Primary tabs

Short Course
on
Copulas with Applications in Finance
The financial and other industries currently have a shortage of people with the necessary data science skills to address problems relating to large and/or complex data sets. Nowadays financial institutions are confronted with an abundance of data, and so in order to maintain a competitive advantage it is imperative to have analysts with these necessary data science skills. This short course will address one of the topics needed to become a skilful data scientist.

Purpose of the course:

This short course aims to equip participants with knowledge of the necessary underlying theoretical concepts relating to copulas required to effectively address real-world problems that arise in the financial sector. As a result, the participant will be able to model the dependence structures between financial variables and thereby negating the need to use the naïve assumption of independence that is often used.

Admission requirements:

Admission requirements: 
NQF level 7 majoring in either Mathematics/Statistics/Engineering/Computer Science
Learning assumed to be in place: 
NQF level 7

Course outcomes and assessment criteria :

Course outcomes and the associated assessment criteria: 

Study Unit

Outcomes

Assessment Criteria

  After completion of this course, participants will: Participant will be assessed on the following criteria:
 
  • Demonstrate applied knowledge and understanding of copulas and understand how they are applied in practice.
  • Demonstrate an ability to identify real-world practical problems that can be solved using copulas and how to apply them to solve these problems.
  • Demonstrate an understanding of the complexities and uncertainties of selecting and applying appropriate copula procedures or techniques to unfamiliar problems in a financial setting
  • Critically discuss and explain the necessary theoretical results relating to copulas, specifically Sklar’s Theorem, Frechet-Hoeffding bounds, methods of constructing copulas, different family of copulas, copula-based dependent measures and parametric, semiparametric and nonparametric estimation of copulas.
  • Critically discuss the most widely used copula classes, their corresponding sampling procedures, along with selected copula transformations that are important for practical purposes.
  • Explain the use of copulas to model the tail dependence between variables.
  • Implement copulas in financial applications such as credit scoring, asset return modelling and value-at-risk analysis.
  • Implement graphical diagnostics, statistical tests and model selection for copulas.

 

Assessment: 
Assignments and participation
Method of assessment: 
Learning objectives will be accomplished through the successful completion of assignments and participation in short course activities, proving insight into the topics at hand.

Additional information

Mode of delivery: 
Mixed
Target group: 
Graduates and/or industry professionals working in fields that require data science and analytical skills.
Contact us
Name: 
Me M Cockeran
Telephone number: 
018 299 2552