I knew my internship at DRW was going to come with a lot of unexpected challenges, but that’s what made me most excited. I’ve always had an interest in quantitative finance, so working at a trading firm like DRW was the perfect fit. During my time here, I’ve been solving real-world problems, which means the work I’m doing is really valuable.
I’ve focused on using deep learning to predict yield curve dynamics over different conditions. A traditional approach to this problem is principal component analysis (PCA), a statistical procedure. By implementing PCA I was able to establish a baseline for my project. Since recurrent neural networks can “learn” more complicated patterns in the data sets, I was able to observe and compare the accuracy and predictive power of artificial neural networks versus PCA.
Another highlight during the summer is a project on the analysis and development of a new local-stochastic volatility model for OTC and listed IR options. The implementation of the model could provide faster and more accurate option pricing.
I’ve learned a lot of new practical skills and I’ve learned about more niche areas like neural networks, interest rates modeling, stochastic calculus and more. My understanding of theoretical concepts from school have now developed into an ability to implement those models and solve real-world problems. I know I have many more tangible skills I can bring back to my studies at Rutgers and throughout all my career.