杏吧论坛

Dr Andrea Tamoni

This course is available on the MSc in Accounting and Finance, MSc in Finance and Economics, MSc in Finance and Economics (Work Placement Pathway), MSc in Finance and Private Equity, MSc in Finance and Private Equity (Work Placement Pathway), MSc in Financial Mathematics, MSc in Quantitative Methods for Risk Management, MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Quantitative Methods for Finance and Risk Analysis (FM442).

The first half of FM437 Financial Econometrics, or alternatively FM442 Quantitative Methods for Finance and Risk Analysis, is a required prerequisite. Students who can demonstrate comparable background may be granted an exemption from this requirement.

This course will examine the techniques involved with forecasting key variables in finance, and how to incorporate model uncertainty into financial forecasts. Students will learn both the theory and the practice of forecasting in finance.

• The following topics will be covered: introduction to time series analysis; Maximum Likelihood Estimation (MLE), and MLE based model selection; Bayesian inference, posterior probabilities, and Bayesian Model Averaging; Markov Chain Monte Carlo methods; Present value regressions; Multivariate models and Bayesian Vector Autoregressions; Cointegration; Asset pricing and the Generalized Method of Moments (GMM); Dynamic factor models and their connection with principal components; Introduction to Machine Learning and Finance.

Additional information can be found on Moodle (for current students)

20 hours of lectures, 10 hours of classes and 10 hours of computer workshops in the LT.

Regular problem sets.

Lecture notes will be provided, and some journal articles may also be used.

Exam (90%, duration: 3 hours, reading time: 15 minutes) in the summer exam period.
Coursework (10%).

Summative coursework during term time teaching in LT