| 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. |