Syllabus academic year 2011/2012
(Created 2011-09-01.)
Credits: 7,5. Grading scale: TH. Cycle: A (Second Cycle). Main field: Technology. Language of instruction: The course will be given in English on demand. FMS161 overlaps following cours/es: MAS229 and MASM18. Optional for: D4, F5, F5fm, I4, I5fir, Pi4, Pi4fm. Course coordinator: Director of studies, Anna Lindgren,, Mathematical Statistics. Prerequisites: FMS045/FMSF10 Stationary stochastic processes. Recommended prerequisits: MIO140 Financial Management and preferrably also one or several of FMS051 Time series analysis, TEK180 Financial Valuation and Risk Management, and FMS170 Valuation of Derivative Assets. The course might be cancelled if the number of applicants is less than 16. Assessment: Written report and oral presentation of a larger project and compulsory computer exercises. The course grade is based on the project grade. Further information: The course is also given at the faculty of science with the code MASM18. Home page:

The course should be regarded as the statistical part of a course package also including TEK180 Financial Valuation and Risk Management and FMS170 Valuation of Derivative Assets. Its purpose is to give the student tools for constructing models for risk valuation and pricing, based on data.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
For a passing grade the student must

The course deals with model building and estimation in non-linear dynamic stochastic models for financial systems. The models can have continuous or discrete time and the model building concerns determining the model structure as well as estimating possible parameters. Common model classes are, e.g., GARCH models with discrete time or models based on stochastic differential equations in continuous time. The course participants will also meet statistical methods, such as Maximum-likelihood and (generalised) moment methods for parameter estimation, kernel estimation techniques, non-linear filters for filtering and prediction, and particle filter methods.

The course also discusses prediction, optimization, and risk evaluation for systems based on such descriptions.

Madsen, H, Nielsen, J N, Lindström, E, Baadsgaard, M & Holst, J: Statistics in Finance. IMM, DTU, Lyngby and KFSigma, Lund 2006.