Online Masters - Mathematical Finance (University of York): Top 50 in the world according to Risk.net

[damola]

I wanted to make a thread about this online masters because it gets little attention. As far as I'm aware, the only Universities with courses in the top 50 consistently for Financial Engineering Masters (according to Risk.net) are York (UK) and John Hopkins (US). Mind you, the John Hopkins course is 50k USD and the York course is 22.5k GBP. Given how the GBP exchange rate has been crushed since 2008, the course fees are worlds apart.

John Hopkins is 11th in the top 50 for Risk.net, while all entries above number 25 don't have an order (York is above 25). I mention this for a reason. John Hopkins is a target, so getting a job is much easier. They advertise their graduates as getting jobs such as Quantitative Analyst, Data Scientist and Quantitative Researcher) York advertise Financial analyst, Accountant, Credit risk manager, Investment manager, Risk analyst and Statistician.

John Hopkins York has 2 programmes, Financial Engineering and Mathematical Finance. Only the latter do they have which can be delivered online. I mention this for a reason. Here's the silver lining; the professors on the programme, such as M. Capinski and T. Zastawniak are world reknowned academics in the Financial Engineering space. Wherever you go (even at the top schools), it's far more probable than not that their textbooks will be used. They're career academics, that are experts in Financial Mathematics. If I wish to explain, the gap in post-employment outcomes, the entry requirements are much lower. York accept 2:2s (50%+) onto their programme and encourage graduates from other discplines, such as business, finance and economics to enter outside of STEM. For those with uncertain quantitative credentials, they have a pre-sessional costing 1.6k GBP, of which a grade of 60% gets you onto the programme, covering real analysis, probability, single/multivariate calculus and convergence.

York advertise:

• Online one-to-one tutorials
• Interactive presentations with video recordings
• Discussion forums

Now bear with me because I'm going to quote an online forum, then quote my own experience contacting alumni (in relevant FO roles):

I ignored the negative posts regarding the theoretical nature because, understanding the mathematics rigorously is most important in a FO before learning how to trade. The financial intuition can be learned afterwards (I'll get to that later).

University of York - Mathematical Finance (YOUR THOUGHTS) - The Student Room

The pre-sessional was very helpful because it was my first contact with mathematical proofs. The tutor is EXTREMELY helpful and they reply very quickly (this is also true during the MSc. I am impressed by how fast I get answers on my questions). There are live sessions and you can skype with your tutor during the pre-sessional. During the MSc you have forums (which are quickly replied to) and you can also skype your supervisor or email any of the professors (they are all very open, friendly and quick to reply).

You are provided with:
1) Lecture notes (books, really): I like them a lot. Takes you step by step, from simple to complex, with examples, proofs, references, etc. Focuses only on what is relevant for math fin. This is the main thing I use to study/learn
2) Lecture slides with embedded audios: has the same content as the notes, sometimes with different examples etc. To be honest, I don't really use these as the lecture notes are really enough for me
3) Exercise lists: exercises to help you put to practice what you have learned. They are not easy and good luck finding answers online (you won't, trust me). You need to solve them yourself and this is where you really really learn, in my opinion. Basically, you won't manage to solve the exercises if you didn't really understand what is in the lecture notes, so this is the real way to check if you really learned.

There is a period for solving the exercises, where you can ask questions in the forums, by email, on skype sessions with your supervisor, etc. Then after handing over your solutions, you get it back with commentaries from your supervisor along with the worked solutions (Step by step so you can follow the rationale). After that, you have more time to study the solutions, go to the forums, speak to the professors, etc. And then you have the assessments.

Everything I've read online tells me that you can get quick and regular support from supervisors and academics. This is a benefit which you may not be afforded on a larger ticket programme. It's sperated into 3 stages, a certificate stage, a diploma stage and dissertation stage.

Certificate Stage​

Core modules:

• Mathematical Methods of Finance
• Discrete Time Modelling and Derivative Securities
• Portfolio Theory and Risk Management

Diploma Stage​

Core module:

• Stochastic Calculus and Black-Scholes Theory

Choose two of the following options:

• Numerical and Computing Techniques in Finance
• Modelling of Bonds, Term Structure and Interest Rate Diversities
• Credit Risk
  

  Dissertation​

  • Mathematical Finance Dissertation
  • You'll be provided with a list of topics to choose from, or you can propose your own topic (subject to approval). Dissertation topics are unique for each student, and are typically based on recent publications in leading journals or preprints by leading researchers in the field of mathematical/quantitative finance.

  • Module aims​

    
    Candidates for the MSc degree will submit a dissertation of around 60 pages on a selected topic in Mathematical Finance. Support and advice will be provided by individual dissertation supervisors and consultants.

