My story
My background in business, while providing a strong foundation in finance and economics, presented a unique challenge when I began my PhD. The sophisticated statistical theories underpinning financial econometrics proved to be a steep learning curve, even more so coming from an undergraduate program that didn't emphasize the deeper mathematical underpinnings. I vividly recall the initial struggle to master these concepts. It demanded a significant investment of time and effort. In fact, I dedicated a substantial portion of my early PhD years to immersing myself in lecture notes, textbooks, and research papers. I also actively sought out additional coursework, both on campus and online, to solidify my understanding. This wasn't simply about memorizing formulas; it was about developing a deep, intuitive grasp of the underlying mathematical principles, particularly in areas like mathematical analysis and linear algebra, which are often less emphasized in business-focused curricula. It was a demanding, sometimes frustrating, but ultimately rewarding process. Now, as a fourth-year PhD candidate, I can confidently say that I've made significant progress in mastering these statistical foundations, equipping me to tackle the complex challenges of my research.
Useful Books
Understand Analysis, Stephen Abbott.
Principles of Mathematical Analysis, Rudin, Walter.
Useful Lectures
Useful Lecture Notes
The Econometrics of High Frequency Data, Per. A. Mykland and Lan Zhang.