Economics at your fingertips  

Analysis of stock market volatility: Adjusted VPIN with high-frequency data

Haijun Yang and Feng Xue

International Review of Economics & Finance, 2021, vol. 75, issue C, 210-222

Abstract: The volume-synchronized probability of informed trading (VPIN) is widely accepted as a proxy of volatility in the high-frequency market. We propose a novel VPIN model, called Adjusted VPIN, to improve the performance of VPIN so that it can directly analyze and better predict the information asymmetry of individual stocks. We extend the VPIN model by optimizing the classification algorithm with a neural network method and high-frequency data. Both trading volume and trends are considered to capture stock volatility. Empirical results on three different trading volume groups generate a 37.86% higher relevant result with logarithm stock yield than the VPIN model.

Keywords: High-frequency trading; Volatility; Adjusted VPIN; Stock market (search for similar items in EconPapers)
JEL-codes: G10 G12 G14 G17 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.iref.2021.04.003

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2021-07-24
Handle: RePEc:eee:reveco:v:75:y:2021:i:c:p:210-222