thesis in mathematical finance

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• Department of Mathematics
• Faculty of Natural Sciences
• Departments, institutes and centres
• Postgraduate
• MSc courses
• MSc in Mathematics and Finance

Project and thesis

The MSc project is a substantial component of the programme, occupying around 4 months. It is a piece of original work undertaken by the students under the supervision of an academic researcher and, in most cases, also with an external supervisor. Most projects are carried out in association with a bank, hedge fund, consultancy, or systems provider in the finance industry, and we endeavour to arrange suitable placements. 

You will find below a large sample of past theses, covering wide range of topics.

19 November 2020- Goldman Sachs-Imperial MSc Flash Talk Series

  We are proud to partner with Goldman Sachs for a Flash Talk series of 2019-2020 MSc Math Finance Theses.

Event type :  Zoom Webinar

Date : thursday, 19 november , time : 4:30-6pm gmt.

Past and Current Project Partners

• George Coxon: Optimal execution with intraday liquidity changes and transient no
• Minyuan Li: Simulations of calibrated local stochastic volatility models
• David Malone: Methods of pricing cross-currency Bermudan swaptions
• Ilia Sobakinskikh: Optimizing transformer neural network for real-time outlier d
• James Mc Greevy: Detecting multivariate market regimes via clustering algorithms
• Victoria Xing: Liquidity saving mechanisms and mixed-integer linear programming
• Changan Qian: Transformer-based probabilistic forecasting model for intraday for
• Anthea He: Reservoir Computing in alpha forecasting of foreign exchange market
• Thomas Simon: Evaluating approaches for estimating the Day-Ahead price and Water
• Jeroen Nelis: Detecting and repairing arbitrage: from European to American optio
• Martin Weirich: Fokker-Planck calibration of one-factor stochastic local volatil
• Valentin Blanda: FX barrier option pricing
• Zhihao Xu: Harvest Volatility Risk premia using deep reinforcement learning
• Andrea Iannucci: Machine learning for directional movement prediction of US corp
• Weiqing Cao: LMM to FMM: an illustration of SONIA cap pricing
• Félix Eychenne: Correctly pricing continuous barrier contracts
• Alberto Moreno de Vega Garcıa: Deep solvers
• Callum Rough: The rough Bergomi model: from motivation to implementation
• Yuchen Yan: Financial bubble prediction with neural networks
• George Akerman: Empirical Bond Pricing with Affine Models
• Yuvraj Anand: Algorithmic Market-Making for Options
• Xingyu Du: Predicting Economic Recessions Using Signature Kernels
• Jan Jasper Eckstein: Low Latency Finance
• Konstantinos Evangelides: Backward Stochastic Differential Equations
• Chun Hei Fan: Application of Schrodinger's Bridge in Volatility Models
• Yijun Fu: Dynamic Portfolio Optimisation under Multiscale Stochastic Structures
• Zhenya Grigoriev: General Signature Kernel for Time Series Modelling
• Tom Ham: Actor-Critic Reinforcement Learning Methods for Electronic Market Making
• Yizhe He: CIR++ vs Shifted Squared Vasicek in Interest Modelling
• Jay Jethwa: Uncertain Volatility Model with Applications to Cliquets
• Balint Zoltan Keresztfalvi: Pricing of Bermudan Swaptions in the Cheyette Model
• James Leach: Dividend Modelling and the Particle Method
• Tianyu Luo: Deep Hedging with Transaction Costs and Risk Preferences
• Manola Meconcelli:
• Nilesh Ramnarain: Harvesting Volatility Risk Premia using Deep Reinforcement Learning
• Bassam Sinan: Deep hedging of Autocallables with rough Bergomi model
• Etienne Wallerich: Arbitrage Detection with Quantum Annealing
• Tianhao Wang: Reinforcement Learning Provides Free-Lunch, but at What Cost?
• Kexin Xie: Ranking of Covariance Forecasts by Robust Loss Functions
• Jiaxin Xu: Delta Hedging Convertible Bonds with Credit Risk
• Junwei Yuan: Deep learning for unconstrained Markov regime-switching quadratic utility maximisation
• Bo Yuan: Deep learning interpretability of the parameters to smile map in a rough volatility model
• Jing Zhang: Machine Learning in Credit Risk
• Zihui Zhao: Random Matrix Theory: Moment expansions, algebra and combinatorics
• Andrew Alden:  NLP for Financial Chat Message Classification ‌
• Hinrik Bergs:  Neural Rough Differential Equations for Long Time Series and Classification of D ‌
• Arvid Bertermann:  Reinforcement learning trading strategies with limit orders and high-frequency signals
• Joseph Burrin:  Implementing an IR-FX Model for CVA Calculation
• Millie Deng:  Study of the Conformance Anomaly Detection Algorithm on Streamed Data
• Jingxuan Dong:  Efficient Methods for Dynamical Initial Margin Modelling
• Wiam El Mouden:  Deep intensity-based CVA with Wrong Way Risk
• Sami Farih:  A Risk Methodology for Cross-Asset Volatility Trading Strategies
• Si Cheng Fong:  Distributional Prediction of Foreign Exchange Rates with Mixture Density Network
• Kian Hatamieh:  European Government Bond Volume Prediction Using Dealer to Client Flow
• Jonah Humphreys:  ML and Artificial Neural Networks Applied to Forecasting Energy Commodity Prices
• Jiarou Li:  Utility Maximizastion in Regime-Switching Markets with Full and Partial Informat
• Minghao Li:  Uncertain Volatility Model for Option Pricing
• Viola Pu:  Pricing Options using Deep Neural Networks from a Practical Perspective
• Winter Shi:  Deep Hedging under Reweighted Asset Measure
• Xiaofu Tang:  Use of Kernel Methods for Dynamic Hedging in Incomplete Markets
• Jiajie Tao:   Semilinear PDEs for Valuation under Credit, Collateral and Funding
• Tzyy Tong:  Reinforcement Learning with Continuous Controls for Foreign Exchange Trading
• Marianne Toscano:   Deep Structural-based CVA with Wrong Way Risk
• Konstantinos Tsoulias:  Bitcoin Trading Strategies Based on Twitter Sentiment Analysis
• Lidan Xing:  Fx Trading Strategy Using Rough Path Signature
• Baofeng Ye: Discrete Dynamic Risk Constraints for Controlling Tail-risk Seeking Traders
• Tao Yu:  Bootstrapping Past Inflation Returns
• Kaixuan Zhou:  Connecting Caplets and Swaptions in the Displace Diffusion Market Model
• Xingyao Zhu:  Improvements on Target Risk Portfolio in Strategic Asset Allocation
