oxford phd financial mathematics

DPhil Finance

Start date:

• 6 October 2025

Time commitment:

About the programme

Our doctoral training will immerse you in all aspects of academic life.

You will be both a student and a junior research colleague. We provide courses in a wide variety of research methods and you will work closely with your supervisors to define your research question and develop your thesis. You will also have opportunities to gain teaching and research assistant experience and become involved with the intellectual community within both Saïd Business School and the wider University. You will attend academic conferences, make presentations, organise lectures and seminars and contribute to management and academic decisions. Both of our doctoral programmes run in parallel, with only differences in taught courses and preparation for writing in relevant journals to your subject of choice. We have deliberately kept the programmes small which means that in the vast majority of cases, students are fully funded to allow them to devote their energies to research. The DPhil corresponds to a PhD degree offered at most other universities. Examples of previous research topics include asset-pricing and corporate finance, the design and regulation of securities markets, corporate financial policy, and the impact of financial markets on real economic activity.

Supervision

You will be assigned two initial supervisors who will guide you through your first year.

They will help you to identify your specialist area of interest and further suitable advisers in that field. You will work closely with them to define your research question and develop your thesis. It is an important relationship and also a very personal one: it is shaped by you, your supervisors and the ways you interact. You will have a minimum of nine meetings, or equivalent per year with your supervisor.

You do not need to contact any faculty in advance of making your application but you can review the  profiles of our faculty  to look for at the areas of research covered at the School. You can note within your application if you feel that you are interested in a particular research area and working with a specific faculty member. 

The allocation of a supervisor is the responsibility of Saïd Business School, it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside the School.

Review some current research taking place around the school as well as from some of our alumni.

Review articles and podcasts written by our researchers at  Oxford Answers . 

Learn more about  becoming a researcher  from Andromachi Athanasopoulou, who graduated in 2007 and is now an Associate Professor in Organisational Behaviour at Queen Mary University London and an Associate Fellow at Oxford Saïd.

View Professor Renée Adams' discussion on  Women on boards: The superheroes of tomorrow?  

View Dr Amir Amel-Zadeh discussion on  (Mis-)information in financial markets .

Assessment and programme milestones

Our DPhil offers students the opportunity to engage with internationally renowned faculty who are here to help you become an academic scholar.

You will initially be admitted to the status of Probationer Research Student (PRS). During your first year, you are required to attend six core modules from the MPhil Economics and Saïd Business School doctoral courses.

MPhil Economics:

• Microeconomics (Core)
• Econometrics (Advanced)
• Financial Economics 1
• Financial Economics 2

Saïd Business School doctoral courses:

• Empirical Finance
• DPhil Finance Professional Development Course

You will also attend two elective courses from the list below. The list includes courses from the second year of the MPhil Economics as well as Saïd Business School doctoral courses.

• Macroeconomics (Advanced)
• Microeconomics (Advanced)
• Behavioural Economics
• Development Economics 1
• Development Economics 2
• Economic History 1
• Economic History 2
• Empirical Microeconomics
• Industrial Organisation 1
• International Macroeconomics and Finance
• International Trade 1
• International Trade 2
• Labour Economics
• Public Economics
• Urban Spatial Economics
• Empirical Corporate Finance
• Empirical Asset Pricing

All students will satisfactorily complete the courses, examinations and coursework as determined by the supervisor and/or DPhil Committee.

After successful completion of all necessary courses and within a maximum of six terms as a PRS student (and normally by the fourth term), you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. A successful transfer of status is required to give a clear indication of whether it would be reasonable to consider submission within the course of a further three terms, if work on the thesis continues to develop satisfactorily. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status within nine terms of admission, to show that your work continues to be on track. Both milestones normally involve an interview with two assessors (other than your supervisor). This provides important experience for the final oral examination. You will be expected to submit a thesis, which provides a significant and substantial contribution to the field of learning in finance, which should not exceed 100,000 words after four years from the date of admission. It should be good enough to be published in book form or as a series of academic articles. To be successfully awarded a DPhil in Finance you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

Changes to this course and your supervision

We seek to deliver this course in accordance with this description. However, there may be situations in which it is desirable or necessary for the us to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. Also in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our pages on changes to courses and the provisions of the student contract regarding changes to courses.

