Central Limit Theorems of Local Polynomial Threshold Estimators for Diffusion Processes with Jumps Article Swipe
Yuping Song
,
Hanchao Wang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.00907
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.00907
Central limit theorems play an important role in the study of statistical inference for stochastic processes. However, when the nonparametric local polynomial threshold estimator, especially local linear case, is employed to estimate the diffusion coefficients of diffusion processes, the adaptive and predictable structure of the estimator conditionally on the $σ-$field generated by diffusion processes is destroyed, the classical central limit theorem for martingale difference sequences can not work. In this paper, we proved the central limit theorems of local polynomial threshold estimators for the volatility function in diffusion processes with jumps. We believe that our proof for local polynomial threshold estimators provides a new method in this fields, especially local linear case.
Related Topics
Concepts
Estimator
Mathematics
Central limit theorem
Local volatility
Local martingale
Applied mathematics
Polynomial
Limit (mathematics)
Nonparametric statistics
Diffusion
Martingale (probability theory)
Stochastic volatility
Jump diffusion
Volatility (finance)
Mathematical analysis
Econometrics
Statistics
Physics
Jump
Thermodynamics
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1702.00907
- https://arxiv.org/pdf/1702.00907
- OA Status
- green
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2586953683
All OpenAlex metadata
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https://openalex.org/W2586953683Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1702.00907Digital Object Identifier
- Title
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Central Limit Theorems of Local Polynomial Threshold Estimators for Diffusion Processes with JumpsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-02-03Full publication date if available
- Authors
-
Yuping Song, Hanchao WangList of authors in order
- Landing page
-
https://arxiv.org/abs/1702.00907Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1702.00907Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1702.00907Direct OA link when available
- Concepts
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Estimator, Mathematics, Central limit theorem, Local volatility, Local martingale, Applied mathematics, Polynomial, Limit (mathematics), Nonparametric statistics, Diffusion, Martingale (probability theory), Stochastic volatility, Jump diffusion, Volatility (finance), Mathematical analysis, Econometrics, Statistics, Physics, Jump, Thermodynamics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.structure | 42 |
| abstract_inverted_index.threshold | 22, 80, 99 |
| abstract_inverted_index.$σ-$field | 49 |
| abstract_inverted_index.destroyed, | 55 |
| abstract_inverted_index.difference | 63 |
| abstract_inverted_index.especially | 24, 108 |
| abstract_inverted_index.estimator, | 23 |
| abstract_inverted_index.estimators | 81, 100 |
| abstract_inverted_index.martingale | 62 |
| abstract_inverted_index.polynomial | 21, 79, 98 |
| abstract_inverted_index.processes, | 37 |
| abstract_inverted_index.processes. | 15 |
| abstract_inverted_index.stochastic | 14 |
| abstract_inverted_index.volatility | 84 |
| abstract_inverted_index.predictable | 41 |
| abstract_inverted_index.statistical | 11 |
| abstract_inverted_index.coefficients | 34 |
| abstract_inverted_index.conditionally | 46 |
| abstract_inverted_index.nonparametric | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |