extreme events

Statistical Modeling of Financial Extremes and Volatility Dynamics

Junshu Jiang, Ph.D. Student, Statistics

Apr 6, 14:00 - 17:00

B2 R5220

Quantitative finance Statistical Modeling extreme events numerical analysis

This thesis provides comprehensive statistical tools for understanding and modeling extreme risks and volatility dynamics in financial markets.

Understanding Extreme Market Behavior: The Efficient Tail Hypothesis

2 min read · Thu, Aug 28 2025

extreme events market microstructure extreme statistics

A new study by researchers Junshu Jiang, James Richards, Raphaël Huser, and David Bolin introduces the Efficient Tail Hypothesis (ETH), an analogue of the Efficient Market Hypothesis focused on extreme events in financial markets. Drawing on extreme value theory, the team developed a novel statistical measure to evaluate whether asset markets remain informationally efficient even during rare, extreme fluctuations. Their findings not only challenge conventional wisdom on market efficiency but also open the door to identifying potential opportunities (and risks) that emerge during market

Junshu Jiang

Ph.D. Student, Statistics

Quantitative finance Statistical Modelling extreme events numerical analysis qualitative research Linux Python