This is part 2 in a 3-part series on how to self-study to get into quantitative finance. We've already covered self-studying to become a quantitative developer. In this article we'll look at forming a self-study plan to become a quantitative analyst/financial engineer.
Quantitative analysts and financial engineers spend their time determining fair prices for derivative products. This involves some deep mathematical theory including probability, measure theory, stochastic calculus and partial differential equations. Thus to become a quant analyst it is necessary to have a strong mathematical background in mathematics, usually through an undergraduate degree in mathematics, physics or engineering.
Undertaking self-study to become a quantitative analyst is not a straightforward task. Depending upon your background, aptitude and time commitments, it can take anywhere from six months to two years to be familiar with the necessary material before being able to apply for a quantitative position. However, the rewards are worthwhile. An extremely challenging intellectual environment coupled with highly attractive compensation provides strong motivation to work towards becoming a quant.
Nowadays there is a comprehensive literature available for financial engineering. I've written many articles on this site about which books to start with, but I want to provide greater detail in this article as it is a study plan not a reading list!
I would suggest that these books are sufficient to gain a good understanding of options pricing. If you know that you are going to become a fixed income quant, then you will obviously need to be extremely familiar with interest rate derivatives and models, such as Heath-Jarrow-Morton (HJM) and the Hull-White models.
If you really want to become an expert at the underlying mathematics, say for carrying out a top Masters in Financial Engineering (MFE) program or for beginning a PhD in Mathematical Finance, you will need to gain a deeper level of mathematical sophistication at stochastic calculus. Steven Shreve has written a two-volume set, which covers both the discrete (Stochastic Calculus for Finance I: The Binomial Asset Pricing Model) and continuous (Stochastic Calculus for Finance II: Continuous-Time Models) cases. The books are quite involved and given the limited time with which you may have to study, you may find them too deep and specific for front office quant job interviews.
If you wish to delve into more mathematical finance books take a look at the quantitative finance reading list section on mathematical finance.
For some of you, getting a job in the financial industry is not the goal - you might want to pursue research in certain topics, either at PhD level or as a post-doctoral student, potentially coming from industry. The following books will give you a much deeper appreciation for options/derivatives pricing and will concentrate more on particular topic areas, such as fixed income or credit derivatives. You will almost certainly know your (approximate) research area before committing to a program. I've tried to provide some books which will give you a solid introduction to that particular area. By following the references, you will be able to learn more.
If you are simply interested in a career change into quantitative finance within industry or are after an entry-level role then feel free to skip this section and take a look at Programming Skills below.
Advanced mathematical finance really comes down to learning more about stochastic calculus and risk neutral pricing. These are both extensive research areas in mathematics. The following books will give you a deeper flavour of what quantitative finance is about.
If your research area is geared more towards particular products - specifically in the fixed income and credit spaces - then the following books will be of interest.
Unfortunately I can't do justice to all of the highly interesting areas of research that financial engineering encompasses in this article, so I will have to stop there!
Although you won't need to have as extensive a programming knowledge base as a quantitative developer, you will still need to have solid object-oriented programming skills, particularly in a language such as C++.
As a financial engineer you will spend about 50% of your time programming and implementing models. For that reason you will need to be familiar with C++ (or C#/Java) syntax, its pitfalls and "best practices". You will also need to be extremely competent at taking a mathematical algorithm and creating an object-oriented implementation that promotes maintainability, re-use and optimisation. These are difficult skills to learn unless you actually start implementing models. However, before we discuss numerical algorithms, we will talk about how to learn an object-oriented language, such as C++, to the extent necessary to perform well on a quant job (and pass an interview!).
Note that there will be some crossover here with the article on quantitative development, so feel free to look at that article for more details on programming.
If you wish to go further with your programming and learn about topics such as software engineering, version control and optimisation then take a look at the self-study guide for quantitative developers.
I have to admit that numerical methods are my favourite component of the financial engineering landscape. Further, they are possibly the most important part as well. Having a solid grasp of mathematics and stochastic calculus, while essential, means very little if you are not able to apply that knowledge to the practical pricing of derivative products. Generally one gains an education in scientific computing at PhD level or in grad school, as part of a computational/numerical PhD program. For those who haven't had a background in numerical methods, most likely due to a career change, it can seem like a daunting task to learn the material.
The best way to get started is to learn a fast language such as C++, as described above in Programming Skills, and then work through the books in the list below.
While the above may seem like a lot of material, you can break it down by avoiding many of the irrelevant algorithms. Concentrate on NLA, Monte Carlo and (maybe) some finite differences, as these are the cutting edge techniques. Remember though that you will only really gain experience via actually implementing these models. Make sure you program as many as you can to really get to grips with the material.
I've already written an article on interview preparation for becoming a quantitative analyst so I won't repeat myself too much here. Make sure you work through the five books described in that article and brush up on the myriad of brainteasers found within. They are an extremely common tactic to put a candidate under stress in an interview environment.
A strong investment in learning the material above well, coupled with extensive implementations of quant models in C++ along with practice interview questions from the above article will give you a very good chance of gaining a quant job in one of the top-tier firms.
Be aware though that it is a tougher market than usual for trying to find a quant position - particularly at entry-level. Investment banking interviews can be challenging. Thus it is extremely important that you study hard, implement the models and understand the basics thoroughly before applying to the recruiters.comments powered by Disqus
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