Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data |
| |
| Institution: | 1. Department of Statistics, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea;2. School of Management Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea;1. Scuola Normale Superiore, Piazza dei Cavalieri 7, Pisa 56126, Italy;2. Department of Mathematics, Box B6-230, Baruch College, One Bernard Baruch Way, New York, NY 10010, USA;3. QUANTLab, Via Pietrasantina 123, Pisa 56122, Italy;1. Department of Economic Theory, University of Basel, Switzerland;2. Wang Yanan Institute for Studies in Economics and the School of Economics, Xiamen University, China;1. GERAD and Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte Sainte-Catherine, Montréal, Quebec H3T 1V7, Canada;2. La Tour Deloitte, 1190 Avenue des Canadiens-de-Montréal, Montréal, Quebec H3B 0M7, Canada;1. Department of Biology, Center for the Integrative Study of Animal Behavior, Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA;2. Department of Chemistry, Institute for Pheromone Research, Indiana University, Bloomington, IN 47405, USA |
| |
| Abstract: | 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: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