• Hendrik Zimmermann:  Intraday Trading with Neural Networks and Deep Reinforcement Learning
• Nassim Amer-Ouali:  Calibrability of first-to-default correlation structure
• Pierre-Alexis Corpechot:  Study of the joint S&P 500/VIX smile calibration problem within rough volatility
• Hicham El Jerrari:  Robust option pricing: the uncertain volatility model
• Qingxin Geng:  Dynamically controlled kernel estimation for XVA pricing and options replication
• Thomas Hengstberger:  Increasing Venture Capital Investment Success Rates Through Machine Learning
• Xiaoshan Huang:  Interpretability in Deep Learning for Heston Smile Modeling Calibration
• Chenhao Jin:  Truncated Order Decision for Signature Least Square Regression Model Under the P
• Haoyin Lin:  Dynamic convex duality and backward stochastic differential equations in utility
• Chenyu Liu:  Deep Reinforcement Learning and Electronic Market Making
• Shibo  Lu:  Harvesting Volatiltiy Risk Premium
• Conor McIndoe:  A data driven approach to market regime classification
• Jeremy Marc:  Prediction of financial bubbles and backtesting of a trading strategy
• Clea Morand:  Predicting US Stock Returns Using Closing Auction Imbalance Data
• Peixuan Qin:  Pricing and Hedging of Derivatives by Unsupervised Deep Learning
• Umor Sami:  Discrete Hedging and Pricing of European Options using Reinforcement Learning
• Francesco Sciacovelli:  Interest Rate Prediction with Twitter Sentiment
• Viraj Shah:  Optimal Stopping Problems: Autonomous Trading Over an Infinite Time Horizon
• Jianxiong Sun:  Stochastic control problem with constrained condition and random drift
• Hafsae Tabti:  Utility Maximisation
• Hugues Thorin:  Artificial Neural Networks for SABR model calibration & hedging
• Yuchen Tu:  Predicting High-Frequency Stock Market by Neural Networks
• Niklas Walter:  A Jump-Diffusion Model for Credit Risk with Endogenous Contagion
• Toby Weston:  Distributional Reinforcement Learning for Optimal Execution
• Ruizhe Xia:  An Automated Approach on Generating Macro-Economic Sentiment Index Through Centr
• Xinyu Yan:  Forecasting Cryptocurrency pricing
• Chenyu Yang:  Dual control methods for tight bounds of value function when the drift following
• Yaozhang Wang:  Unconstrained utility maximization problem via four methods
• Yifan Zheng:  Robust Option Pricing
• Jietao Zhou:  Constrained quadratic risk minimization problem via four different approaches
• Tara Aghajani:  Solving high-dimensional non-linear PDE using deep learning
• Majd Agoumi:  Hedging Dividend Futures
• Benyamin Azoulay:  Iceberg orders and shapes of the limit order book
• Apolline Bonnerre:  Automated valuation platform for vanilla products
• Lewis Brown:  A Machine Learning Approach to Cardinality Constrained Portfolio Optimisation ‌
• Sahil Chadha:  Classification-based Prediction of Gadget Time Series Using Machine Learning ‌
• Zuming Gao:  Optimal Trading strategy and trading behaviour
• Xinnan Gu:  Machine Learning For Foreign Exchange Rate Forecasting
• Justin Gwee:  Stochastic Optimisation under Probability Distortion ‌
• Ghada Hamieh:  LIBOR Discontinuation ‌
• Elissa Ibrahim:  Constant Maturity Swap Pricing ‌
• Ruizhi Kong:  Alpha in Short and Long Term Bond Market ‌
• Francois Le Dain:  Trading volume forecasting in the equity market using machine learning
• Thomas Leygonie:  Reinforcement learning for derivatives pricing and uncertain volatility model
• Justin Li:  A Machine Learning Approach to Improve the Pegging Algorithm ‌
• Remi Mouzayek:  Extraction of relation-objects from documents using a weakly supervised setting
• Mohamed Taik:  Robust Option Pricing, Taik ‌
• Xiyu Yang:  Optimal Investment with discretionary stopping and trading constraints ‌ ‌
• Jonathas Castello Branco:  ETF Market Making
• Francois Cluzeau:  Hazard rate surface model and its implication to SouthernEuropean sovereign bond
• Antoine Collas:  Option and CVA Greeks with adjoint algorithmic differentiation
• Jason de La Borie De La Batut:  Advanced methods in portfolio optimisation for trading strategies and smart beta
• Alexander Done:  Static and Dynamic Execution Strategies ‌
• Laurids Gert Nielsen:  Machine Learning For Foreign Exchange Rate Forecasting ‌
• William Goldberg:  An assessment of the validity of linear propagator models
• Kees Groeneweg:  On the class of modulated volterra stochastic volatility processes
• Marjorie Grootenboer:  Model risk management for market risk
• Kanstantsin Kulak:  Performance improvements for risk balances portfolios
• Haibo Li:  Gaussian process regression: a machine learning approach to derivatives pricing
• Yilang Lu:  Application of clustering method to trading strategy in the US equity market
• James McIndoe: Tactical asset allocation strategies derived using sentiment analysis
• Nelson Okou: Fractional Brownian motion and machine learning for variance prediction
• Luis Eduardo Pavon Tinoco:  Application of Stochastic Control in Optimal Execution Algorithms ‌ ‌
• Zhentian Qiu:  Classify Neural Networks in Credit Scoring area based on the financial ratios ‌
• Henry Sorsky:  Factor Investing in the Automotive Sector ‌
• Ghali Tadlaoui:  Intelligent Portfolio Construction: Machine Learning enabled ‌
• Turker Temel:  Rough stochastic volatility and applications of the rough Bergomi model
• Mehdi Tomas:  Pricing and calibration of stochastic models via neural networks
• James Edbooke:  Time Series Modelling Technique Analysis for Enterprise Stress Testing
• Inass El Harrak:  An Application of Importance Sampling to the Evaluation of the Economic Capital
• Jessy Hu:  Liquidity Risk Arising from Margin Requirements
• Shijun Liu:  A Mathematical Solution to Seeking Arbitrage Opportunity in M&A
• Alexandre Maraval:  Indicators of Risk Appetite and Applications on Trading
• Fei Wang:  Forward Variance Dynamics: Bergomi Model and its Applications in Pricing Cliquet  
• Yafan Wang:  Applications of Recurrent Neural Network on Financial Time Series
• Haixia Zhong:  Malliavin Calculus Applied to Monte Carlo Methods in Mathematical Finance
• Cheng Luo:  On the Calibration of the SABR Model and its Extensions
• Kyriacos Neocleous:  Longevity Risk: An Intensity-Based Approach
• Nadya Patricia:  Approximation Error in Dependence Iteration for Default Modelling