I think the most important issues in the supervisor relationship are communication and trust. You need a supervisor who can tell you the things you need to hear even if you don’t want to hear them, and who can nudge you back on to the right track. Alexander Montag Current DPhil in Finance student

Benefits and opportunities

• Engage with internationally renowned faculty
• Conference and research funding
• Training in principal research methods at both at Saïd Business School and wider University.

Opportunities

• Paid teaching and research assistant opportunities
• Contribute to management and academic programme decisions through student representation on committees
• Postgraduate careers resources

You will become a member of an Oxford college. Your college is both an academic and social community that will enrich your time at Oxford. It offers everything from formal dinners and balls to sports and lecture series.

The Oxford college system enables you to interact with students and faculty from other disciplines. Some colleges provide accommodation for students. 

Who can apply

Our candidates are passionately intellectual people who have a superlative academic record and are committed to a career in academia.

DPhil in Finance

You will require:

• a good undergraduate degree: 2.1 (GPA 3.5 or its equivalent)
• GMAT or GRE test results
• TOEFL or IELTS test results (If you are not from an English speaking majority country)
• three pieces of written work, including a well-developed research proposal
• three academic references
• £20 application fee

Application process

Applications for October 2025 entry are open.

The application deadline is 13 December 2024 at 23:59 UK time. 

Complete applications received by the deadline will be considered. You will be informed by late January if you have been shortlisted for interview.

Final decisions will be communicated by the end of February.

There are nine shared places available for the DPhil Finance and DPhil Management. The average number of applications for entry between 2021 and 2023 was 70. 

Fees and funding

The course fee in 2025-26 is £25,160 for both home and overseas students.

The programme is four years in duration. Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). Please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Course fees do not cover your accommodation, residential costs or other living costs. 

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Additional cost information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of the expenses.

Scholarships and funding

Doctoral students admitted to our programme receive full funding over four years. This includes course fees and an annual living expenses stipend. To maximise the overall availability of funding for candidates, we will identify suitable alternative scholarships and may ask you to submit funding applications. We also ask that you identify and pursue any other funding opportunities, including external funding.   

For some scholarships you are required to submit a scholarship essay and/or tick the relevant box in the Funding section of the application form.

Cost of living

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course. Please view the University's  living expenses page for information about likely living costs for 2025-26.

Further information about fees

The Fees and Funding section of The University of Oxford's website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Alumni placements

• University of Michigan Ross School of Business – Assistant Professor of Finance
• Vanderbilt University – Assistant Professor of Finance
• Ivey Business School - Assistant Professor in Finance
• University of Warwick - Assistant Professor of Entrepreneurship and Innovation
• International Monetary Fund - Economist (Economist Program), Research Department
• Harvard Business School - Post-Doctoral Fellow
• University of Hong Kong - Assistant Professor of Finance
• Vrije Universiteit Amsterdam and Tinbergen Institute - Assistant Professor of Finance
• Federal Reserve Bank of Cleveland – Research Economist
• Indiana University – Assistant Professor of Finance
• City University of Hong Kong - Assistant Professor
• Please contact us if you have any queries.
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Data-driven models & mathematical finance: apposition or opposition?

The aftermath of the financial crisis of 2009 as well as the multiple Flash Crashes of the early 2010s resulted in social uproars in the general population and ethical malaises in the scientific community [15, 9, 11, 10] which triggered noticeable changes in Quantitative Finance (QF).

More specifically, QF was instructed to change [16, 17, 18] and become more realistic as opposed to more convenient. The concurrent rise of Big Data (BD) [19] and Data Science (DS) [20] contributed ...

