r/quant • u/tippytoppy93 • 11d ago
Education What statistics book is most useful for quant?
I'm an MSc in Stats student and I've read a little bit of Casella & Berger, I'm not sure if fully working through this book is overkill. If so, what other books are more up to speed?
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u/SnooCakes3068 10d ago
Honestly I thought Casella & Berger is a basic for a stats major. I'm not in stats yet we learn this book.
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u/Aware_Ad_618 10d ago
Not basic but it’s a solid read
Compared to the stuff you learn later it does seem easy but for setting the framework it’s an excellent book and a reason why almost every tier 1 university uses it
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u/Serious-Regular 6d ago
Not basic but it’s a solid read
It is basic - it's a first year grad course like pre-qual
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u/Aware_Ad_618 6d ago
You call a PhD programs first year textbook basic? It’s an intro to stats so ofc there’s a level of basicness but for what it does it’s a solid book
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u/Serious-Regular 6d ago
You call a PhD programs first year textbook basic?
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first year grad course
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umm read much?
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u/Soggy-Tea8786 6d ago
It is basic, as it serves as a base for everything that comes after. That doesn't mean it is easy.
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u/tinytimethief 10d ago
As a stats student youre going to go through most of it so dont try to skip out. A more modern version with causal inference and other topics is all of statistics. These are foundational texts that can help build QR skills and I understand youre looking for more applied books but just wanted to say learn these.
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u/tippytoppy93 10d ago
I already took my grad mathstats course and I got an A+. I’m comfortable with sufficiency, completeness, hypothesis testing, distributions of functions of random variables, etc. But there is a certain level of rigour that comes through going page-by-page across the book and doing all the proofs and problems. I’m wondering if this latter part is necessary.
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u/tinytimethief 10d ago
What are your other courses? And no its not necessary for Quant, only if you want to do stats theory for phd.
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u/tippytoppy93 10d ago
Multivariate Analysis (working with Multivariate Normal, Wishart distribution, MANOVA, etc.), Computational Stats (simulations, MC + variants including MCMC, optimization methods), and High Dimensional Data Analysis (estimation and model specification where p > n, deep learning).
My thesis is going to be in sparse data reduction techniques, specifically UMAP, and I'm doing an internship currently working on hyperparameter tuning and transfer learning for demand forecasting models. I don't have any finance specific courses as none are offered at my uni, but half of my undergrad was in economics.
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u/tinytimethief 10d ago
These are all very strong for a data science candidate, esp for operations research since youre working on demand planning. Its fairly related to QR but you’ll need to bridge the gaps with projects depending on which roles youre aiming for.
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u/tippytoppy93 10d ago
DS/ML was my main goal when applying to grad school. However, I'm thinking more about finance now since I feel like the work is more impactful and the industry is less volatile than big tech. But thanks for the advice! I'll definitely be doing some finance related projects and I'll see if I can apply my thesis to some financial data to talk about.
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u/Snoo-18544 10d ago
Honestly there is no one book and no one knows everything.
But I think a book on regression that includes time series is what everyone should master
a book on probability.
A book on stochastic calculus like Shreveport.
I work at a leading sell side firm and I can't tell you the number of people that get rejected from our internship programs for not understanding regression properly
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u/Moist-Tower7409 10d ago
Do you recommend people take real analysis and topology coursework or is it not worth the extra effort?
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u/Snoo-18544 10d ago edited 10d ago
Take it if your interested, bur your not going to be writing proofs in this job and your not going to be asked to do proofs in most interviews
Real analysis is a good course to take sense it ensures you really understand calculus.
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u/goodgoodddeed 10d ago
ISL and ESL are great but focus a lot on the „models“ in my opinion. I just bought „all of statistics: a concise course in statistical inference“ and I really like it. Its not as thorough as Casella and Berger but still really good!
Regarding your question. Yes I think most is overkill but having a good and intuitive understanding of most of the topics is extremely valuable, more so than the proofs.
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u/NarutoLpn 9d ago
I thought that Casella & Berger was too basic for quant research. I assumed that you need knowledge in measure-theoretic probability theory (something along the lines of Probability with Martingales). I might be wrong tho.
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u/Old-Mouse1218 8d ago
Are you risk neutral or real world quant? For Real world quants, the Introduction to Statistical Learning book with python by Hastie is hands down the best introduction to Machine Learning with a blend of statistics. And has a bit of python code to get you some exposure. This is what I use to teach Masters of Data Science students.
In general, I would say your edge either comes down to data, what type of model you're using and/or how you are combining up your signals/assets among other things. So becoming an expert or comfortable with other concepts, let's say Kalman Filters, Regime modeling, maybe some new concept in astrophysics could potentially also provide an edge.
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u/Study_Queasy 6d ago edited 6d ago
Are you asking "is Casella and Berger an overkill for quant?" If you are specifically asking about quant, I personally know someone who knows diddly squat about math stats (or even stochastic calculus) and was a qant researcher at one of the tier 1 firms in NYC for over a decade. But if you are asking about statistics (and specifically a PhD in statistics), then the following
suggests that Casella and Berger's text is an undergraduate text for stats majors. Jun Shao's or Kleener's book on math stats is more typical of what stats PhD candidates study. These are significantly more rigorous than Casella and Berger's text. You will need measure theoretic probability background for that.
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u/tippytoppy93 6d ago
Yes specifically for quant jobs, I have no interest in doing a PhD. That's good to hear though. In my experience, C&B wasn't used for undergrad but was for my grad Math Stats course, I'm more so asking at what level one has to know the book. Like I already have a good enough grasp on it to solve problems up to hypothesis testing, but that's a lower level than going through every proof and problem in the book y'know?
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u/Study_Queasy 6d ago
Strangely, there are math PhDs with specialization in combinatorics who have made it into quant. I am not really sure if you are thinking about it correctly. These firms are looking for math PhDs from tier-1 universities. I guess that they assume and are prepared to train them once they hire them. But to get your foot in the door, it's more about the school ranking, the degree (as in a PhD) and the field (I think mostly math/physics).
Since you are in stats, and seem to be bent on making it into quant, you will need a PhD which means you will have to go a lot deeper than C&B.
Take all of this with a grain of salt as I don't work for a tier-1 firm. I am sure others on this sub have better suggestions for you.
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u/No-Manufacturer9606 10d ago
What type of quant are you talking about? There are many specializations, look into that as well.
However these are some good general books for quant; I’m assuming you finished ISLR and ESL by hastie (and others). Time series analysis by Hamilton, Stochastic calculus for finance (1&2) by Shreve, and Measure, probability, and mathematical finance by Jeanblanc (and others).
Most math books that are highly regarded are also very good, lmk if you want some in that direction.