Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning

TL;DR

This paper develops a stochastic volatility model for the CSI 300 index, incorporating market asynchrony and leveraging machine learning for parameter estimation and jump prediction, enhancing understanding of high-frequency market dynamics.

Contribution

It introduces a generalized Barndorff-Nielsen and Shephard model with machine learning techniques to improve jump prediction and volatility modeling in high-frequency Chinese stock data.

Findings

Deterministic volatility components are consistently captured.

Machine learning algorithms effectively estimate parameters and forecast jumps.

Model accounts for market asynchrony and long-term dependence issues.

Abstract

This paper models stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-Nielsen and Shephard model, the lag caused by asynchrony of market information is considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of different magnitudes offers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research finding could be suitable for influence investors and regulators interested in predicting market dynamics based on realized volatility.