First of all, since this is my first post, hi to everyone.
I'm in my senior year of an undergrad "pure" math degree, with a minor in physics. I also have a part-time job doing research at a machine learning lab on campus. I have had a couple of upper-level stat classes and a course in corporate finance and I know how to program reasonably well in C++.
For about a year I have been considering going to grad school for financial math with the ultimate goal of working in quantitative finance. However, what I read on quantnet, nuclear phynance and so forth makes me wary of pursuing such a specific degree. A number of people seem concerned for the future of financial markets in general, expressing apprehension about the economy, regulation, and the obsolescence of entire methodologies. One grad program I investigated (it may have been Stony Brook?) contained something of a cautionary note on its page for prospective students, in fact--something that surprised me, though I guess for a competitive program that practice might not hurt them.
As a result, I'm very close to changing my tune. I could just go for a degree in applied math and attempt to work some of the QF-relevant topics into my degree as a way of hedging my bet. But I've also heard that times are changing, in the sense that since specialized QF programs are available now, applicants from (for instance) a physics background have lost some of the edge they once had. If that's really the case, then a PhD in applied math might not leave my options open.
I don't have much time to decide. A few weeks ago all of these concerns led me to the conclusion that if I have doubts about a financial math PhD program and my future with it, I shouldn't undertake it. On the other hand, certainly I don't want to live my life in academia, either, which a degree in applied math could lead to by accident.
Maybe I should just start taking SOA/CAS exams...
I'm in my senior year of an undergrad "pure" math degree, with a minor in physics. I also have a part-time job doing research at a machine learning lab on campus. I have had a couple of upper-level stat classes and a course in corporate finance and I know how to program reasonably well in C++.
For about a year I have been considering going to grad school for financial math with the ultimate goal of working in quantitative finance. However, what I read on quantnet, nuclear phynance and so forth makes me wary of pursuing such a specific degree. A number of people seem concerned for the future of financial markets in general, expressing apprehension about the economy, regulation, and the obsolescence of entire methodologies. One grad program I investigated (it may have been Stony Brook?) contained something of a cautionary note on its page for prospective students, in fact--something that surprised me, though I guess for a competitive program that practice might not hurt them.
As a result, I'm very close to changing my tune. I could just go for a degree in applied math and attempt to work some of the QF-relevant topics into my degree as a way of hedging my bet. But I've also heard that times are changing, in the sense that since specialized QF programs are available now, applicants from (for instance) a physics background have lost some of the edge they once had. If that's really the case, then a PhD in applied math might not leave my options open.
I don't have much time to decide. A few weeks ago all of these concerns led me to the conclusion that if I have doubts about a financial math PhD program and my future with it, I shouldn't undertake it. On the other hand, certainly I don't want to live my life in academia, either, which a degree in applied math could lead to by accident.
Maybe I should just start taking SOA/CAS exams...