Do Financial Engineering PhD Students Engage in a Lot of Computer Coding?
The world of financial engineering is vast and multifaceted, encompassing a range of academically rigorous programs. While many delve into the intricacies of financial economics at the PhD level, a specialized subset explores financial engineering.
Financial engineering programs, as opposed to those in financial economics, are more focused on the practical application of advanced mathematical and statistical techniques to solve real-world financial problems. These programs typically offer both Master's and PhD degrees, with the PhD being a more advanced and research-oriented stage of the educational journey.
Contrasting Financial Engineering and Financial Economics PhDs
It is common for students to pursue a PhD in financial engineering, especially those interested in the intersection of mathematics, statistics, and finance. However, it is important to note that the majority of financial economics PhD programs exist, catering to a broader audience with a wider range of interests and academic pursuits.
The distinction between the two fields can be subtle, but the focus of financial engineering usually leans towards the practical application of concepts. This often means a greater emphasis on the development and implementation of algorithms and models, as well as a wealth of empirical research.
PhD Programs in Financial Engineering
Financial engineering PhD programs are typically Master's degrees, focusing on advanced coursework and research training. The diversity of these programs means that they accommodate various levels of theoretical versus empirical work.
For students who wish to engage in pure theoretical research, coding is not a requirement. However, for those pursuing empirical work, coding is often a critical skill. Modern statistical packages and software tools have made coding less essential for some tasks, but proficiency in coding remains valuable for handling more complex analyses and simulations.
Empirical Research and Programming Involvement
Many financial engineering PhD students conduct empirical research, which heavily relies on coding skills. They use statistical packages such as Excel, R, and Stata to perform quantitative analysis. For instance, Excel is commonly used for maintaining and manipulating large datasets, while R and Stata are more specialized for statistical modeling and analysis.
In addition, students may also write custom programs for specific tasks. My experience in writing empirical papers involved using Excel to store and manage my data, while Stata was the go-to tool for conducting complex statistical analyses. I also wrote several programs using the C language, which was particularly useful for tasks such as finding the values of exotic options, a critical aspect of financial engineering.
Challenges and Opportunities in Financial Engineering PhD Programs
The challenges faced by financial engineering PhD students include mastering a wide range of technical skills, including coding, mathematical modeling, and financial analysis. The opportunity, however, lies in the flexibility of the programs, which allow for a diverse set of research projects and practical applications.
Ultimately, whether a financial engineering PhD student engages in a lot of coding depends on the research focus and the specific requirements of their dissertation. While not all financial engineering PhD students require extensive coding, it is increasingly becoming a necessary skill for those who wish to contribute to the field with innovative and practical solutions.
Keywords: Financial Engineering, PhD Coding, Financial Economics