
| id | oxford-uuid:c0773d2a-f44f-4436-a3a5-b22a3718304c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:48:43Z |
| publishDate | 2002 |
| publisher | Nuffield College (University of Oxford) |
| record_format | dspace |
| spelling | oxford-uuid:c0773d2a-f44f-4436-a3a5-b22a3718304c2022-03-27T05:54:31ZMeasuring and forecasting financial variability using realised variance with and without a model.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:c0773d2a-f44f-4436-a3a5-b22a3718304cEnglishDepartment of Economics - ePrintsNuffield College (University of Oxford)2002Barndorff-Nielsen, ONielsen, BShephard, NYsusi, CWe use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known characteristics. Hence filtering, smoothing and forecasting ideas can be used to improve our estimates of variability by exploiting the time series structure of the realised variances. This can be carried out based on a model or without a model. A comparison is made between these two methods. |
| spellingShingle | Barndorff-Nielsen, O Nielsen, B Shephard, N Ysusi, C Measuring and forecasting financial variability using realised variance with and without a model. |
| title | Measuring and forecasting financial variability using realised variance with and without a model. |
| title_full | Measuring and forecasting financial variability using realised variance with and without a model. |
| title_fullStr | Measuring and forecasting financial variability using realised variance with and without a model. |
| title_full_unstemmed | Measuring and forecasting financial variability using realised variance with and without a model. |
| title_short | Measuring and forecasting financial variability using realised variance with and without a model. |
| title_sort | measuring and forecasting financial variability using realised variance with and without a model |
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