论文标题
高频CSI 300指数和动态跳跃预测的随机波动建模由机器学习驱动
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning
论文作者
论文摘要
本文模拟了中国金融市场中CSI 300指数价格时间序列的随机过程，分析了盘内高频价格数据的波动性特征。在新的广义Barndorff-Nielsen和Shephard模型中，考虑了市场信息异步引起的滞后，并且解决了缺乏长期依赖性的问题。为了加快评估过程，使用了几种机器学习和深度学习算法来估计参数并评估预测结果。跟踪不同幅度的历史跳跃为模拟动态价格过程并预测未来的跳跃提供了有希望的途径。数值结果表明，随机波动过程的确定性组成部分将始终在短期和长期的窗口中捕获。研究发现可能适合影响有兴趣基于实现波动性的市场动态的投资者和监管机构。
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.
