Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED I
Date of Decision: 2024-04-16
Effective: 2024-05-08
Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective for: F5, F5-fm, I5-fir, Pi4-fm, R5
Language of instruction: The course will be given in English
The course should be regarded as the statistical part of a course package also including EXTQ35 Financial Valuation and Risk Management and FMSN25 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
Competences and skills
For a passing grade the student must
Judgement and approach
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.
Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment: Written report and oral presentation of a larger project and compulsory computer exercises.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Modules
Code: 0117. Name: Project Work.
Credits: 4.5. Grading scale: TH - (U, 3, 4, 5).
Assessment: Written and oral project presentation
Code: 0217. Name: Laboratory Part 1.
Credits: 1.5. Grading scale: UG - (U, G).
Assessment: Computer exercise 1 and 2
Code: 0317. Name: Laboratory Part 2.
Credits: 1.5. Grading scale: UG - (U, G).
Assessment: Computer exercise 3 and 4
Admission requirements:
Assumed prior knowledge: EXTF45 Financial Management and preferrably also one or several of FMSN45 Time series analysis, TEK180/EXTQ35 Financial Valuation and Risk Management, and FMSN25 Valuation of Derivative Assets.
Director of studies: Johan Lindström,
studierektor@matstat.lu.se
Course administrator: Susann Nordqvist,
expedition@matstat.lu.se
Course homepage: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram/