Packages for Data Analysis
Published:
This article introduces the fundamental packages for data analysis in Python: NumPy, Pandas, SciPy, and Matplotlib.
Posts by Collection
Econ
Python
Matplotlib: The Visualization Toolkit for Data Analysis
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This article introduces Matplotlib, the fundamental library for creating static, interactive, and animated visualizations in Python.
BERTopic: Neural Topic Modeling for Modern Text Analysis
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This article introduces BERTopic, a state-of-the-art neural topic modeling technique that leverages transformer-based embeddings and density-based clustering to discover meaningful topics from text corpora.
Quan
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
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Short description of portfolio item number 2
publications
The Propagation Path of Jump Risk in the Stock Market
Published in Working paper, 2023
This paper identifies the propagation paths of jump risk in the stock market by combining high-frequency data with network analysis. The results reveal six risk communities and key transmission channels, showing that the securities community is the main source of jump risk, which spreads to the broader market and then to energy and infrastructure sectors.
Recommended citation: He, Sixuan, and Cheng Liu. (2023). "The Propagation Path of Jump Risk in the Stock Market." Working paper.
High-Dimensional Realized Tail Risk Forecasting Based on Tail-Event Driven Networks
Published in Working paper, 2025
This paper proposes a tail-event driven network approach for high-dimensional realized tail risk forecasting.
Recommended citation: He, Sixuan, and Cheng Liu. (2025). "High-Dimensional Realized Tail Risk Forecasting Based on Tail-Event Driven Networks." Working paper.
High-dimensional Multivariate Realized Volatility Forecasting with Community Network Structure
Published in Journal of Business & Economic Statistics (accepted author version), 2026
We introduce the Community Network Heterogeneous Autoregressive model with Quarticity (CNHARQ), which integrates network-based information to enhance multivariate realized volatility forecasting. A Correlation-Based Stochastic Block Model (CBSBM) uncovers latent community structures, reducing parameter dimensionality and improving out-of-sample forecast accuracy over static industry classifications.
Recommended citation: He, Sixuan, and Cheng Liu. (2026). "High-dimensional Multivariate Realized Volatility Forecasting with Community Network Structure." Journal of Business & Economic Statistics. (accepted author version, posted online February 18, 2026) https://doi.org/10.1080/07350015.2026.2632810
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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