报告题目：Forecasting Volatility with Sentiment: Do we need to catch the hype of Al and LLM?
报告人：陈静 教授
邀请人：陈阵 副教授
报告时间及地点：2026年10月9日 15：30-17：00 经管学院D307
报告人简况：
陈静（Maggie CHEN），威尔士皇家学术学院院士、卡迪夫大学金融数学教授，卡迪夫大学数据转型创新研究所金融科技项目负责人，主持并参与英国国家物理工程科学基金人工智能以及金融科技国家级项目，威尔士数据国家加速计划、图灵研究所以及卡迪夫数据转型创新研究所等多项项目，在SSCI期刊发表学术论文近百篇，主要研究方向是运用跨学科方法进行金融建模（如霍克斯过程、网络分析等）来解决现代金融问题，担任 《The Jounrnal of Futures Market》, 《The European Journal of Finance 》(EJF), 《International Review of Economics and Finance》, 《IMA Journal of Management Mathematics and Quantitative Finance》等多家期刊编委。
报告内容摘要：
Abstract: Volatility forecasting for Bitcoin is of great interest but challenging due to its complex features. Although the GARCH models are typically used, they are limited to accommodate properties exacerbated by extreme movements and behavioural factors. In the era of AI, we investigate whether the old (GARCH) can work with the new (machines or agents) that are equipped with great capacity to actually produce better forecasting for the highly liquid and risk Bitcoin. We start with a simple GARCH-LSTM (Long Short-Term Memory) structure and find strong evidence of better forecasting performance. Then, we introduce deep learning algorithms or LLM agents to news sentiment signals - that includes three architectures benchmarked under strict leakage controls: Convolutional Neural Network (CNN)-(LSTM), Attention-LSTM and Transformer. We find that the Attention-LSTM, which employs an attention mechanism to dynamically weight time steps and sentiment features beats others, achieving the lowest errors for 1- and 5-day forecasting in- and out-of-sample. We conclude that sentiment signals are important for accurate Bitcoin volatility forecasting; machines and AI agents are stronger than traditional models but AI is not necessarily more intelligent than the well defined machines.
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