The Information Content of The Implied Volatility Surface: How to More Efficiently Use Option Information to Predict Stock Returns?
（来自隐含波动率曲面的信息：如何更有效地利用期权信息预测股票收益？）
主讲人：唐潇潇（UT Dallas金融系助理教授）
主持老师：（北大经院）王熙
参与老师：（北大经院）王一鸣、刘蕴霆
（北大国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构）胡博
时间：2021年12月24日（周五） 10:00-11:30
地点（线上）： 腾讯会议：556-470-704，会议密码：211224
链接：https://meeting.tencent.com/dm/gkeVaknH4I8W
主讲人简介：
唐潇潇，UT Dallas金融系助理教授，分别于2018年，2014年，和2009年获得美国华盛顿大学金融学博士学位，美国弗吉尼亚大学统计学博士学位和清华大学数学学士学位。唐潇潇博士的研究领域是asset pricing, options 和recovery。其研究成果发表在《Review of Financial Studies》、《Journal of Portfolio Management》以及《Journal of Forecasting》等国外核心期刊。
摘要：
Applying the partial least squares (PLS) approach to the entire implied volatility (IV) surface, we show that option prices predict downward jumps, but not upward jumps, in the underlying stock prices. The long-short portfolio formed based on the estimated downward jump factor yields an annual return of 18.36% with a Sharpe ratio of 1.29. The predictability of the downward jump factor is very robust and much stronger than that of other IV-related predictors. Finally, we show that the predictability is consistent with the notion that informed investors trade options to profit from negative information to circumvent the equity short-sale constraint.
