Abstract: This paper applies extreme value theory (EVT) and Hawkes processes in the AR-GARCH framework to model the tail risk clustering effect. The proposed model improves forecasts of the timing of extreme returns and is particularly useful for downside risk analysis. Due to a large parameter set, we propose a two-step calibration method to estimate the model. We apply this model on 90 stocks, including both large-caps and small-caps, in nine industry sectors. The out-sample experiments show a strong self-excitation of negative AR-GARCH residuals, and it is well captured using the proposed model. The value-at-risk forecasting experiments over the past 25 years con rm that our model produces accurate downside risk estimations. More importantly, the proposed model provides more accurate risk analysis results during market crisis than other existing benchmark models.

Zoom Meeting：https://us02web.zoom.us/j/89218017166

Meeting Number：892 1801 7166

Meeting ID：467381

About the Speaker：Dr. Steve Yang is an Associate Professor of the School of Business at Stevens Institute of Technology. He holds a Ph.D. in Systems and Information Engineering from University of Virginia. His current research interests include financial decisions, behavioral finance, algorithmic trading, portfolio optimization, and agent based financial market simulation. His research has been published on journals such as Quantitative Finance, Decision Sciences, Journal of Banking & Finance, Expert Systems with Applications, etc. His research has been founded by many NGOs, government agencies and industry firms such as, NSF, SWIFT, IRRC, IAAER, CFTC, DoD, Accenture, Northrop Grumman, KPMG, etc. He was a visiting academic scholar with the U.S. Securities and Exchange Commission (SEC) in 2020. Dr. Yang currently serves the Director of the NSF Center for Research toward Advancing Financial Technologies.