Predictive Factor Model for Jump Intensities. submitted.
with Yi Ding, Yingying Li, and Xinghua Zheng
Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6496119
Presented at: The 2025 Annual Meeting of the Greater Bay Econometrics Study Group (Hong Kong, 2025), The 2nd HKUST IAS-SBM Joint Workshop on Financial Econometrics in the Big Data Era (Hong Kong, 2025), 17th SoFiE Conference, ESSEC, (France, 2025) ,The 19th International Symposium on Econometric Theory and Applications (Macau, 2025).
(Abstract) We propose a parsimonious factor model for jump intensities for a large number of stocks, which we call the Jump Factor Intensity Thinning (J-FIT) Model. The model utilizes a jump factor process to capture the complete grid of potential jumps, and a thinning procedure to capture individual stock jumps. The jump factor process features temporal dependence, which leads to predictability. We develop estimators and inference theories for the model parameters, enabling applications such as jump intensity prediction and constructing prediction intervals for the jump risk exposures of individual stocks and sectors. Empirically, we provide solid evidence that jumps are predictable with the J-FIT model, whose predictive performance substantially surpasses various benchmark models. We also demonstrate that J-FIT is useful for tail risk management.
Efficient High-Dimensional Covariance Matrix Estimation Incorporating Trading Information
with Dachuan Chen, Yingying Li, and Xinghua Zheng
(Abstract) This paper proposes the first trading information incorporated estimation methodology for high-dimensional covariance matrix using high-frequency data. Our method extends the univariate trading information incorporated variance estimator of Li et al. (2016) to the high-dimensional setting, allowing the cross-sectional dimension d to grow exponentially in n^{1-\varepsilon} for any \varepsilon∈ (0,1), where n is the intraday observation frequency. Theoretically, under mild assumptions, we establish the tail property of the trading information incorporated covariance estimator. We then impose a factor structure on cross-sectional intraday returns and apply POET to estimate high-dimensional covariance and precision matrices. We demonstrate the effectiveness of the proposed estimators through simulations and an empirical study using second-by-second Trade and Quote data from S&P 500 index constituents, showing that the minimum variance portfolio constructed with our estimator achieves lower long-term risk than that relying on the pre-averaging-based estimator.
Improving High-Frequency Volatility Forecasting: Integrating Machine Learning Models with Intraday Periodic Patterns and Firm-Specific News
with Bo Zhou and Yingying Li
(Abstract) This paper introduces an innovative framework for high-frequency volatility forecasting by synergistically integrating intraday periodic patterns, advanced machine learning (ML) models, and firm-specific news. Utilizing one-minute price data and real-time news feeds from DJ30 index constituents, we benchmark the forecasting performance of Neural Network (NN) and Gated Recurrent Unit (GRU) models against the traditional Heterogeneous Autoregressive (HAR) model. Our approach flexibly incorporates both intraday patterns and overnight news to enhance predictive accuracy. Empirical results reveal that the NN reduces prediction error by 25% and the GRU by 10% relative to HAR. Moreover, integrating intraday patterns delivers an additional average error reduction of 8%, while overnight news contributes a further 7% reduction. Notably, the predictive power of news is strongest near market open and declines towards market close. Overall, our findings demonstrate that combining ML techniques with structural intraday market patterns and firm-specific news significantly improves high-frequency volatility forecasting.
Is a promise a promise? Analyzing performance commitment in acquisitions and target firm performance
with Qizhi Tao, Ming Liu, and Yun Zhang. - The European Journal of Finance (2022).