Teaching
Teaching responsibilities and course materials.
Current courses
2026-2027 AT
ST326: Financial Statistics
Role: GTAInstructor: Prof. Qiwei Yao
Seminars, office hours, and marking support for the undergraduate-level course on financial statistics, covering linear and non-linear time series modelling, portfolio allocation and risk measures.
Archived courses
Seminars, office hours, and marking support for the undergraduate level time series course, covering topics including estimation, inference, model selection, diagnostics and forecasting for linear models, as well as some financial time series topics such as ARCH and GARCH models.
- Week 2: Stationarities, ACVS and ACF Slides
- Week 3: AR and MA processes Slides
- Week 4: AR, MA and ARMA processes Slides
- Week 7: PACF and ARIMA processes Slides
- Week 8: Yule-Walker estimator and some asymptotic results Slides
- Week 9: Estimation, model selection, model diagnostics and forecasting for ARMA processes Slides
- Week 11: ARCH and GARCH models Slides
Seminars, office hours, and marking support for the master's level time series analysis course, covering various topics in univariate and multivariate time series modelling, estimation, inference, diagnostics, forecasting, and financial time series topics such as ARCH and GARCH models. More advanced topics such as vector/matrix factor models, frequency domain methods are covered as well.
- Week 2: Stationarities, ACVS and ACF Slides
- Week 3: White noise, AR and MA processes Slides
- Week 4: ARMA, stationarity and invertibility Slides
- Week 8: Yule-Walker, some asymptotic results, ARCH and GARCH Slides
- Week 9: Estimation, model selection, model diagnostics and forecasting for ARMA processes Slides
- Week 10: Multivariate time series, stationarity, Y-W estimator, CCM Slides
- Week 11: Vector/Matrix factor models Slides
Seminars, office hours, and marking support for the undergraduate level course, covering linear time series modelling, as well as some financial time series topics such as ARCH and GARCH models, some machine learning approaches, portfolio allocation and risk measures.
- Week 2: Stationarities Slides
- Week 3: White noise, AR and MA Slides
- Week 4: ARMA, stationarity, invertibility and ARCH Slides
- Week 5: GARCH and MLE Slides
- Week 7: ML approach and ridge regression Slides
- Week 9: LASSO and portfolio allocation Slides
- Week 10: Factor model Slides
- Week 11: Risk measures and SARIMA Slides
Seminars, office hours, and marking support for the undergraduate level course on introductory probability foundations.
1 GTA = Graduate Teaching Assistant.