时间：2023年12月21日（星期四）上午10:00-12:00

地点：线上腾讯会议（会议号618-616-998）

报告题目：Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

【内容简介】

Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises.

【主讲人简介】

林航，伊利诺伊大学尔巴纳-香槟分校（University of Illinois Urbana-Champaign，UIUC）期货与期权研究中心（Office for Futures and Options Research，OFOR）助理研究员、应用经济学博士研究生，对外经济贸易大学金融学专业经济学学士、统计学专业统计学博士，威斯康星大学麦迪逊分校（University of Wisconsin–Madison）统计学系访问学者。研究方向为金融经济学、金融统计学、宏观金融、金融衍生工具、资产定价理论与实证、极端金融风险、气候金融等。在Energy Economics、Earth’s Future、Mathematics、金融理论与实践等国内外重要期刊发表学术论文多篇。现为Journal of Empirical Finance、Finance Research Letters、Financial Innovation、Energy Economics、Applied Economic Perspectives and Policy、Agricultural Economics等学术期刊资产定价、金融衍生工具、金融风险分析、金融时间序列分析等方向匿名审稿人。