Education Advice for an Econ Guy

[SwissEcon]

Hello everybody,

I have been following this site a little bit

I am about to finish my undergrad studies in Economics and getting very interested in Quantitative Finance. With my current background I am pretty sure that I will have a hard time with a Master in Quantitative Finance.

Therefore, I would like to ask you guys if you have any tipps to improve the necessary knowledge, in particular, it´s mathematics, probability and programming (I saw the thread with the reading list recommendations, but I guess these are for those with a math background); for instance, to let you guy, what you are dealing with: I took as many "quantitative courses" as possible, in my case: 3 maths courses (for economists), 1 stat, 2 econometrics courses - and the first time I read about "martingales, measure and integration" was in the book of Jacod/Protter´s "Probability Essentials". (The whole book is a prerequesite for the MSc in Quantitative Finance at the ETH in Switzerland).

So, I hope you guys could recommend me some real basic stuff which (I can use for self-studying that and) will give a solid background in maths, probability and programming (I know a little bit of R and Matab).

Thank you very much for help!

Cheers, SwissEcon

 

[bigbadwolf]

... and the first time I read about "martingales, measure and integration" was in the book of Jacod/Protter´s "Probability Essentials". (The whole book is a prerequesite for the MSc in Quantitative Finance at the ETH in Switzerland).

So, I hope you guys could recommend me some real basic stuff which (I can use for self-studying that and) will give a solid background in maths, probability and programming (I know a little bit of R and Matab).

Jacod & Protter is a terse grad-level introduction to measure-theoretic probability for students who already have a strong undergrad degree in math -- specifically a couple of courses in real analysis and ease and familiarity with the staccato style of presentation (definition-lemma-theorem-corollary). It will probably take you at least a couple of years to build up that level of ease and familiarity. I'm not sure it's worth it. Your time might be better spent learning to code in C and C++. The use of abstract math in finance will be seen in hindsight as a historical anomaly, an aberration. The use of computers and coding will remain for as long as the grid remains up (so I'm not sure about that either but it's probably a safer bet). There are any number of good books on C and C++ that are accessible to a noob. Take your pick.

 

[Ken Abbott]

Time series. Multivariate stat. Regression.

 

[Jedison]

The use of abstract math in finance will be seen in hindsight as a historical anomaly, an aberration.

The abstract math is responsible for the models they're coding. I'm not saying you will use sigma algebras everyday on the job but the people who do are the same ones winning Nobel Prizes while they give the bankers new ways to make money

 

[ygmwy]

The abstract math is responsible for the models they're coding. I'm not saying you will use sigma algebras everyday on the job but the people who do are the same ones winning Nobel Prizes while they give the bankers new ways to make money

Oh Reeeeeeeeeeeeally...Is that how it works?...How do you figure?

You are still in school mode. You think all the math you learn in school is important, and that nobel prize winning models is what makes money. Not so my friend. When you get to Columbia, one of the first things you will learn is that all the mathematical models you learn in school are wrong. Nobel prize winning models don't make money. Secret models that companies develop and use privately make money. And those are not taught in school.

I agree with @bigbadwolf. Especially at entry level, implementation is more important, and that is what will get you the job. If you think your first job out of school will be doing stochastic calculus, or using sigma algebras (either mark of a pure genius, or that of an inexperienced greenhorn), you will only be right 1 out of maybe 30 times. That is, there is typically one pure genius for every 30 MFE students that actually work on research that is even remotely close to what you learn in school. Everyone else has to live in the real world where models usually don't work. Read Derman's book "Models Behaving Badly."

 

[Jedison]

Oh Reeeeeeeeeeeeally...Is that how it works?...How do you figure?

You say, whilst applying thousands of years of abstract mathematics to finance...

 

[ygmwy]

You say, whilst applying thousands of years of abstract mathematics to finance...

Models are guidelines. Quants don't predict the future. Where I work, models are run, and then the answers changed based on gut feeling. Relying too much on theoretical models is what got us into the crisis. A good modeler knows when his model is wrong, and by how much. I agree that math is important, but less than one might think. More of your time will be spent on implementation, and that's where most of the hiring is. There are only so many jobs where they trust you to write a mathematical model with pencil and paper for their portfolio. Especially at entry level. It's more likely that you will be coding up someone else's genius. So yes, if there were a button to learn abstract math instantly, press it. But if you have limited time, it can be spent more wisely. (Again, unless you're a genius interested in research, which everyone starts out thinking they are.) Basic math, stat, and econ will suffice in the meantime. You'll learn more useful math on the job than at school. And usually it doesn't involve abstract algebra (or sigma algebras for that matter).

 

[Jedison]

Ok so do the secret models use some sort of secret mathematics other than the one I was referring to?