MSc in Mathematical and Computational Finance

• Entry requirements
• Funding and costs

College preference

• How to apply

About the course

The MSc in Mathematical and Computational Finance provides you with a strong mathematical background and the skills necessary to apply your expertise to the solution of problems.

You will develop skills to formulate mathematical problems that are based on the needs of the financial industry. You will carry out relevant mathematical and financial analysis, develop and implement appropriate tools to present and interpret model results.

The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution.

Structure and content

You will take four introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics, financial markets and instruments, and Python.

The first term will then focus on compulsory core material, offering 64 hours of lectures and 24 hours of classes, plus one compulsory computing course offering 16 hours of lectures. 

Core courses

• Stochastic Calculus (16 lectures, and 4 classes of 1.5 hours each)
• Financial Derivatives (16 lectures, and 4 classes of 1.5 hours each)
• Numerical Methods (16 lectures, and 4 classes of 1.5 hours each)
• Statistics and Financial Data Analysis (16 lectures, and 4 classes of 1.5 hours each)

Computing course

• Financial computing with C++ I (16 hours of lectures, plus 4 classes of 2 hours each over weeks 1-9)

The second term will be a combination of core material, offering 48 hours of lectures (18 hours of classes) and 48 hours of electives.

• Deep Learning (16 lectures, and 4 classes of 1.5 hours each)
• Quantitative Risk Management (8 lectures, and 2 classes of 1.5 hours each)
• Stochastic Control (8 lectures, and 2 classes of 1.5 hours each)
• Fixed Income (16 lectures, and 4 classes of 1.5 hours each)

Elective courses

A number of elective courses will be offered, of which you will choose four options. Courses usually offered include: 

• Advanced Volatility Modelling (8 lectures, and 2 classes of 1.5 hours each)
• Advanced Monte Carlo Methods (8 lectures, and 2 classes of 1.5 hours each)
• Advanced Topics in Computational Finance (8 lectures, and 2 classes of 1.5 hours each)
• Asset Pricing (8 lectures, and 2 classes of 1.5 hours each)
• Market Microstructure and Algorithmic Trading (8 lectures, and 2 classes of 1.5 hours each)
• Decentralised Finance (8 lectures, and 2 classes of 1.5 hours each)
• Financial computing with C++ II (24 hours of lectures and classes)

The third term is mainly dedicated to a dissertation project which is to be written on a topic chosen in consultation with your supervisor. This may be prepared in conjunction with an industry internship.

The course is full-time and requires attendance in Oxford. Full-time students are subject to the University's Residence requirements.

Resources to support your study

As a graduate student, you will have access to the University's wide range of world-class resources including libraries, museums, galleries, digital resources and IT services.

The Bodleian Libraries is the largest library system in the UK. It includes the main Bodleian Library and libraries across Oxford, including major research libraries and faculty, department and institute libraries. Together, the Libraries hold more than 13 million printed items, provide access to e-journals, and contain outstanding special collections including rare books and manuscripts, classical papyri, maps, music, art and printed ephemera.

The University's IT Services is available to all students to support with core university IT systems and tools, as well as many other services and facilities. IT Services also offers a range of IT learning courses for students, to support with learning and research.

The Mathematical Institute's home is the purpose-built Andrew Wiles Building, opened in 2013. This provides ample teaching facilities for lectures, classes and seminars. The Mathematical Institute provides IT support, and students can use the department's Whitehead Library, with an extensive range of books and journals. In addition to the common room, where graduate students regularly gather for coffee and other social occasions, there is also a café in the Andrew Wiles Building.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Mathematical Institute and 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 Mathematical Institute.

You will be assigned an initial supervisor on arrival in Oxford whose role is to act as an academic advisor during the first two terms of the course. In the third term, your supervisor will usually change when you start work on your dissertation.

The examination will consist of the following elements:

• Three written examinations assessing the core material in the first and second terms
• One written examination assessing elective material in the second term
• Two projects assessing one of the core courses in the first term and one of the core courses in the second term
• Two practical examinations assessing two courses in financial computing with C++
• One dissertation in the third term.

Graduate destinations

MSc graduates have been recruited by prominent investment banks and hedge funds. Many past students have also progressed to PhD-level studies at leading universities in Europe and elsewhere.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University 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 if a pandemic, epidemic or local health emergency occurs. In addition, 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 page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2025-26

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

We know that factors such as socio-economic circumstances and school performance can make it difficult for students to demonstrate their full potential. This course is taking part in an initiative to use contextual data to help us to better understand your achievements in the context of your individual background. For further details, please refer to the information about improving access to graduate study in the How to apply section of this page.

