Asymptotic Normality of Bias Reduction Estimation for Jump Intensity Function in Financial Markets.

  • Published In: Journal of Time Series Analysis, 2024, v. 45, n. 4. P. 558 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Song, Yuping; Zhu, Min; Qiu, Jiawei 3 of 3

Abstract

Continuous‐time diffusion models with jumps, especially the jump intensity coefficient, can depict the impact of sudden and large shocks to financial markets. It is possible to disentangle, from the discrete observations, the contributions given by the jumps and those by the diffusion part through threshold functions. Based on this threshold technique, we employ non‐parametric local linear threshold estimator for the unknown jump intensity function of a semimartingale with jumps. The asymptotic normality of our estimator is provided in the presence of finite activity jumps under certain regular conditions. The finite‐sample performance for the underlying estimator has been shown through a Monte Carlo experiment and an empirical analysis on high frequency returns of indexes in the USA and China. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Time Series Analysis. 2024/07, Vol. 45, Issue 4, p558
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0143-9782
  • DOI:10.1111/jtsa.12727
  • Accession Number:177650658
  • Copyright Statement:Copyright of Journal of Time Series Analysis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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