On becoming a Quant
Mark Joshi, our friend and colleague, sadly died in October of 2017 but his short guide "On becoming a Quant" has consistently been one of the most popular articles here at MoneyScience. After the success of the article below - he went on to collaborate with Nicholas Denson and Andrew Downes to produce a book, "Quant Job Interview Questions and Answers" which you can find on Amazon.com and Amazon.co.uk.
What sorts of quant are there?
(1) Front office/desk quant
(2) Model validating quant
(3) Research quant
(4) Quant developer
(5) Statistical arbitrage quant
(6) Capital quant
A desk quant implements pricing models directly used by traders.Main plusses close to the money and opportunities to move into trading. Minuses can be stressful and depending on the outfit may not involve much research.
A model validation quant independently implements pricing models in order to check that front office models are correct. Plusses more relaxed, less stressful. Minusses model validation teams can be uninspired and far from the money.
A Research quant tries to invent new pricing approaches and sometimes carries out blue-sky research. Plusses it's interesting and you learn a lot more. Minusses sometimes hard to justify your existence.
A Quant developer, a glorifed programmer but well-paid and easier to find a job. This sort of job can vary a lot. It could be coding scripts quickly all the time, or working on a large system debugging someone else's code.
A Statistical arbitrage quant, works on finding patterns in data to suggest automated trades. The techniques are quite different from those in derivatives pricing. This sort of job is most commonly found in hedge funds. The return on this type of position is highly volatile!
A capital quant works on modelling the bank's credit exposures and capital requirements. This is less sexy than derivatives pricing but is becoming more and more important with the advent of the Basel II banking accord. You can expect decent (but not great) pay, less stress and more sensible hours. There is currently a drive to mathematically model the chance of operational losses through fraud etc, with mixed degrees of success.
People do banking for the money, and you tend to get paid more the closer you are to where the money is being made. This translates into a sort of snobbery where those close to the money look down on those who aren't. As a general rule, moving away from the money is easy, moving towards it is hard.
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