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• Mathematical and Computational Finance @ Oxford

Research in Mathematical & Computational Finance

• MCF Working Papers 2024
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The Oxford Mathematical and Computational Finance Group is one of the leading academic research groups  in the world focused on mathematical modeling in finance and offers a thriving research environment, with experts covering multiple  areas of quantitative finance. Our group maintains close links with the Data Science , Stochastic Analysis and  Numerical Analysis groups as well as the Institute for New Economic Thinking (INET), the Alan Turing Institute (Machine Learning in Finance ) ,  DataSig ,  the Oxford-Man Institute of Quantitative Finance  and the Oxford Probability Group ,  enabling cross-fertilisation of ideas and techniques.

Research activities of the group cover a wide spectrum of topics in Quantitative Finance , ranging from  market microstructure and high-frequency modeling  to macro-financial modeling and systemic risk, as well as more traditional topics such as  portfolio optimisation, derivative pricing, credit risk modeling, using a variety of methods: stochastic analysis, probability, partial differential equations, optimisation, numerical simulation, statistics and machine learning.

Mathematical Foundations and Continuous-time finance

Positioned within Oxford's Mathematical Institute, the group has developed a unique expertise in the mathematical foundations underlying quantitative finance and pioneered new approaches in mathematical modeling.

Sam Cohen , Rama Cont , Ben Hambly , Blanka Horvath ,  Jan Obloj   and Zhongmin Qian explore topics in stochastic analysis  -stochastic calculus, backward stochastic differential equations, interacting particle systems, Malliavin calculus, Functional Ito calculus, rough path theory, pathwise methods in stochastic analysis, optimal transport- and their applications to the design of robust models for the pricing and hedging of derivatives in presence of model uncertainty.   Michael Monoyios works on duality methods for optimal investment and consumption problems, and on valuation and hedging problems in incomplete markets. He has worked on models with transaction costs, and with partial and inside information on asset price evolution. He has interests in Fernholz's stochastic portfolio theory, and on the geometric interpretation of functionally generated portfolios that arise in this theory.  Jan Obloj works on robust formulations of classical problems -- pricing, hedging, risk management, optimal investment – and seeks to understand and quantify the effects of model uncertainty. Blanka Horvath focusses on implied volatility modelling, rough volatility models, stochastic volterra equations and stochastic volatility models their short -time asymptotic properties as well as their numerical properties for pricing, hedging and simulation.

Statistical modeling and Machine Learning in Finance

Our group is one of the few academic research teams in the world with an active research agenda at the interface of machine learning and quantitative finance. Several group members are Fellows of the Alan Turing Institute. Hanqing Jin is Director of the Oxford-Nie Big Data Lab , where Ning Wang has developed algorithms for sentiment analysis based on social media data. Sam Cohen  is exploring applications of Deep Learning  to continuous-time finance as well as issues related to model robustness and its interaction with statistical modelling and optimal control. Rama Cont ,  Blanka Horvath   and Justin Sirignano investigate the use of Deep Learning and  data-driven modelling in finance. Terry Lyons and his team investigate the use of rough path signatures for machine learning. Jan Obloj   employs tools from the optimal transport theory to develop data-driven estimators for risk measures, and to quantify robustness of deep neural networks to adversarial attacks. Blanka Horvath   develops deep learning tools for option pricing, (deep) calibration and hedging and for data-driven simulation of asset price dynamics and data-driven portfolio choice problems.

Market microstructure and algorithmic finance

Álvaro Cartea   focuses on mathematical models of algorithmic trading and  the design of optimal trade execition strategies in electronic markets.

Rama Cont pioneered the analytical study of stochastic models for limit order books and intraday market modeling, and investigates the impact of algorithmic trading on market stability and liquidity.

Leandro Sanchez-Betancourt studies the equilibrium between makers and takers of liquidity with continuous-time models and tools from stochastic control and machine learning.

Macro-financial modeling: financial stability and systemic risk

Our group is actively engaged in the development of mathematical models of large-scale financial systems with the goal of providing quantitative insights on financial stability and systemic risk to regulators and policy makers. Rama Cont and Ben Hambly   investigate the link between micro- and macro-behavior in stochastic models of direct and indirect contagion in financial markets, using network models and analogies with interacting particle systems.

