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From time to time people email me asking how to get started as a quant. While there are a number of fields that comprise the quant discipline and no list can be all-inclusive, if you are going to be interviewing for a quant position, you may wish to be conversant in the following areas:
- Finance and Financial Engineering, including complex financial derivatives and valuations, volatility surfaces and smiles, replication, arbitrage and equilibrium pricing models, CAPM, APT, Fama-French models and possibly risk management concepts depending on the area you’ll be supporting.
- Statistics and Probability, at a fairly deep level with a good knowledge of distributions, maximum likelihood theory and perhaps empirical distribution fitting, tests for normality and fitting of joint distribution using tools such as copulas, how to perform out of sample tests, properties and expectation of random variables, correlation and covariance and so on.
- Strong mathematics skills in areas including stochastic calculus, including martingales, markov processes (quick! What is the difference between a martingale and a markov process?), Ito’s lemma and so forth as well as ordinary calculus, differential equations, numerical methods, linear algebra and possibly a little computational complexity, algorithm analysis and optimization.
- Econometrics – properties of ARCH, GARCH, detecting the order of an AR/MA process and so on, stationary and non-stationary variance and how to test and correct for the same if need be, transformations, random walks, unit root tests and so forth.
- Knowledge of several computer packages, operating systems and languages including SAS, S-Plus, R, Matlab; expertise in a programming language such as C++, C# and/or Java, and experience with a non-Windows operating systems such as Unix.
- Detailed knowledge of capital markets may be required, including understanding of credit derivatives, mortgage securities, fixed income and detailed knowledge of various interest rate models, depending on where you will be interviewing.
- Understanding of simulation techniques such as generating simulations from various distributions and inverse transform theory, details of the Monte Carlo method and how simulation is used to value various financial instruments (also when you need to simulate as opposed to using other methods), possibly including variance reduction methods; random and pseudo-random number generation techniques, the pros and cons of various techniques, extreme value theory and so forth.
So where do you go to acquire this knowledge if you don’t already have it? Some good places to start are with free online MIT courses (and other universities) already discussed in a previous column. Some of the books that I recommend for your library include:
Financial Knowledge
You can get a great foundation in most of what you need to get started with John Hull’s book “Options, Futures and Other Derivatives” available on amazon.com and at other retailers. You can even get a student solution manual.
Financial Engineering
Baruch University professor Dan Stefanica has just come out with a great foundation book for most of what you’ll need to know and he has other books planned in this series. This type of book has been long needed. There is a solution manual available as well!
A Primer for the Mathematics of Financial Engineering, Dan Stefanica,
Probability and Statistics
I love the Schaum’s series for quick review. They’re inexpensive and comprehensive. There are a number of outlines including:
Time Series Analysis
I recommend getting a copy of S-Plus and buying Ruey Tsay’s book, Analysis of Financial Time Series, and working through the examples. Professor Tsay, of the University of Chicago, has a lot of worked examples and solutions from the book on his website as well.
There’s a great time series book on University of Chicago Professor John Cochrane’s website, and it’s completely free:
Monte Carlo Methods
The book by Paul Glasserman of Columbia University, Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability), is a classic in the field.
If you buy these from Amazon, you can also often get a digital edition that you can view online from any computer.
Stochastic Calculus for Finance
There are a lot of great books for learning stochastic calculus, but you can’t go wrong with Steve Shreve’s books:
Quantitative Interview Prep Books
There are books on finance and advanced finance interview prep, including my book from Vault.com, which should give you the basics.
1) The Vault Guide to Finance Interviews by D. Bhatawedekhar
2) Vault Guide to Advanced Finance & Quantitative Interviews (disclaimer: this is my book)
4) A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou
5) Quant Job Interview Questions and Answers, by Mark Joshi, Nick Denson and Andrew Downes.
Matlab
Matlab is a powerful and widely used tool in quantitative finance. The mathworks site has lots of sample code, examples and webinars. There are some great free resources to help you learn this tool as well. I have some of these posted on my blog in the Matlab category
This should get you started. Good luck, and if you have questions, just post them in the comments section.
