报告题目：Forecasting Exchange Rate: Using a Large Panel of Individual Stock Price Data
内容摘要：Exchange rate forecast is of great importance to economic agents, including households, businesses, and policy makers. However, predicting exchange rates is a challenging task. The Meese-Rogoff puzzle claims that economic models of exchange rates seem unable to outperform a simple Random Walk model in out-of-sample forecasting. This paper uses a novel econometric micro approach to forecast exchange rate based on a large panel of individual stock prices, which is expected contain valuable information about future inflation according to economic theory. Our results demonstrate that this micro forecasting method significantly enhances the accuracy of exchange rate forecasting when compared to macro-econometric models such as Random Walk and AR time series models. By employing machine learning algorithms, we can effectively aggregate the information contained in individual stock prices, which leads to more precise exchange forecasts. Moreover, the improvement achieved through the micro forecasting method becomes increasingly prominent as the forecast horizon increases.
主讲人简介：黄乃静，中央财经大学经济学院长聘副教授，博士生导师，国民经济系主任。2015年取得美国波士顿学院经济学博士学位。以金融计量经济学和货币经济学为主要研究方向，先后在《管理科学学报》、《经济学动态》、《中国软科学》、Emerging Market Finance and Trade等国内外学术刊物发表多篇论文。获得2017年厦门大学金融工程与量化金融学术会议最佳论文奖、2014年美国国家科学基金会旅行奖励（US National Science Foundation Travel Grant）等学术奖项。主持并参与多项国家自然科学基金，国家社科基金。
报告时间：2023年10月10日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：393 3774 3329