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. Contextual data may also be used in the assessment of studentships. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours in mathematics or a related discipline. 

Applicants should have a background in probability, statistics, ordinary and partial differential equations, linear algebra and analysis. They must demonstrate their aptitude for, and knowledge of, mathematics, particularly in the area of real analysis, through their application. Applicants with undergraduate degrees that are not purely mathematical will still be expected to demonstrate they have sufficient knowledge to perform well on the course.

For applicants with a degree from the USA, the minimum overall GPA that is normally required to meet the undergraduate-level requirement is 3.6 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

Publications are not expected.

English language proficiency

This course requires proficiency in English at the University's  higher level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides  further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are not normally held for this course. 

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the About section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Mathematics

Mathematics has been studied in Oxford since the University was first established in the 12th century. The Mathematical Institute aims to preserve and expand mathematical culture through excellence in teaching and research.

The Mathematical Institute offers a wide range of graduate courses, including both taught master’s courses and research degrees. Research and teaching cover the spectrum of pure and applied mathematics with researchers working in fields including:

• number theory
• combinatorics
• mathematical physics
• mathematical finance
• mathematical modelling
• mathematical biology networks
• numerical analysis.

The Mathematical Institute is proud to have received an Athena SWAN silver award renewal in 2021, reflecting its commitment to promoting diversity and to creating a working environment in which students and staff alike can achieve their full potential.

Graduate students are an integral part of the department, interacting with each other and with academic staff as part of a vibrant community that strives to further mathematical study. As a graduate student at Oxford you will benefit from excellent resources, extensive training opportunities and supportive guidance from your supervisor or course director.

The Mathematical Institute has strong ties with other University departments including Computer Science, Statistics and Physics, teaching several courses jointly. Strong links with industrial and other partners are also central to the department.

The institute’s home is the purpose-built Andrew Wiles Building, opened in 2013. This provides ample teaching facilities for lectures, classes and seminars. The Mathematical Institute provides six lecture theatres and six classrooms.

Graduate students have access to the department common room, where members of the department regularly gather for coffee and other social occasions, and the mezzanine level of the Andrew Wiles Building houses a café and teaching spaces.

View all courses   View taught courses View research courses

For entry in the 2025-26 academic year, the collegiate University expects to offer over 1,000 full or partial graduate scholarships across a wide range of graduate courses.

If you apply by the January deadline shown on this page and receive a course offer, your application will then be considered for Oxford scholarships. For the majority of Oxford scholarships, your application will automatically be assessed against the eligibility criteria, without needing to make a separate application. There are further Oxford scholarships available which have additional eligibility criteria and where you are required to submit a separate application. Most scholarships are awarded on the basis of academic merit and/or potential.

To ensure that you are considered for Oxford scholarships that require a separate application, for which you may be eligible,  use our fees, funding and scholarship search tool  to identify these opportunities and find out how to apply. Alongside Oxford scholarships, you should also consider other opportunities for which you may be eligible including  a range of external funding ,  loan schemes for postgraduate study  and any other scholarships which may also still be available after the January deadline as listed on  our fees, funding and scholarship search tool .

Details of college-specific funding opportunities can also be found on individual college websites:

Select from the list:

Please refer to the College preference section of this page to identify which of the colleges listed above accept students for this course.

For the majority of college scholarships, it doesn’t matter which college, if any, you state a preference for in your application. If another college is able to offer you a scholarship, your application can be moved to that college if you accept the scholarship. Some college scholarships may require you to state a preference for that college when you apply, so check the eligibility requirements carefully.

Further information about funding opportunities for this course can be found on the department's website.

Annual fees for entry in 2025-26

Information about course fees

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). For courses lasting longer than one year, 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. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

If your application is successful, you will be asked to pay a deposit against your course fees at the application stage as a condition of your offer. The deposit amount and date by which payment must be made are shown below.

The department's website provides  further information about deposits for this course .

Where can I find further information about fees?

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

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees and living costs. However, as part of your course requirements, you may need to choose a dissertation, a project or a thesis topic. Please note that, depending on your choice of 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 these expenses.

Living costs

In addition to your course fees and any additional course-specific costs, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

Living costs for full-time study

For the 2025-26 academic year, the range of likely living costs for a single, full-time student is between £1,425 and £2,035 for each month spent in Oxford. We provide the cost per month so you can multiply up by the number of months you expect to live in Oxford. Depending on your circumstances, you may also need to budget for the  costs of a student visa and immigration health surcharge and/or living costs for family members or other dependants that you plan to bring with you to Oxford (assuming that dependant visa eligibility criteria are met).

Further information about living costs

The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. For study in Oxford beyond the 2025-26 academic year, it is suggested that you budget for potential increases in living expenses of around 4% each year – although this rate may vary depending on the national economic situation. For further information, please consult our more detailed information about living costs , which includes a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . 

If you are a current Oxford student and you would like to remain at your current Oxford college, you should check whether it is listed below. If it is, you should indicate this preference when you apply. If not, you should contact your college office to ask whether they would be willing to make an exception. Further information about staying at your current college can be found in our Application Guide. 

The following colleges accept students on the MSc in Mathematical and Computational Finance:

• Christ Church
• Exeter College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Lincoln College
• Magdalen College
• Mansfield College
• New College
• Oriel College
• Pembroke College
• Reuben College
• St Anne's College
• St Catherine's College
• St Cross College
• St Edmund Hall
• St Hilda's College
• St Hugh's College
• St John's College
• St Peter's College
• Somerville College
• Wadham College
• Wolfson College
• Wycliffe Hall

Before you apply

Our guide to getting started provides general advice on how to prepare for and start your application. You can use our interactive tool to help you evaluate whether your application is likely to be competitive .