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From time to time people email me asking how to get started as a quant. While there are a number of fields that comprise the quant discipline and no list can be all-inclusive, if you are going to be interviewing for a quant position, you may wish to be conversant in the following areas:
- Finance and Financial Engineering, including complex financial derivatives and valuations, volatility surfaces and smiles, replication, arbitrage and equilibrium pricing models, CAPM, APT, Fama-French models and possibly risk management concepts depending on the area you’ll be supporting.
- Statistics and Probability, at a fairly deep level with a good knowledge of distributions, maximum likelihood theory and perhaps empirical distribution fitting, tests for normality and fitting of joint distribution using tools such as copulas, how to perform out of sample tests, properties and expectation of random variables, correlation and covariance and so on.
- Strong mathematics skills in areas including stochastic calculus, including martingales, markov processes (quick! What is the difference between a martingale and a markov process?), Ito’s lemma and so forth as well as ordinary calculus, differential equations, numerical methods, linear algebra and possibly a little computational complexity, algorithm analysis and optimization.
- Econometrics – properties of ARCH, GARCH, detecting the order of an AR/MA process and so on, stationary and non-stationary variance and how to test and correct for the same if need be, transformations, random walks, unit root tests and so forth.
- Knowledge of several computer packages, operating systems and languages including SAS, S-Plus, R, Matlab; expertise in a programming language such as C++, C# and/or Java, and experience with a non-Windows operating systems such as Unix.
- Detailed knowledge of capital markets may be required, including understanding of credit derivatives, mortgage securities, fixed income and detailed knowledge of various interest rate models, depending on where you will be interviewing.
- Understanding of simulation techniques such as generating simulations from various distributions and inverse transform theory, details of the Monte Carlo method and how simulation is used to value various financial instruments (also when you need to simulate as opposed to using other methods), possibly including variance reduction methods; random and pseudo-random number generation techniques, the pros and cons of various techniques, extreme value theory and so forth.
So where do you go to acquire this knowledge if you don’t already have it? Some good places to start are with free online MIT courses (and other universities) already discussed in a previous column. Some of the books that I recommend for your library include:
Financial Knowledge
You can get a great foundation in most of what you need to get started with John Hull’s book “Options, Futures and Other Derivatives” available on amazon.com and at other retailers. You can even get a student solution manual.
Financial Engineering
Baruch University professor Dan Stefanica has just come out with a great foundation book for most of what you’ll need to know and he has other books planned in this series. This type of book has been long needed. There is a solution manual available as well!
A Primer for the Mathematics of Financial Engineering, Dan Stefanica,
Probability and Statistics
I love the Schaum’s series for quick review. They’re inexpensive and comprehensive. There are a number of outlines including:
Time Series Analysis
I recommend getting a copy of S-Plus and buying Ruey Tsay’s book, Analysis of Financial Time Series, and working through the examples. Professor Tsay, of the University of Chicago, has a lot of worked examples and solutions from the book on his website as well.
There’s a great time series book on University of Chicago Professor John Cochrane’s website, and it’s completely free:
Monte Carlo Methods
The book by Paul Glasserman of Columbia University, Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability), is a classic in the field.
If you buy these from Amazon, you can also often get a digital edition that you can view online from any computer.
Stochastic Calculus for Finance
There are a lot of great books for learning stochastic calculus, but you can’t go wrong with Steve Shreve’s books:
Quantitative Interview Prep Books
There are books on finance and advanced finance interview prep, including my book from Vault.com, which should give you the basics.
1) The Vault Guide to Finance Interviews by D. Bhatawedekhar
2) Vault Guide to Advanced Finance & Quantitative Interviews (disclaimer: this is my book)
4) A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou
5) Quant Job Interview Questions and Answers, by Mark Joshi, Nick Denson and Andrew Downes.
Matlab
Matlab is a powerful and widely used tool in quantitative finance. The mathworks site has lots of sample code, examples and webinars. There are some great free resources to help you learn this tool as well. I have some of these posted on my blog in the Matlab category
This should get you started. Good luck, and if you have questions, just post them in the comments section.
