I have a background in applied mathematics and actuarial science. My first job experience was in the life insurance business, where I worked as part of my actuarial thesis on modeling the investor behavior in a stressed market environment. I learned a lot there, but I was more attracted by the asset side of the job, which is why I joined the investment management industry. I always found it fascinating to have portfolio figures moving along with current events, and optimize portfolios to take most of what you think is upcoming. Also, I like the challenge of taking active decisions based on your forecasting abilities.
At the beginning, I learned everything I know in finance by working in the financial sector, and went after a few years for the FRM certification. It gave me a good overview on the risks and challenges the banking industry faces, but I was interested in going further. For a moment, I considered going for a PhD until I heard of the CQF from a friend. I looked at the curriculum and it was fantastic. What I immediately liked when I read it was the willingness to get to the bottom of each financial model. I was also excited about the machine learning and artificial intelligence modules, as I was keen to catch up and have the proper training as I mostly learned that part by myself. In the meantime, I joined a central bank, as I was keen to understand better what drives monetary policy decisions, more particularly in the context of Covid-19 pandemic – and now booming inflation, where central banks played a key role.
The machine learning and artificial intelligence modules were ideal for me to progress. You can teach yourself for a while, but at some point you reach your limits and it is very good to have insights from real experts. From that angle, I have the feeling that the CQF took me to a different level.
Putting aside my background, I also have the feeling that I really enjoyed the CQF because of my professional experience. Once you traded options or built a hedging or absolute return strategy, it is much easier to grasp all concepts and models explained in the program, as you know how these instruments work in practice, despite looking very theoretical at first sight. In addition, the machine learning and artificial intelligence modules were ideal for me to progress. You can teach yourself for a while, but at some point you reach your limits and it is very good to have insights from real experts. From that angle, I have the feeling that the CQF took me to a different level. Of course, when I thought of a trading idea or a risk modeling idea prior to the CQF, I could implement a workable solution. But the way that we were taught to code during the CQF was so clear and structured that I’ve become a much better coder now. More generally, I really enjoyed the continuous practical insight on some theoretical concepts all along the program.
For incoming delegates, I would say that every module is essential, as they are all building blocks that fit together, and it goes amazingly fast. With that in mind, the key thing is to keep up with the momentum and study thoroughly before and after each lecture. It is very important to dedicate the right amount of time to your studies every week, so that you can keep up. It takes a lot of work and dedication to get through the CQF program, but the learning curve is so steep that it keeps you motivated along the way. Every bit of the CQF must be taken as a way to test your understanding and skills: one of the lecture provides a code in R, see if you can do it in Python. Practice is key.
Finally, the CQF lays down the foundations of all models in finance. The program is too short to cover all aspects of quantitative finance, but it gives you all the tools to get most of unseen models quite quickly. From my point of view, this is where the real part of the journey starts, work by yourself things and models that are of your interest. From that perspective, the CQF continues to provide support, with books, lifelong learning and conferences, which is great.