If it is important for you to have your application considered under a particular deadline – eg under the January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the information about deadlines and when to apply in our Application Guide.

Application fee waivers

An application fee of £75 is payable for each application to this course. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Do I need to contact anyone before I apply?

You do not need to make contact with the department before you apply but you are encouraged to visit the relevant departmental webpages to read any further information about your chosen course.

If you have any questions, you are welcome to make contact with the  Course Director . It is not necessary to contact a potential supervisor as this will be arranged on your arrival.

Improving access to graduate study

This course is taking part in initiatives to improve the selection procedure for graduate applications, to ensure that all candidates are evaluated fairly.

Socio-economic data (where it has been provided in the application form) and your contextual statement (if you choose to provide one) will be used as part of an initiative to contextualise applications at the different stages of the selection process.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

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Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Your references will support intellectual ability, academic achievement and motivation. At least two of the references must be academic.

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You can find  more information about the contextual statement  on our page that provides details of the continuing pilot programme to improve the assessment procedure for graduate applications.

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This will be assessed for:

• your reasons for applying
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• ability to absorb new ideas, often presented abstractly, at a rapid pace. 

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• PhD in Mathematical Finance

The PhD in Mathematical Finance is for students seeking careers in research and academia. Doctoral candidates will have a strong affinity for quantitative reasoning and the ability to connect advanced mathematical theories with real-world phenomena. They will have an interest in the creation of complex models and financial instruments as well as a passion for in-depth analysis.

Learning Outcomes

The PhD curriculum has the following learning goals. Students will:

• Demonstrate advanced knowledge of literature, theory, and methods in their field.
• Be prepared to teach at the undergraduate, master’s, and/or doctoral level in a business school or mathematics department.
• Produce original research of quality appropriate for publication in scholarly journals.

After matriculation into the PhD program, a candidate for the degree must register for and satisfactorily complete a minimum of 16 graduate-level courses at Boston University. More courses may be needed, depending on departmental requirements.

PhD in Mathematical Finance Curriculum

The curriculum for the PhD in Mathematical Finance is tailored to each incoming student, based on their academic background. Students will begin the program with a full course load to build a solid foundation in not only math and finance but also the interplay between them in the financial world. As technology plays an increasingly larger role in financial models, computer programming is also a part of the core coursework.

Once a foundation has been established, students work toward a dissertation. Working closely with a faculty advisor in a mutual area of interest, students will embark on in-depth research. It is also expected that doctoral students will perform teaching assistant duties, which may include lectures to master’s-level classes.

Course Requirements

The minimum course requirement is 16 courses (between 48 and 64 units, depending on whether the courses are 3 or 4 units each). Students’ course choices must be approved by the Mathematical Finance Director prior to registration each term. The following is a typical program of courses.

• CAS EC 701 Microeconomic Theory
• CAS MA 711 Real Analysis
• CAS MA 779 Probability Theory I
• QST FE 918 Doctoral Seminar in Finance
• CAS EC 703 Advanced Microeconomic Theory
• CAS MA 776 Partial Differential Equations
• CAS MA 781 Probability Theory 2
• QST FE 920 Advanced Capital Market Theory
• CAS EC 702 Macroeconomic Theory
• CAS MA 783 Advanced Stochastic Processes
• QST MF 850 Advanced Computational Methods
• QST MF 922 Advanced Mathematical Finance
• CAS EC 704 Advanced Microeconomic Theory
• CAS MA 751 Statistical Machine Learning
• QST MF 810 FinTech Programming
• QST MF 921 Topics in Dynamic Asset Pricing

Additional Requirements

Qualifying examination.

Students must appear for a qualifying examination after completion of all coursework to demonstrate that they have:

• acquired advanced knowledge of literature and theory in their area of specialization;
• acquired advanced knowledge of research techniques; and
• developed adequate ability to craft a research proposal.

Guidelines for the examination are available from the departments. Students who do not pass either the written and/or oral comprehensive examination upon first try will be given a second opportunity to pass the exam. Should the student fail a second time, the student’s case will be reviewed by the Mathematical Finance Program Development Committee (MF PDC), which will determine if the student will be withdrawn from the PhD program. In addition, the PhD fellowship (if applicable) of any student who does not pass either the written and/or oral comprehensive examination after two attempts will be suspended the term after the exam was attempted.

Dissertation

Following successful completion of the qualifying examination, the student will develop a research proposal for the dissertation. The final phase of the doctoral program is the completion of an approved dissertation. The dissertation must be based on an original investigation that makes a substantive contribution to knowledge and demonstrates capacity for independent, scholarly research.

Doctoral candidates must register as continuing students for DS 999 Dissertation, a 2-unit course, for each subsequent regular term until all requirements for the degree have been completed. PhD students graduating in September are required to register for Dissertation in Summer Session II preceding graduation.

Academic Standards

Time limit for degree completion.

After matriculation into the PhD program, a candidate for the degree must meet certain milestones within specified time periods (as noted in the table below) and complete all degree requirements within six years of the date of first registration. Those who fail to meet the milestones within the specified time, or who do not complete all requirements within six years, will be reviewed by the PhD PDC and may be dismissed from the program. A Leave of Absence does not extend the six-year time limit for degree completion.

Performance Review

The Mathematical Finance Program Development Committee will review the progress of each doctoral candidate. Students must maintain a 3.30 cumulative grade point average in all courses to remain in good academic standing. Students who are not in good academic standing will be allowed one term to correct their status. Prior to the start of the term, the student must submit a letter to the Faculty Director (who will forward it to the PDC) explaining why the student has fallen short of the CGPA requirement and how the student plans to correct the situation. Failure to increase the CGPA to acceptable levels may result in probation or withdrawal from the program, at the discretion of the PhD Program Development Committee (PDC).

Graduation Application

Students must submit a graduation application at least five months before the date they expect to complete degree requirements. It is the student’s responsibility to initiate the process for graduation. The application is available online and should be submitted through the Specialty Master’s & PhD Center website for graduation in January, May, or August.

