报告题目：Inference for Volatility Roughness from Options
报告人：李辰旭 教授
时间：10月23日（周五）下午16:00-17:00
地点：创新港涵英楼8121会议室
报告人简介：
李辰旭博士，北京大学光华管理学院教授，北京大学金融数学与金融工程研究中心主任，博士生导师。致力于金融计量经济学和金融工程学等领域的研究，多项研究成果发表在国际顶级的金融经济学、计量经济学、运筹学、统计学、数理金融学期刊上，包括Review of Financial Studies、Management Science、Operations Research、Mathematics of Operations Research、Annals of Statistics、Journal of Business and Economic Statistics、Journal of Econometrics、Mathematical Finance等。著有《金融中的数学方法》（于2021年1月由北京大学出版社出版)。担任学术期刊《系统工程理论与实践》编委和学术期刊《经济管理学刊》数据科学与人工智能方向副主编（Associate Editor）。曾获由国际工业与系统工程学会（The Institute of Industrial and Systems Engineers）颁发的IIE Transactions运筹学最佳论文奖“2018 Operations Engineering and Analytics Best Paper Award”、全国第七届教育部高等学校科学研究优秀成果奖（人文社会科学）。
摘要：
Rough volatility models have recently gained significant attention in both academia and industry due to their potential advantages over traditional stochastic volatility models in option pricing and volatility forecasting. This paper focuses on statistical inference for volatility roughness using option data. As a theoretical foundation, we analyze the non-Markovian nature of volatility under a general rough volatility model by employing functional stochastic processes that capture the dependency of volatility on historical information. This approach allows us to derive a functional expression for option prices. We then obtain closed-form short-maturity asymptotics of the at-the-money shape characteristics of implied volatility surfaces. These results enable us to develop statistical methods for testing volatility roughness. Through Monte Carlo simulations, we validate the robustness and accuracy of these inference methods. Empirical analysis further confirms the pronounced volatility roughness, driven primarily by idiosyncratic shocks affecting volatility rather than common shocks influencing both the underlying asset's return and volatility.
