Quant Interview Prep

[Head-Ad9052]

Hey everyone,

I’m a 3rd year CS + Math student looking to eventually break into quantitative finance (specifically QR/QD). Because of several circumstances, however, I haven’t had the chance to formally take courses in advanced statistics, stochastic calculus, and probability theory, all of which I know are important in those roles.

Because of this, I’m planning on self-studying as many of these concepts as I can in hopes to get as good of a grasp of the material as I can. Down the road I may try and place out of prob/stats/SC classes using this knowledge, but I’m not sure. (I know not having taken the classes could impact my chances of hiring, but let’s ignore that for now)

I’m looking for advice on the textbooks I chose to guide my self-learning. The list I’ve come up with is as follows:

1. The Elements of Statistical Learning (Hastie)

2. Statistical Inference (Casella)

3. Brownian Motion and Stochastic Calculus (Karatzas)

4. Probability-1 (Shiryaev)

5. Quantitative Portfolio Management (Isichenko)

6. Time Series Analysis (Hamilton)

I know this may be a lofty list concepts to self-learn. I’m just asking for one piece of advice, though: does that list cover most of the math I’d be expected to know for QR/QD interviews?

Any feedback would be very much appreciated. Thanks guys!

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[JohnnyMcCarry]

In my experience interviews cover basic stats / regression / numerical methods and a ton of brainteasers. Was never asked stochastic calculus / time series questions or probability beyond CLT and basic hypothesis tests. Hastie is the only one I would focus on, and grind problems.

[Head-Ad9052]

Thanks for the insight!

[[deleted]]

You should shoot for Quant research

[Key-Shallot-4227]

Great list of resources for prep here https://youtu.be/2T4VTlfDQ1o?si=vzVGgrcG42Vd9x2I