Course syllabus

Finansiell statistik Financial Statistics

FMSN60, 7,5 credits, A (Second Cycle)

Valid for: 2021/22
Faculty: Faculty of Engineering, LTH
Decided by: PLED I
Date of Decision: 2021-04-21

General Information

Elective for: F5, F5-fm, I5-fir, Pi4-fm
Language of instruction: The course will be given in English

Aim

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.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

• handle variance models such as the GARCH family, stochastic volatility, and models used for high-frequency data,
• use basic tools from stochastic calculus: Itô's formula, Girsanov transformation, martingales, Markov processes, filtering,
• use tools for filtering of latent processes, such as Kalman filters and particle filters,
• statistically validate models from some of the above model families.

Competences and skills
For a passing grade the student must

• be able to find suitable stochastic models for financial data,
• work with stochastic calculus for pricing of financial contracts and for transforming models so that data becomes suitable for stochastic modelling,
• understand when and how filtering methods should be applied,
• validate a chosen model in relative and absolute terms,
• solve all parts of a modelling problem using financial and statistical theory (from this course and from other courses) where the solution includes model specification, inference, and model choice,
• present the solution in a written technical report, as well as orally,
• utilise scientific articles within the field and related fields.

Contents

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.

Examination details

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.

Parts
Code: 0117. Name: Project Work.
Credits: 4,5. Grading scale: TH. Assessment: Written and oral project presentation
Code: 0217. Name: Laboratory Part 1.
Credits: 1,5. Grading scale: UG. Assessment: Computer exercise 1 and 2
Code: 0317. Name: Laboratory Part 2.
Credits: 1,5. Grading scale: UG. Assessment: Computer exercise 3 and 4

• FMSF10 Stationary Stochastic Processes or FMSF15 Markov Processes

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.
The number of participants is limited to: No
The course overlaps following course/s: FMS161, MASM18