Rama Cont ,Research Fellow at the  Institute for New Economic Thinking (INET), have developed network models and simulation-based approaches for macro stress-testing and monitoring systemic risk in banking systems, in liaison with central banks and international organisations such as the Bank of England, the European Central Bank, IMF and Norges Bank.

Rama Cont   is  Director of the Oxford Martin Programme on Systemic Resilience , an interdisciplinary programme aimed at exploring solutions for managing stress scenarios with the potential for major and prolonged economic disruption, severe human or economic impacts, and contagion. 

Computational Finance

Our group is a leader in the development of advanced numerical methods and  high performance computiing for high-dimensional problems in finance: Mike Giles  is a pioneer  on multilevel Monte-Carlo methods and their applications in finance, and a leading expert on the use of GPU and high performance computing methods in finance. Raphael Hauser has developed robust numerical methods for portfolio optimisation and high-dimensional optimisation problems in finance. Jan Obloj develops numerical methods for martingale optimal transport problems which yield bounds for option prices and optimal transport techniques for model calibration. Justin Sirignano has pioneered the use of Deep Learning methods for various applications in finance ranging from credit risk modeling to limit order book modeling. Christoph Reisinger develops novel and efficient numerical methods for stochastic control problems and high-dimensional (S)PDEs and their applications in finance; Terry Lyons devised cubature methods in Wiener space for solving stochastic differential equations. Sam Howison and Jeff Dewynne were among the pioneers in the development of advanced  partial differential equation methods in finance, the use of asymptotic methods for their solution and their application to various markets such as energy and commodities. Blanka Horvath   develops numerical solutions for pricing, hedging and optimal investment problems and analytic- and asymptotic methods for a wide variety of stochastic models for equity, FX and interest rate modelling. The numerical methodologies explore path-dependent   data-driven machine learning solutions as well as quantum machine learning algorithms.

Behavioural finance

Hanqing Jin   develops quantitative models of investor behaviour, building on the fundamental work of Kahneman and Tversky's prospect theory and Lopes' SP/A theory. Ning Wang  is working on sentiment analysis based on social media data, as well as on using data to establish metrics for learning and identification purposes. Jan Obloj works on optimal decision problems for cumulative prospect theory agents and understanding their actions in dynamic environments, such as casino gambling.

For more information on research activities of our group please visit the individual websites of group members .

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• UK Quant Programs

Review of Oxford MScMCF

• Thread starter Thread starter Khorge
• Start date Start date 6/25/14

• General Oxford experience - old architecture, punting, societies, Oxford Union etc...
• Top notch facilities - especially the new Mathematical Institute building. It's arguably the coolest math building in the world :D.
• Excellent teaching - Apart from one or two lecturers, the quality of teaching is exceptional . You are taught by some fairly big names in financial mathematics.
• Solid coursework - The coursework is very rigorous and a lot more theoretical than other programs. The overall focus of the course has been on derivatives pricing although you have an option next year to focus on data-driven topics e.g. algo trading and market microstructure.
• Course structure - This is by far the biggest complaint among the current students and to Oxford's credit the course will be restructured next year (2014/2015) in light of this. We learned programming far too late to be useful in interviews and were expected to find jobs at the very beginning of the course when the graduate recruitment season began.
• C++ Programming Courses - You learn the basics and then learn how to use and extend the lecturer's own library. This is inadequate preparation for quant interviews and actual quant work. You will need to study some Comp. Sci (e.g. sorting algorithms) yourself.
• Course is too intensive/long. The course tries to pack in too much material for the 10 months and as a result you will be pressed hard. By the start of the final semester only less than 40% of the course marks have been assessed . Make no mistake - this course is one of the most difficult in Oxford.
• No careers service - you have a careers office (outside of the department) but nothing else. No one is actively searching for roles for you unlike at some top US programs. You might also get some networking sessions/presentations from banks/HF's but that's about it.
• Dissertation and Miniprojects - These were very time consuming - you need to do all of these during Trinity term. That's around 60-80 pages of stuff you are required to write in 8 weeks or so. On the other hand, these projects were a good way to reinforce material learned in the first two semesters.

