Subsampling realised kernels
In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...
| Main Authors: | , , |
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| Format: | Working paper |
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University of Oxford
2006
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| _version_ | 1797094671104606208 |
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| author | Shephard, N Barndorff-Nielsen, O Lunde, A |
| author_facet | Shephard, N Barndorff-Nielsen, O Lunde, A |
| author_sort | Shephard, N |
| collection | OXFORD |
| description | In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work. |
| first_indexed | 2024-03-07T04:17:14Z |
| format | Working paper |
| id | oxford-uuid:c9cc770f-7193-45ac-943b-8722af45b601 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:17:14Z |
| publishDate | 2006 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:c9cc770f-7193-45ac-943b-8722af45b6012022-03-27T07:02:24ZSubsampling realised kernelsWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:c9cc770f-7193-45ac-943b-8722af45b601Bulk import via SwordSymplectic ElementsUniversity of Oxford2006Shephard, NBarndorff-Nielsen, OLunde, AIn a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work. |
| spellingShingle | Shephard, N Barndorff-Nielsen, O Lunde, A Subsampling realised kernels |
| title | Subsampling realised kernels |
| title_full | Subsampling realised kernels |
| title_fullStr | Subsampling realised kernels |
| title_full_unstemmed | Subsampling realised kernels |
| title_short | Subsampling realised kernels |
| title_sort | subsampling realised kernels |
| work_keys_str_mv | AT shephardn subsamplingrealisedkernels AT barndorffnielseno subsamplingrealisedkernels AT lundea subsamplingrealisedkernels |