Financial Statistics



The preliminary course programme for fall 2017 can later be found here:

There will be four (six) seminars for you to present the take home exam/project. These are December 20th (10.15-12 in MH:332b), December 22nd (10.15-12 in MH:229, 13.15-15 in MH:228), January 9th (10.15-12 in MH:227, 13.15-15 in MH:227) and January 13th (10.15-12 in MH:227). The seminars on December 20th and January 9th will use data set A, the seminars on December 22nd and January 13th will use data set B. You can sign up for the seminars via the doodle The data needed is StockReturnA.mat, CreditDataA.mat, CreditReadMeA.txt, or StockReturnB.mat, CreditDataB.mat, CreditReadMeB.txt,



  • Exercise 5: 14.1, 14.2
  • Exercise 4: Solve assignments 13.1, 13.2, 13.3
  • Exercise 3: Solve assignments 7.3, 8.2, 8.5, 8.6 and 8.7, (and if you have time 7.1, 8.9)
  • Exercise 2: Solve assignments 5.5, 5.6 (and if you have time 5.4)
  • Exercise 1: Solve assignments 4.5, and 4.1 (if you have time).

Computer exercises

MLmax help: You can find a demo file and demo log-likelihood function.
  • Computer Exercise 4 can be found here: Financial Statistics lab 4 Data files needed are
  • Computer Exercise 3 can be found here: Financial Statistics lab 3. Data files needed are
  • Computer Exercise 2 can be found here: Financial Statistics lab 2. Data files needed are You may also want to use extra the Matlab routines ccc_mvgarch.p and ccc_mvgarch.m
  • Computer exercise 1 can be found here: Financial Statistics lab 1 You need some additional files: Sign up for the computer exercise via SAM
  • Course Coordinator/Lecturer

    Erik Lindström, tel 046-222 45 78, MH:221.

    Teaching assistant

    Carl Åkerlindh

    Sidi Mohamed Aly,

    Course secretary

    Maria Lövgren

    General information

    University credits: 7.5 Grading scale: TH Level: A
    Language of Instruction: The Course will be given in English

    Lectures: Monday 8.15-10.00, MH:309a or MH:227, Tuesday 10.15-12.00 MH:362d
    Exercise: Thursday 10.15-12.00 MH:362d
    Computer exercises: varies

    The course starts November 3rd.

    Computer exercises


    The course should be regarded as the statistical part of a course package also including TEK180 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:
    • handle variance models such as the GARCH family, stochastic volatility, and models use for high-frequency data,
    • use basic tool from stochastic calculus: Ito'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.

    Skills and Abilities

    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 economic 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.


    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, optimisation, and risk evaluation for systems based on such descriptions.

    Formal required Qualifications

    FMSF10/FMS045 Stationary stochastic processes or corresponding courses.

    Assume Qualifications

    Preferably also one or several of MIO140 Financial Management, FMS051 Time series analysis, TEK180 Financial Valuation and Risk Management, and FMSN25MASM24 Valuation of Derivative Assets.


    Lindstrom, E., Madsen, H and Nielsen, J. N. (2015) , Statistics for Finance CRC Press/Chapman Hall
    Additional handouts on the home page
    Errata can be found here


    Written report and oral presentation of a larger project and compulsory computer exercises. The course grade is based on the project grade.


Questions: Erik Lindström Top of page
Last modified: Wed Jun 19 17:32:12 CET 2017 Validate: HTML CSS

Course Start

Introductory Meeting:
2017-10-30, 10:15

Approximate start date:
Oct, 30, 2017

Reading periods:


Erik Lindström Assistants: Carl Åkerlindh


  • Stand-alone Courses
  • Engineering Physics
  • Industrial Engineering and Management
  • Master's Program in Mathematical Statistics
  • Engineering Mathematics
Centre for Mathematical Sciences, Box 118, SE-22100, Lund. Telefon: +46 46-222 00 00 (vx)