Financial Econometric Time Series Analysis (FinMetrics)

Course content

The course gives an introduction to the mathematical statistical and econometric analysis of time series data with emphasis on financial time series data. Various applications are considered, e.g. risk management, forecasting and derivatives pricing.

Specifically we consider univariate linear time series models for the conditional mean (autoregressive processes, or AR) and nonlinear time series models for higher order moments including autoregressive conditional heteroskedastic (ARCH) models. In addition, multivariate extensions are considered, including vector autoregressive (VAR) models, and multivariate ARCH (MGARCH) models.

The stochastic properties of the processes are analyzed in detail in terms of stationarity and dependence. Statistical analysis is likelihood-based, and we consider asymptotic theory for estimators and test-statistics.

The modeling is illustrated empirically using standard statistical software.


MSc Programme in Mathematics-Economics

MSc Programme in Statistics

MSc Programme in Actuarial Mathematics

Learning outcome


  • Account for properties of stochastic processes used for financial time series modelling. This includes strict stationarity, mixing, and geometric ergodicity.
  • Account for properties of maximum likelihood estimators and test-statistics.



  • Analyze stochastic properties of time series proceses.
  • Establish likelihood-based estimators asymptotic distributions, and clarify under what conditions properties hold.
  • Implement estimation of financial time series models in statistical software.
  • Discuss the suitability of a given time series models for typical finanical empirical applications.


  • Apply the acquired knowledge and skills in new contexts. For example the student should be able to analyze richer classes of models and carry out estimation of these.
  • Ability to read leading and novel journal articles within financial econometrics.

4 hours of lectures and 2 hours of exercises per week for 7 weeks.

Literature will be announced in Absalon.

MStat, Sand2 and Finansering 1.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
On-site written exam, 3 hours under invigilation
Exam registration requirements

Two mandatory written assignments (mid and final term tests) must be handed in and approved.

Without aids

The exam is closed book.

Marking scale
7-point grading scale
Censorship form
External censorship

30 minutes oral exam, no preparation time and no aids.

Mandatory assignment(s) that have not been approved or are invalid must be handed in no later than three weeks before the start of the re-exam period and approved.

Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 136
  • Theory exercises
  • 14
  • Project work
  • 25
  • Exam
  • 3
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Anders Rahbek   (13-69766c6d7a7b367a69706a6d73486d6b777636737d366c73)

Anders Rahbek
Rasmus Søndergaard Pedersen
Frederik Vilandt Rasmussen

Saved on the 14-02-2024

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