If graduation must be postponed beyond the term for which the application is submitted, students should contact the Specialty Master’s & PhD Center to defer the date. If students wish to postpone their graduation date past the six-year time limit for completion, they must formally petition the PhD Program Development Committee (PDC) for an extension. The petition, which must include the reason(s) for the extension as well as a detailed timetable for completion, is subject to departmental and PDC approval.

PhD degree requirements are complete only when copies of the dissertation have been certified as meeting the standards of Questrom School of Business and have been accepted by Mugar Memorial Library.

Related Bulletin Pages

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• Questrom PhD in Mathematical Finance Course Requirements
• Questrom PhD Program Admissions
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• Minor in Innovation & Entrepreneurship
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(2019) PhD thesis, London School of Economics and Political Science. --> - Submitted Version The first part of this thesis deals with the consideration of thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elastic demand functions. Noncompetitive equilibrium prices and allocations result as an outcome of a game among traders. General sufficient conditions for existence and uniqueness of such equilibrium are provided, with an extensive analysis of two-trader transactions. Even though strategic behaviour causes inefficient social allocations, traders with sufficiently high risk tolerance and/or large initial exposure to market risk obtain more utility gain in the noncompetitive equilibrium, when compared to the competitive one. The second part of this thesis considers a continuum of potential investors allocating funds in two consecutive periods between a manager and a market index. The manager’s alpha, defined as her ability to generate idiosyncratic returns, is her private information and is either high or low. In each period, the manager receives a private signal on the potential performance of her alpha, and she also obtains some public news on the market’s condition. The investors observe her decision to either follow a market neutral strategy, or an index tracking one. It is shown that the latter always results in a loss of reputation, which is also reflected on the fund’s flows. This loss is smaller in bull markets, when investors expect more managers to use high beta strategies. As a result, a manager’s performance in bull markets is less informative about her ability than in bear markets, because a high beta strategy does not rely on it. We empirically verify that flows of funds that follow high beta strategies are less responsive to the fund’s performance than those that follow market neutral strategies. Item Type: Thesis (PhD) Additional Information: © 2019 Georgios Vichos Library of Congress subject classification: Sets: Supervisor: Kardaras, Konstantinos URI: Actions (login required) Record administration - authorised staff only Downloads per month over past year View more statistics Dissertations Most Harvard PhD dissertations from 2012 forward are available online in DASH , Harvard’s central open-access repository and are linked below. Many older dissertations can be found on ProQuest Dissertation and Theses Search which many university libraries subscribe to. Mathematical Models in Finance Conference paper Cite this conference paper Maria do Rosário Grossinho 2   1773 Accesses In this paper we illustrate the interplay between Mathematics and Finance, pointing out the relevance of stochastic calculus and mathematical modelling in some important aspects of modern finance. We present two types of mathematical models: the binomial asset pricing model and continuous-time models. We point out some sensitive points of research. This is a preview of subscription content, log in via an institution to check access. Access this chapter Subscribe and save. Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Available as PDF Read on any device Instant download Own it forever Compact, lightweight edition Dispatched in 3 to 5 business days Free shipping worldwide - see info Durable hardcover edition Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Unable to display preview.  Download preview PDF. Similar content being viewed by others Stochastic Processes for Asset Price Modelling Excursion to Financial Economics: A Radically Elementary Approach to the Fundamental Theorems of Asset Pricing Model Risk and Uncertainty—Illustrated with Examples from Mathematical Finance Bachelier, L. “Théorie de la Spéculation”, Ann. Sci. Ecole Norm. Sup. 17 (1900) 21–86 [English translation in P. Cootner (ed.) The Random Character of Stock Prices, MIT Press, 1964, reprinted Risk Books, London 2000]. MathSciNet   Google Scholar   Black, F. and Scholes, M. “The pricing of options and corporate liabilities”, J. Political Econom. 81 , 1973, 637–654. Article   Google Scholar   Cox, J.; Ross S. and Rubinstein, M. “Option pricing, a simplified approach”, J. Financial Economics 7 , 1979, 229–263. Article   MATH   Google Scholar   Doob, J. L. Stochastic Processes . New York, Wiley, 1953. MATH   Google Scholar   Einstein, A. “Uber die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten”, Teilchen. Annalen der Physik 17 , 1905, 549–560. Harrison, J. M. and Kreps, D. M. “Martingales and arbitrage in multiperiod securities markets”, J. Econ. Theory 20 , 1979, 381–408. Article   MATH   MathSciNet   Google Scholar   Harrison, J. M. and Pliska, S. R. “Martingales and stochastic integrals in the theory of continuous trading”, Stoch. Proc. and Appl. 110 , 1981, 215–260. Article   MathSciNet   Google Scholar   Itô, K. “Stochastic Integral”, Proc. Imp. Acad. Tokyo 20 , 1944, 519–524. Itô, K. “On a formula concerning stochastic differentials”, Nagoya Math. J. 3 , 1951, 55–65. MATH   MathSciNet   Google Scholar   Kolmogorov, A. N. On Analytic Methods in Probability Theory , in A. N. Shiryaev, ed., Selected Works of A. N. Kolmogorov; Volume II: Probability Theory and Mathematical Statistics, Dordrecht, Kluwer, pp. 62–108, 1992. [Original: Uber die analytischen-Methoden in der Wahrscheinlichkeitsrechnung, Math. Ann. 104, (1931) 415–458.] Google Scholar   Markowitz, H. “Portfolio Selection”, Journal of Finance 7 (1), 1952, 77–91. Merton, R. C. “Theory of rational option pricing”, Bell J. Econom. Manag. Sci. 4 , 1973, 141–183. Samuelson, P. “Mathematics of Speculative Price”, SIAM Review 15 , 1973, 1–42. Samuelson, P. “Modern finance theory within one lifetime”, Mathematical Finance — Bachelier Congress 2000, eds. Geman, H., Madan, D., Pliska, S. R., and T. Vorst; Springer-Verlag, Heidelberg, 2002, pp. 41–46. Shiryaev, A. N. Essentials of Stochastic Finance. Facts, Models, Theory . World Scientific, 1999. Wiener, R. “Differential Space”, J. Math. Phys. 2 , 1923, 131–174. Download references Author information Authors and affiliations. Instituto Superior de Economia e Gestão, Universidade Técnica de Lisboa, Rua do Quelhas, 6, 1200-781, Lisboa, Portugal Maria do Rosário Grossinho You can also search for this author in PubMed   Google Scholar Editor information Editors and affiliations. Technical University of Lisbon, Lisbon, Portugal Manuel Seabra Pereira Rights and permissions Reprints and permissions Copyright information © 2007 Springer About this paper Cite this paper. Grossinho, M.d.R. (2007). Mathematical Models in Finance. In: Pereira, M.S. (eds) A Portrait of State-of-the-Art Research at the Technical University of Lisbon. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5690-1_5 Download citation DOI : https://doi.org/10.1007/978-1-4020-5690-1_5 Publisher Name : Springer, Dordrecht Print ISBN : 978-1-4020-5689-5 Online ISBN : 978-1-4020-5690-1 eBook Packages : Engineering Engineering (R0) Share this paper Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Publish with us Policies and ethics Find a journal Track your research Google Custom Search Wir verwenden Google für unsere Suche. Mit Klick auf „Suche aktivieren“ aktivieren Sie das Suchfeld und akzeptieren die Nutzungsbedingungen. Hinweise zum Einsatz der Google Suche Chair of Mathematical Finance TUM School of Computation, Information and Technology Technical University of Munich Bachelor Theses The prerequisite for a bachelor's thesis in the research group are certificates or passed examinations in at least 2 of 3 of the following courses: Financial Mathematics 1 (MA3407) Insurance Mathematics 1 (MA3405)   Bachelor's seminar in the financial and actuarial research group Please send your complete application documents to [email protected] . A decision on your application will be made by the research group according to available supervision capacities and you will be informed. Information Sheet on Bachelor Theses at the Research Group Finance and Actuarial Science The LaTeX template for Bachelor Theses at M13 can be downloaded from here: Template Bachelor Theses Please note that theses at Department of Mathematics have to be organized according to a specific format. 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Bachelor thesis, 2003 more… Search form Travel & Maps Our Building Supporting Mathematics Art and Oxford Mathematics Equality, Diversity & Inclusion Undergraduate Study Postgraduate Study Current Students Research Groups Case Studies Faculty Books Oxford Mathematics Alphabet Oxford Online Maths Club Oxford Maths Festival It All Adds Up Problem Solving Matters PROMYS Europe Oxfordshire Maths Masterclasses Outreach Information Mailing List Key Contacts People List A Global Department Research Fellowship Programmes Professional Services Teams Conference Facilities Public Lectures & Events Departmental Seminars & Events Special Lectures Conferences Summer Schools Past Events Alumni Newsletters Info for Event Organisers & Attendees Mathematical and Computational Finance @ Oxford The  Oxford Mathematical and Computational Finance Group  is one of the world's leading research groups in the area of mathematical modeling in finance.  Research Topics  include stochastic processes, derivative pricing, multi-level Monte Carlo methods, computational methods for PDEs, credit risk modelling, quantitative risk management,  data-driven modeling and machine learning, market microstructure and high-frequency modeling, macro-financial modelling, agent-based modelling and systemic risk. The group is a partner in the  Centre for Doctoral Training in Mathematics of Random Systems . Research Topics Faculty and DPhil Students Seminars and workshops Stochastic Analysis and Mathematical Finance Seminar (Monday 15:30-16:30) Mathematical & Computational Finance seminar (Thursday 16:00-17:00)   Frontiers in Quantitative Finance Seminar Series Past events Useful Links MCF Advisory Board Oxford Martin Program on Systemic Resilience Oxford Suzhou Centre for Applied Research 牛津大学高等研究院（苏州） Study with us DPhil (PhD) studies in Mathematical Finance Centre for Doctoral Training in Mathematics of Random Systems MSc in Mathematical and Computational Finance Email:  @email Phone:  +44 (0)1865 615234 Join us on  LinkedIn  or sign up to our newsletter USF Research USF Libraries Digital Commons @ USF > College of Arts and Sciences > Mathematics and Statistics > Theses and Dissertations Mathematics and Statistics Theses and Dissertations Theses/dissertations from 2024 2024. The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang Theses/Dissertations from 2023 2023 Statistical Analysis of Ribonucleotide Incorporation in Human Cells , Tejasvi Channagiri Matrix Models of 2D Critical Phenomena , Nathan Hayford Data-Driven Learning Algorithm Via Densely-Defined Multiplication Operators and Occupation Kernels. , John Kyei Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani Applied Analysis for Learning Architectures , Himanshu Singh Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze Recovering generators of principal ideals using subfield structure and applications to cryptography , William Youmans Theses/Dissertations from 2022 2022 Application of the Riemann-Hilbert method to soliton solutions of a nonlocal reverse-spacetime Sasa-Satsuma equation and a higher-order reverse-time NLS-type equation , Ahmed Ahmed New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana Advances and Applications of Optimal Polynomial Approximants , Raymond Centner Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly Methods in Discrete Mathematics to Study DNA Rearrangement Processes , Lina Fajardo Gómez Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He Adversarial and Data Poisoning Attacks against Deep Learning , Jing Lin Exploring the Vulnerability of A Neural Tangent Generalization Attack (NTGA) - Generated Unlearnable CIFAR-10 Dataset , Gitte Ost Statistical Methods for Reliability Test planning and Data Analysis , Oluwaseun Elizabeth Otunuga Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias Effective Statistical and Machine Learning Methods to Analyze Children's Vocabulary Learning , Houston T. Sanders Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman Theses/Dissertations from 2021 2021 Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang Theses/Dissertations from 2020 2020 Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala Theses/Dissertations from 2019 2019 Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado Power Graphs of Quasigroups , DayVon L. Walker Theses/Dissertations from 2018 2018 Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields Generalizations of Quandles and their cohomologies , Matthew J. Green Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou Human Activity Recognition Based on Transfer Learning , Jinyong Pang Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang Theses/Dissertations from 2017 2017 Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati Dynamics of