Rate My Professor Felix Chan

Chan's research specializations lie in econometrics, applied statistics, and information theory, with a focus on nonlinear dynamics, volatility modeling, and advanced econometric techniques. His influential publications include the highly cited 'Generalized Autoregressive Conditional Correlation' in Econometric Theory (2008, with Michael McAleer, Suhejla Hoti, and Offer Lieberman), which examines multivariate GARCH processes. Other key works encompass 'Nonlinear dynamics of exchange rate returns: A multi-country experience' (Curtin University repository), 'It pays to Violate: Model Choice and Critical Value Assumption for Forecasting Value-at-Risk Thresholds' (conference contribution), and contributions to 'Econometrics with Machine Learning' (Springer, 2022, co-authored with László Mátyás and others), including chapters on nonlinear econometric models. He has published extensively on topics such as asymptotic properties of quasi-maximum likelihood estimators for Log-ACD models, modeling volatility in foreign currency options, and congestion costs in government services. Chan serves as an Associate Editor, delivers keynote addresses on econometrics and machine learning, and participates in advisory boards and research supervision. His scholarship has advanced panel data modeling, exchange rate analysis, and applied economic forecasting, impacting the fields of economics and finance.