As an undergraduate, I studied Business and Economics and completed my masters and PhD degrees in finance at the University of Basel. Near the end of my PhD program, I began working part-time as a quantitative analyst before gaining a full-time position on the market risk team at Zurich Cantonal Bank, which is where I am now.
One of main draws of the CQF was to see what particular models are needed for particular situations and how they are actually used in industry. By the end of the course, I was able to get a better understanding of the strengths and weaknesses of various models and what the fallbacks are.
After a short period of time, I changed teams to cover the market risk analysis of our equity and derivatives trading desks. It was clear that while I had a substantial academic background, further in-depth study of the models used in trading would be helpful and the CQF seemed to be a great opportunity. From the primers at the beginning and throughout the program, it was essential to review the math used in quantitative finance; although it was like the math I did at the beginning of my PhD program, the CQF was a good refresher and focused on teaching math in an applied and practical way. One of main draws of the CQF was to see what particular models are needed for particular situations and how they are actually used in industry. By the end of the course, I was able to get a better understanding of the strengths and weaknesses of various models and what the fallbacks are.
I did not expect or anticipate being awarded First in Class, but it is satisfying to know the hard work and time spent paid off in this way.
The machine learning component of the program was also really attractive to me. It was all fairly new territory since the integration of machine learning was still in the early stages during my studies. The CQF covered a wide range of topics, and the material was very dense. These days, it has been useful for me to go back and see how a certain model or application might work for a particular problem or context. For my final project, for example, I worked on an optimal hedging question, and it was directly relevant to my work on the equity and derivative desk. I understood what was going on and it was interesting to see how you can take different approaches to finding solutions. At work, I was able to talk through my ideas with the traders and I learned a lot. Additionally, discussions of the weekly lecture topics within the cohort and my peers were very insightful. Overall, I’d say that the CQF is a good combination of structured guidance and rigorous work that you complete independently. I did not expect or anticipate being awarded First in Class, but it is satisfying to know the hard work and time spent paid off in this way.
Looking ahead, I am in a professional talent development program at the bank and am in discussion for another promotion by the beginning of next year. I found that the CQF was like a personal knowledge course and the talent development program through the bank has provided me with an opportunity to focus on my soft skills, so they played nicely together. I like the desk I am on, but in the future, I hope to expand my opportunities in different roles and departments, for instance moving into trading seems like an intriguing prospect.
My advice to new CQF delegates would be: Do not underestimate the workload because it is quite heavy and a substantial amount of time going over the material is necessary. Like the majority of the CQF delegates, I worked full time and for six months, I literally came home from work, had dinner, and went right into the online classes. The modules go very quickly and it all interconnects. You will finish a module in four weeks or so—you will have learned so much—and then you will see that it feeds directly into the next module’s requirements. Once you finish the program, there may be some subjects you forget over time, but you can always come back to the course content via the Lifelong Learning library when you have questions. I found many times that I could take the original lectures as a starting point, review which elements were most important, and then take a deeper dive with reading and experimenting from there. Naturally, when the course was still going on, I was too busy to look at everything, but during the project I was able to sample a range of lectures as I developed my research, and they were helpful. It’s another resource that will stay with you long after your final project is done.
Find out more about the CQF program
The CQF has been chosen by thousands of ambitious professionals, like Patrick, to master the practical quant finance and machine learning techniques used in industry today. If you are ready to gain the skills you need to progress your career, download the CQF brochure or register for a CQF information session today.