MS6601 - Statistical Modelling in Economics and Finance | ||||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||||
Course Aims | ||||||||||||
This graduate-level course in financial econometrics explores advanced techniques for analyzing financial time series data. The course covers the following key topics: (1) Linear Time Series Models: Students will learn to model and forecast financial time series using methods such as autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models. (2) Volatility Modeling: The course delves into ARCH and GARCH models, which are widely used for modeling and forecasting the volatility of financial assets, with applications in risk management and portfolio optimization. (3) Cointegration and Pairs Trading: Cointegration analysis will be introduced as a tool for identifying long-run relationships between financial variables, which can then be leveraged for pairs trading strategies. (4) Factor Models: The course covers factor models, which are used to explain the cross-sectional variation in asset returns, with implications for portfolio management and asset pricing. Throughout the course, students will gain hands-on experience in implementing these techniques using appropriate software, developing a strong foundation in financial econometrics that can be applied in various finance-related domains. | ||||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||||
Continuous Assessment: 100% | ||||||||||||
Detailed Course Information | ||||||||||||
| MS6601.pdf | ||||||||||||
Useful Links | ||||||||||||
| Department of Management Sciences | ||||||||||||