Thanks for the very informative post! Could you post a list of the mathematics courses, as well as the textbooks used? Always curious to see what other programs study.  

Daniel Duffy

C++ author, trainer.

C++ Programming Courses - You learn the basics and then learn how to use and extend the lecturer's own library. This is inadequate preparation for quant interviews and actual quant work. You will need to study some Comp. Sci (e.g. sorting algorithms) yourself. Data structures and algorithms are essential. In general, university approach to programming is a million miles away from industry. But you will know what the difference is after a while!  

Ole Bueker said: Thanks for the very informative post! Could you post a list of the mathematics courses, as well as the textbooks used? Always curious to see what other programs study. Click to expand...

I was asking for the books mainly to check the level of the individual courses, e.g. does the stochastic calculus course go beyond Shreve II etc. Seems like a really intensive program, quite a lot of courses for a 10 month period.  

Ole Bueker said: I was asking for the books mainly to check the level of the individual courses, e.g. does the stochastic calculus course go beyond Shreve II etc. Seems like a really intensive program, quite a lot of courses for a 10 month period. Click to expand...

Khorge said: We were using Williams - Probability with Martingales as well. Click to expand...

bigbadwolf said: Not a great book, in my opinion. Too terse, too theoretical. Trying to condense a 2-year course into ten months can only be detrimental -- not enough time to think and to digest. I've seen this frequently in English universities. Even the best don't have time to digest -- so they think they know the material based on their course grade but actually they don't. Click to expand...

Hi khorge, I just have a couple of questions about what you said: 1) Do you know what will be different about C++ in the new structure? 2) Also, you mention that it would be useful to know some statistics/time series before the course. Could you please give a little bit more detail on that (how much stats/time series, what topics in particular, etc.)? 3) On the careers side of things, did many companies come to Oxford for presentations or is it up to the students to apply, etc. I am not talking about the main banks because it is usually straightforward to apply for their jobs, but for the hedge funds (and smaller companies) it is much harder in general (even to find them!). 4) I know that you will know little about this since it will only be implemented this year, but do you have an opinion on the options available (ie either Data or Modelling streams) in the new structure -- maybe you know some of the lecturers/took some similar modules? 5) Apart from that do you have any general advice on how to deal with the rhythm of the MSc especially in terms of preparing for the dissertation? Thanks a lot!  

@Khorge : The data here shows number of applicants only 161 average for 3 years, not 500, so can you please let me know where you get your data from ? MSc in Mathematical and Computational Finance | University of Oxford . As I have asked similar courses at LSE (MSc Fin Maths) and Imperial (MSc Maths & Finance) and they said the number of applicants is around 500. This makes sense since they don't require the admissions test.  

Khorge said: What other courses should I consider (in the UK)? Click to expand...

Khorge said: Imperial has better careers services and employment outcomes - I'd do their industrial training over a dissertation anyday! Cambridge Part III is cheaper and just as employable/reputable as Oxford, imo. However, some say it's even more 'hardcore' than even the Oxford MScMCF. I don't know about LSE but my fellow students say its much worse than Oxford. Frankly, you will have difficulty finding quant jobs if you are in lower ranked universities. Banks' tend to hire from Oxford, Cambridge, Imperial , LSE in no particular order. Feel free to ask any questions below. Click to expand...

I am currently pursuing a Bsc Economics and Finance Course which involves a few mathematics courses but i am really interested in this course and i feel i know the content which is given in the admissions test. So if i am able to pass the test do you think i ll have an equal opportunity like others in getting an admission to that course. In short if i am able to surpass the scores entry requirements and ace the admission test will i be treated equally like the other students from maths and engineering backgrounds  

AnonymousHedger

Phongvasu said: I am wondering what courses were you referring to when you mentioned about Cambridge, Imperial , and LSE. The one in their business school? Math department? or what? Thank you for your answer. Click to expand...

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