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Importance of math rigorousness and other questions
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<blockquote data-quote="Qui-Gon" data-source="post: 280000" data-attributes="member: 43439"><p>No MFE program “requires” prior coursework in real analysis or measure theory, to my knowledge Columbia’s MAFN is the only top program that even mentions measure theory on their website. In many cases, learning stochastic calculus is much easier if you’re comfortable with analysis — this is of course not to say one needs to have such comfort to learn sto cal, but in my experience it makes the process far more enriching. If you know how to construct and have worked with Riemann-Stieltjes integrals (standard in a Real Analysis I course), then Itô integrals will seem relatively tame to you.</p><p></p><p>Let me say this first: PhDs in finance are not failed academics, far from it. Check this out: <a href="https://sig.com/team/meet-quant-intern/" target="_blank">Meet a Quantitative Research Intern | SIG</a>. PhDs bring value because they are at the cutting edge of their domain — while MFEs do learn advanced math and stats, it is only the tip of the iceberg and PhD students focused exclusively on such fields will be far more familiar with current research and their knowledge will be specialized. What if they’re lacking a programming background and want to break into quantitative finance? They do as they surely have had to many times over during their PhD, they self-teach themselves! While I am only a masters student in pure math, this was very much the situation I was in. The introductory C++ course hosted here on QN is fantastic, and is perfectly suited to be the driving force behind learning C++ in a self-guided way.</p></blockquote><p></p>
[QUOTE="Qui-Gon, post: 280000, member: 43439"] No MFE program “requires” prior coursework in real analysis or measure theory, to my knowledge Columbia’s MAFN is the only top program that even mentions measure theory on their website. In many cases, learning stochastic calculus is much easier if you’re comfortable with analysis — this is of course not to say one needs to have such comfort to learn sto cal, but in my experience it makes the process far more enriching. If you know how to construct and have worked with Riemann-Stieltjes integrals (standard in a Real Analysis I course), then Itô integrals will seem relatively tame to you. Let me say this first: PhDs in finance are not failed academics, far from it. Check this out: [URL='https://sig.com/team/meet-quant-intern/']Meet a Quantitative Research Intern | SIG[/URL]. PhDs bring value because they are at the cutting edge of their domain — while MFEs do learn advanced math and stats, it is only the tip of the iceberg and PhD students focused exclusively on such fields will be far more familiar with current research and their knowledge will be specialized. What if they’re lacking a programming background and want to break into quantitative finance? They do as they surely have had to many times over during their PhD, they self-teach themselves! While I am only a masters student in pure math, this was very much the situation I was in. The introductory C++ course hosted here on QN is fantastic, and is perfectly suited to be the driving force behind learning C++ in a self-guided way. [/QUOTE]
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Importance of math rigorousness and other questions
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