Variance reduction estimation for return models with jumps using gamma asymmetric kernels Article Swipe
Yuping Song
,
Weijie Hou
,
Zhou Sheng-yi
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1515/snde-2018-0001
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1515/snde-2018-0001
This paper discusses Nadaraya-Watson estimators for the unknown coefficients in second-order diffusion model with jumps constructed with Gamma asymmetric kernels. Compared with existing nonparametric estimators constructed with Gaussian symmetric kernels, local constant smoothing using Gamma asymmetric kernels possesses some extra advantages such as boundary bias correction, variance reduction and resistance to sparse design points, which is validated through theoretical details and finite sample simulation study. Under the regular conditions, the weak consistency and the asymptotic normality of these estimators are presented. Finally, the statistical advantages of the nonparametric estimators are depicted through 5-minute high-frequency data from Shenzhen Stock Exchange in China.
Related Topics
Concepts
Estimator
Mathematics
Nonparametric statistics
Variance reduction
Smoothing
Consistency (knowledge bases)
Kernel smoother
Asymptotic distribution
Smoothing spline
Applied mathematics
Gaussian
Econometrics
Statistics
Kernel method
Monte Carlo method
Computer science
Bilinear interpolation
Geometry
Artificial intelligence
Spline interpolation
Support vector machine
Radial basis function kernel
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/snde-2018-0001
- https://www.degruyter.com/downloadpdf/journals/snde/23/5/article-20180001.pdf
- OA Status
- bronze
- Cited By
- 3
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2939924526
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2939924526Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1515/snde-2018-0001Digital Object Identifier
- Title
-
Variance reduction estimation for return models with jumps using gamma asymmetric kernelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-04-09Full publication date if available
- Authors
-
Yuping Song, Weijie Hou, Zhou Sheng-yiList of authors in order
- Landing page
-
https://doi.org/10.1515/snde-2018-0001Publisher landing page
- PDF URL
-
https://www.degruyter.com/downloadpdf/journals/snde/23/5/article-20180001.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.degruyter.com/downloadpdf/journals/snde/23/5/article-20180001.pdfDirect OA link when available
- Concepts
-
Estimator, Mathematics, Nonparametric statistics, Variance reduction, Smoothing, Consistency (knowledge bases), Kernel smoother, Asymptotic distribution, Smoothing spline, Applied mathematics, Gaussian, Econometrics, Statistics, Kernel method, Monte Carlo method, Computer science, Bilinear interpolation, Geometry, Artificial intelligence, Spline interpolation, Support vector machine, Radial basis function kernel, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 1, 2019: 1Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2125854854, https://openalex.org/W2136215272, https://openalex.org/W1994083140, https://openalex.org/W2054211118, https://openalex.org/W2062042027, https://openalex.org/W2050617311, https://openalex.org/W2748085134, https://openalex.org/W2059908963, https://openalex.org/W2053255022, https://openalex.org/W1573503109, https://openalex.org/W2119876373, https://openalex.org/W2021310871, https://openalex.org/W2338968327, https://openalex.org/W4293092461, https://openalex.org/W2107193251, https://openalex.org/W2114095694, https://openalex.org/W3021467507, https://openalex.org/W4246040193, https://openalex.org/W1980519218, https://openalex.org/W2009129315, https://openalex.org/W1980500537, https://openalex.org/W1995522593, https://openalex.org/W2011131366, https://openalex.org/W2731788773, https://openalex.org/W2068273852, https://openalex.org/W2043758517, https://openalex.org/W2557956804, https://openalex.org/W2019463243, https://openalex.org/W2522022257, https://openalex.org/W2962868822, https://openalex.org/W2130211402, https://openalex.org/W2329341088, https://openalex.org/W2081877251 |
| referenced_works_count | 33 |
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| abstract_inverted_index.and | 49, 61, 73 |
| abstract_inverted_index.are | 80, 90 |
| abstract_inverted_index.for | 6 |
| abstract_inverted_index.the | 7, 67, 70, 74, 83, 87 |
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| abstract_inverted_index.from | 96 |
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| abstract_inverted_index.with | 14, 17, 22, 27 |
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| abstract_inverted_index.Finally, | 82 |
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| abstract_inverted_index.Shenzhen | 97 |
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| abstract_inverted_index.depicted | 91 |
| abstract_inverted_index.existing | 23 |
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| abstract_inverted_index.kernels. | 20 |
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| abstract_inverted_index.diffusion | 12 |
| abstract_inverted_index.discusses | 3 |
| abstract_inverted_index.normality | 76 |
| abstract_inverted_index.possesses | 38 |
| abstract_inverted_index.reduction | 48 |
| abstract_inverted_index.smoothing | 33 |
| abstract_inverted_index.symmetric | 29 |
| abstract_inverted_index.validated | 57 |
| abstract_inverted_index.advantages | 41, 85 |
| abstract_inverted_index.asymmetric | 19, 36 |
| abstract_inverted_index.asymptotic | 75 |
| abstract_inverted_index.estimators | 5, 25, 79, 89 |
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| abstract_inverted_index.high-frequency | 94 |
| abstract_inverted_index.Nadaraya-Watson | 4 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100653854 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I21945476 |
| citation_normalized_percentile.value | 0.69657751 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |