Are you about to write your master’s thesis or a similar project and interested in exploring the exciting area of generative models for high-dimensional financial time series? Do you want to develop novel solutions to the risk modeling challenges that investment professionals experience in practice? Then this unique research collaboration opportunity might be perfect for you.

Investment risk modeling at a glance

Investment risk modeling is essentially about generating joint paths for a large panel of risk factors, e.g., interest rates, credit spreads, implied volatilities, FX rates, and equity returns. The dimensionality of the problem poses a challenge in itself, but it is particularly challenging to accurately capture both the time series and cross-sectional properties of the risk factors (some of which are observed with different frequencies).

Generative modeling of financial time series project

Increased computational power and large-scale machine learning methods might offer elegant solutions to the above risk modeling challenges, and this is the main topic of the research collaboration. We offer suggestions for concrete problem formulations depending on the number of ECTS credits that you have available, which can be anything from 10 to 60 ECTS.

What’s in it for you?

You will get to apply your theoretical knowledge to practical investment risk modeling problems in collaboration with experienced investment professionals and thus be faced with the nuances that are inherent to real-world risk modeling. We will also make computational resources available to you if necessary.

Who we are

Fortitudo Technologies is a fintech company offering novel investment software as well as quantitative and digitalization consultancy to the investment management industry.

Requirements

• You hold a BSc and are pursuing an MSc in applied mathematics, mathematics, mathematics-economics, engineering, computer science, or similar

• You have achieved excellent academic results and specialize in machine learning, statistical modeling, and data science

• Python programming experience including the most common data science and machine learning packages (SciPy Stack and TensorFlow). If you don’t satisfy this requirement, you should be willing to put in extra work to catch up before and possibly during the project

  
  

How to apply

• Send a link to your LinkedIn and your university grades transcript in PDF format to writing ‘Research project collaboration’ in the subject line no later than the 18th of June

• Interviews will be conducted on a rolling basis, so please apply at your earliest convenience

• If you have any questions, please contact our team below.