 

[Jedison]

My point is not whether or not models are the end all be all of making money but that abstract mathematics is not an "aberration" in the finance industry. Yes C++ might be more useful to learn in terms of raw skills for a job but you can't deny that mathematics has taken the finance industry a very long way and it will continue to do so. If you think I'm wrong tell that to the many firms hiring math PHD's for quant reserach

 

[Jedison]

Also editing your post after I respond to it...common man

 

[ygmwy]

The guy is only just starting out...and only PhDs and geniuses do that. If you think most of your masters class will end up doing quant research, you're seriously mistaken. Those geniuses are not just PhDs. They are the top in the world. I used to be a recruiter in the industry. I remember when a candidate was brought up, the first question asked was "how many medals does he have?" As in nationally recognized mathematical awards. You have lawyer/doctor syndrome. You hear about a few lawyers and doctors making millions, and you think they all do. The truth is very far from that.

 

[Jedison]

That's a red herring. My point is that mathematics isn't going anywhere in quant(yes that word is in the title)itative finance. Feel free to stop using calculus, probability, the Black Scholes model, the Log Normal model, and Ito's Lemma. Good luck.

 

[ygmwy]

Also editing your post after I respond to it...common man

I edited the post before I saw your answer. And if you look back, you have almost no substance behind your words. Would you rather win this argument, or be right? I was arguing the point you said about nobel prize winning economics/mathematics giving bankers a way to make money. It's less important than you think, and with limited time, be better off brushing up on your programming skills to get a job.

You really don't know what Black Scholes, Log Normal, or Ito's Lemma mean in the real world do you? You only know the math, but you lack the insight to realize what it actually means in industry. Do you really think that stocks are lognormal? Those kind of assumptions are what got us into crisis. Just proves you don't really know what you are saying.

Look dude, your point: Math is important in finance. Is acknowledged. I get it. But math is a double edged sword, and it's not the best allocation of time for an econ major with no background. To break in, he can get a job easily with programming skills and minimal quant knowledge.

Your point: Math is important in finance, is not even worth defending. Why are you arguing about it--especially when you don't really know much about it? In fact, I don't know why I'm arguing this either. Sometimes, I just get carried away.

Let's just call a truce. Sorry for stirring up trouble.

 

[bigbadwolf]

There has yet to be a discussion of the real role and significance of abstract math (measure theory, stochastic calculus) in the post-2008 world. Prior to 2008, the tacit consensus was that this abstract math was modelling real aspects of the finance of an advanced free-market economy. After 2008, it became clear to some that the use of this math was just a truckload of manure. Although MacKenzie's book, An Engine, Not a Camera, published in 2006, was already arguing that models were being used to shape markets rather than describe empirical features of those markets. And come to think of it, the bankruptcy of LTCM a decade prior was also a warning bell, a wake-up call.

For the past few years the financial markets have been propped up by massive state intervention -- they are literally a "dead man walking." As the US empire unsucessfully grapples with various existential crises, it's plausible to assume that the complex financial structures will wither and die. Which means the use of the abstract math will wither and die.

 

[tfors]

... models were being used to shape markets rather than describe empirical features of those markets. ...

Very meaningful sentence. I'll ponder on this for the implications of this to my choices in future education..

 

[bigbadwolf]

Very meaningful sentence. I'll ponder on this for the implications of this to my choices in future education..

Read the book. Don't take my word for it. For instance, the use of Black-Scholes: it wasn't used by traders to determine the price of an option using some historically-derived estimate of volatility; rather, it was stood on its head and the price used to determine "implied volatility." Options could then be compared to another by their differing "implied volatilities" -- and this was a part of the impetus for the development of the options market. The initial match between the Black-Scoles-Merton model (using historical volatility to determine price) and empirical options prices was poor; the model was used initially by the Chicago Board Options Exchange to rebut charges that options trading was morally disreputable (i.e., just gambling).

There is a philosophical question no-one asks here: Can market processes be modeled by abstract math? The assumption that they can is an unquestioned tenet of our neoliberal era.

 

[IntoDarkness]

gs made a lot of money in fixed income couple years ago by exploiting risk free discount curve change. its not really that much math.

 

[bigbadwolf]

gs made a lot of money in fixed income couple years ago by exploiting risk free discount curve change. its not really that much math.

Good point. Stick to the basic and transparent stuff and leave the abstruse and abstract material for the suckers.

 

[Daniel Duffy]

The abstract math is responsible for the models they're coding. I'm not saying you will use sigma algebras everyday on the job but the people who do are the same ones winning Nobel Prizes while they give the bankers new ways to make money

Nope.

Measure theory is almost useless and had few applications (although Radon-Nikodym and Girsanov are good to know).

Measure theory was the most boring subject I had in undergrad maths.

http://www.wilmott.com/messageview.cfm?catid=8&threadid=95199&FTVAR_MSGDBTABLE=

With an Econ degree you will have major challenges learning MT.

 

[bigbadwolf]

Measure theory was the most boring subject I had in undergrad maths.

With an Econ degree you will have major challenges learning MT.

The ratio of interesting and deep results to definitions is close to zero. In contrast, areas like complex analysis, differential geometry, algebraic topology, and number theory have healthy ratios. I don't know if it's difficult for an econ major to pick up measure theory but it will be deadly tedious and boring -- and probably serve no purpose.