The Research on the Prediction of VIX Index Based on the Machine Learning Method
- DOI
- 10.2991/978-94-6463-102-9_152How to use a DOI?
- Keywords
- VIX index; Prediction; Machine Learning
- Abstract
As a barometer of quantitative investment, the volatility and future trend of VIX index can provide a reference for investors. This paper uses the data of CBOE from 1990 to 2022 to analyze its data characteristics. It finds the VIX index can be divided into two blocks, a flat zone and a rising zone. In the stable area, the VIX index satisfies the characteristics of mean reorientation, and the fluctuation range is basically near the historical average. In the climbing area, the unstable market environment leads to a surge in people's risk aversion, which leads to a significant increase in the VIX index. Besides, this paper further uses the machine learning method to predict the future VIX index. It suggests that the VIX fluctuated up and down a bit, but did not spike, indicating that the market is still in a normal state. Through the prediction of the trend of VIX index, the public can provide a forward-looking forecast, for the future need to avoid risk, to respond to risk in advance to provide a feasible forecast scheme.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ke Chen PY - 2022 DA - 2022/12/29 TI - The Research on the Prediction of VIX Index Based on the Machine Learning Method BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 1448 EP - 1456 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_152 DO - 10.2991/978-94-6463-102-9_152 ID - Chen2022 ER -