The course is no frills. Core Financial Mathematics syllabus with some applied C++ (optional) in the module 'Numerical and Computing Techniques in Finance'. The 60 page dissertation gives you an opportunity to show serious research skills on your resume. A Quantitative Analyst, I networked with on Linkedin has a link to his thesis on his profile (he's a Quantitative Analyst). It looks as mathematically rigorous as seen at the top schools.

Regarding the C++ module, here's a description on their website:

Module aims​

The aim of the module is to provide programming skills required for the implementation of mathematical models in quantitative finance. The focus will be on the C++ programming language, which is widely accepted as the main tool amongst practitioners in the financial community. The implementation of a given model rarely narrows down to the pricing of a single particular financial instrument. Most often it is possible to devise general numerical schemes which can be applied to various types of derivatives. The code should be designed so that it easily integrates with the work of other developers and can be modified by other users. The student will learn such skills by writing C++ programs designed for pricing various types of derivatives, starting from the simplest discrete time models and finishing with continuous time models based on finite difference or Monte Carlo methods.

Module learning outcomes​

By the end of the module, students should:

• be able to write comprehensive C++ programs;
• be familiar with functions and function pointers;
• be familiar with classes and handle virtual functions, inheritance and multiple inheritance;
• be able to implement non-linear solvers;
• be familiar with data structures and dynamic memory allocation;
• understand and have experience of using class and function templates;
• be familiar with standard numerical methods (finite difference, Monte Carlo) for solving representative problems;
• be able to price European and American options under the CRR model;
• be able to price American options by means of finite difference methods under assumptions of the Black Scholes model;
• be able to price barrier and Asian options by means of Monte Carlo simulation.

Upon talking to alumni, I got the impression that C++ skills gained from this programme are more applied, rather than them necessarily them understanding it deeply. I plan on taking this course, but there's always Duffy afterwards. The first bullet claim is an interesting claim, though the classroom never as good as the real world. Is it good enough for an entry level quant role in C++? I don't know. Edit: They teach the C++ module from one of Duffy's book.

2 of the 3 alumni I spoke to had their grades on Linkedin. They both scored around 80% on the MSc. Two of them became Quantitative Analysts straight after graduation. The third took a job as a 'Data, Methods & Research Analyst' a year after graduating. He then got a job as a quantitative analyst under a year later. That's the guy who's thesis I linked to.

One of the alumni who got a Quant Analyst job after graduation did so for Gazprom (energy) right after his masters. In a 8 months, he jumped to Quantitative Forecasting Analyst, Senior Quantitative Forecasting Analyst after 7 months and then left the job 8 months later to become an Investment Analyst (lol) at an IB specialising in energy. 8 months after becoming an IB, he got in as a Quantitative Trader at Shell.

They all told me that this job, is suitable to fit around full time work. One of them mentioned, they had to travel half an hour each way each day to do a manual blue collar job full time when studying. He still got 80% and went to a Quant Trader straight after the masters.

 

[skulduggery]

Does anyone have more information, reviews, or viewpoints on this degree as it pertains to pursuing a career transition to becoming a quant trader or researcher?

I currently work as a fundamental equity investment analyst / shadow PM at an AM looking to move into buyside quant work; have a MSc in Finance but don't have a numerical UG degree so my options for a rigorous MFE/Mathematics/Statistics Masters are limited. The UOY MSc in MathFin is the only I've found in Europe that has a pre-sessional course approach for making conditional offers that doesn't require a numerical undergrad degree. Have also considered redoing a BSc in Mathematics via the Open University or a PgDip and then reapplying at better-ranked/-connected unis. This would obviously take more time but is perhaps more worth it in the long run?

Would greatly appreciate anyone's thoughts or advice given my situation. Thanks.

 

[annyugen]

Very interesting.
I'm a full-time software develop and been considering doing John Hopkins online Financial Mathematics as part-time.

Extremely curious if anyone has gave the program a try

 

[lourano.x]

The University of York is well-regarded in the UK, something many students can vouch for, and it's not hard to see why. Their math department really stands out, thanks to its impactful research and strong mathematicians. Additionally, the economics department deserves mention, particularly for its strength in econometrics. This is highlighted by the presence of figures like Shin, one of the developers of the KPSS test, underscoring the department's expertise.

I believe that if the University of York's board were to put a bit more effort into showcasing their Quant Finance direction, they could become one of the top choices for anyone looking to study Quantitative Finance in Europe. They've already got a solid base. They just need to highlight this area a bit more.

I think that the MSc in Financial Mathematics and MSc in Financial Engineering at York, already benefiting from the connection between the math and economics departments. However, in my opinion, there's still place for further innovation. In my view, incorporating the Computer Science department, well-known for its research on efficient real-time systems, could pave the way for HFT research, where York would be really excelent place for doing master's for quant research/trading and developing.