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Lack of statistics and probability courses in Physics programs

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8/7/15
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I have recently looked at Physics program at different universities in Europe. I noticed that while all of these programs, naturally, did have lots of Calculus, not many of them had so much statistics and probability. And most of the Financial theory is based in stochastic calculus and hence probability. So I was wondering how come it is so common for Physicists to become quants when they actually don't have that much knowledge about probability? :)
 
Can you not take electives in e.g. Statistics department.

Doesn't Statistical Mechanics cover these topics?
 
It was common because a physics degree is essentially applied maths, and physics PhDs will have had to solve research problems nobody has solved before. Sell side has had less of a need for such a generalist degree since the advent of MFEs and the like, and buy side is now increasingly hiring computer scientists and now that data science and machine learning has taken off as its own branch of, if not science, then at least university esucational prgrammes, they, not physicists, are en vogue on Wall Street.

Physicists still get hired given the history of hiring them, and so some hiring managers may still give them a break and universities from their ivory towers keep saying that an unsuccessful academic in physics can always turn to finance, but this is increasingly less true and physics majors should know this.

As for statistical mechanics, it does not cover the necessary tools: there is for example no time series analysis done on courses because it is not really of interest in carrying out the theory of ensemble averages, and thermal equilibrium, and the approach to probability is very different: The theories and models in physics are much more correct than those in finance, so there is no reason to, for example, study vanilla models separately from term structure models. This means physicists will not learn the fundamental theorems of probability useful in finance, like changes of measure and all the related stuff, nor would a physicist be so averse to a Monte Carlo solution as you don't need to be calibrating on the fly to some targets out of simpler models.

Instead, physicists solve Ornstein-Uhlenbeck analytically, but the problems of interest in statistical mechanics beyond ones with easy solutions, are, for example, stochastic PDEs, which you'll formulate and worry about the Ito-Stratonovich dilemma, but in the end just write up a numerical solver for. It is also the case that in finance the solution is an expected value, whereas in physics one often is interested in for example the dynamics of the probability density function itself: Not much here you can simplify with martingales, I don't think.
 
Then there is the important topic of how much measure and Lebesgue theory background is needed in order to understand how they relate to finance. The first thing to note is that they are difficult topics and form part of 2nd-3rd year syllabi of pure honours maths degrees. And preconditions are hard real (and some functional) analysis that must be mastered in 1st year. You really have to put iu a log of legwork to grasp them.

I can imagine what a yuge challenge it would be to learn this stuff in DIY mode. Even a large percentage of pure maths undergrads have had sleepness nights learning it. I doubt if MT is done in Physics degree programmes (and it would be the wrong place anyways).

BTW the whole topic of Probability can be subsumed into a measure theory framework. And this is certainly useful.

The question is: how much of this do you really need to know to proceed? Is Euler and the ability to understand and use SDEs sufficient?

In fairness, I have never been asked AFAIR by a quant "what is measure theory" :)
 
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The question is: how much of this do you really need to know to proceed? Is Euler and the ability to understand and use SDEs sufficient?

This is the question. What job do they do - are they a strategist, are they a developer, are they a portfolio manager, are they an actuary? Are they a Data Scientist?

Additionally what sector are they working in? Insurance, high frequency trading, asset management. If it is asset management - what type of assets and asset classes, do they hold short or long? Then after that what is their overall strategy - statistical arbitrage, macro event driven etc etc.

It really depends on the job and how much they need to know to proceed. Quant developer in insurance - maybe not so much probability/stats. Quant developer for a prop trading firm - maybe this will be essential. Data Scientist who specialises in Machine Learning - yep this will be essential (but again if you are smart you don't need that much knowledge for the most part). What about a Data Engineer for low latency systems - again probably not.

Read more about the finance industry as a whole. It will give you a better understanding of what suits your personality, your style of thinking and your general skill-set.

This is not a Holywood movie like Big Short where all quants are experts in everything. Everyone will have areas they are weak at including PhD's in Physics, hence the diversity in roles.
 
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