Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data
Kyungsub Lee and
Byoung Ki Seo
Journal of Economic Dynamics and Control, 2017, vol. 79, issue C, 154-183
This study examines the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using the ultra-high-frequency data of stocks in the S&P 500 are performed. The performance of the volatility measure, intraday estimation, and the dynamics of the parameters are discussed. A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model. As a diffusion process, the model provides more analytical simplicity when computing the variance formula, incorporating skewness and examining the probabilistic property. An estimation of the diffusion model is performed using the simulated maximum likelihood method and shows similar patterns to the Hawkes model.
Keywords: Stock price dynamics; Tick structure; Hawkes process; Volatility; Diffusion model (search for similar items in EconPapers)
JEL-codes: C13 C32 C58 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:79:y:2017:i:c:p:154-183
Access Statistics for this article
Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok
More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().