Multicultural Social Networks , Kristina B. Hilton Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally Patterns in Words Related to DNA Rearrangements , Lukas Nabergall Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na Schreier Graphs of Thompson's Group T , Allen Pennington Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou Theses/Dissertations from 2016 2016 A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park Leonard Systems and their Friends , Jonathan Spiewak Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu Advanced Search Email Notifications and RSS All Collections USF Faculty Publications Open Access Journals Conferences and Events Theses and Dissertations Textbooks Collection Useful Links Mathematics and Statistics Department Rights Information SelectedWorks Submit Research Home | About | Help | My Account | Accessibility Statement | Language and Diversity Statements Privacy Copyright Department of Mathematics Mathematical finance and stochastic analysis Our research interests span a broad range of topics in continuous and discrete time. In mathematical finance our areas of research activity include: arbitrage and option pricing in markets with friction and incomplete markets entropy and financial value of information optimal investment strategies in markets, with prices depending on the volume of trading robust arbitrage and model-independent pricing discrete time models and their continuous time limits in the presence of market imperfections numerical methods for pricing financial derivatives applications of optimal stopping, singular control, and game theory to investment problems in the real economy ("real options"). dynamic adverse selection and dynamic moral hazard problems in corporate finance, in particular, use of derivative instruments to reduce the welfare loss due to agency problems. In stochastic analysis our research focuses on: infinite dimensional stochastic analysis, including stochastic differential equations on infinite dimensional manifolds stochastic partial differential equations (especially stochastic Navier-Stokes and Euler equations arising in the context of turbulence phenomena) stochastic analysis on Riemannian and Finslerian manifolds rough paths and their applications to modelling probabilistic phenomena and numerical analysis (for example non-linear filtering) Feynman path integrals and more broad applications to mathematical physics, biology and finance. We welcome PhD applications across a range of mathematical finance and stochastic analysis topics. [email protected] Related links Finance books by our academics Joint Research Group: Mathematical modelling of random multicomponent systems   Professor Zdzislaw Brzezniak Dr Alexei Daletskii Dr Christian Litterer Dr Fabio Profumo Dr Nimit Rana Dr Alet Roux Professor Jacco Thijssen Professor Tomasz Zastawniak Professor Alexander McNeil  (York Management School) Dr. Lewis Ramsden (School for Business and Society) Asma Alalyani -  [email protected] Hessa Alharbi -  [email protected]   Arnon Archankul -  [email protected] Youpeng Sun -  [email protected] Liqiong Wang -  [email protected] We run regular seminars and host talks by external speakers from the UK and overseas, covering a wide range of topics of current interest in stochastic analysis and mathematical finance. Research degrees Push the boundaries of knowledge in our supportive and stimulating environment. COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK Academic Coordinator Department of Mathematics Morningside Opening on: Sep 12 2024 Job Type: Support Staff - Union Bargaining Unit: Local 2110 Regular/Temporary: Regular Hours Per Week: 35 Salary Range: $55,805 - $56,805 Position Summary Reporting to the Director of Academic Administration & Finance (DAAF) in the Department of Mathematics , the Academic Coordinator is responsible for the smooth operation of the Department of Mathematics academic programs by providing a wide range of support to faculty, students, and University Administration. The Academic Coordinator strives to maintain the high standards of the Department's programs and to create a welcoming and rewarding place for all students. The Department of Mathematics is comprised of approximately 80 full-time and part-time faculty, 60 graduate PhD students, over 100 master’s students, and hundreds of undergraduate majors and joint majors. Offering a wide range of courses and boasting world-class faculty, the Department engages in cutting-edge theoretical research and teaching in mathematics. Responsibilities Collects and screens undergraduate Teaching Assistant (TA) applications, ensures that minimum qualifications are met, and provides nominations for new hires. Compiles student enrollment data, generates reports, and makes recommendations for TA’s assignments.    Serves as point of contact for all related inquiries and provides operational supervision to the department's 100+ graduate and undergraduate TAs. Ensures compliance with the Graduate School of Arts & Sciences and departmental policies. Handles the scheduling of Columbia and Barnard Help Rooms.  Provides periodic reviews of usage and attendance and makes sure operations are running smoothly.   Distributes, collects, and reviews undergraduate and graduate student and instructor evaluations and ensures satisfactory levels of performance. Maintains a teaching file for currently enrolled doctoral students including assignments, instructor and student evaluations, and records of teaching observations. Working closely with the DAAF, the Director of Undergraduate Studies, and the Department Chair, reviews historical course enrollments and instructor data to make recommendations for curricular planning and teaching assignments, including course capping, scheduling, and classroom assignments. Collects instructional preferences from faculty and students and assembles Curricular Planning Statements (CPS) for the academic year and Summer Session. In collaboration with the Registrar’s Office, regularly reviews the Directory of Classes and ensures accurate course information.  Maintains database with historical records of CPS submissions and updates. Edits the Department's course offerings and programmatic information before publication in the College Bulletin and ensures accurate course data in Course Management systems.  Serves as the initial point of contact concerning the Department's curriculum and course requirements for undergraduate majors and concentrators. Assists instructors with course logistics including but not limited to class rosters, waiting lists, and grade changes, and ensures proper data in Canvas and SSOL. Coordinates Departmental online course evaluation process. Responsible for the textbook inventory, prompt ordering, and ensuring accurate textbook data and syllabi information in Canvas. Maintains homework boxes and assignment/exam retention storage. Coordinates the Mathematics Prize Exam and the Putnam Exam. Assists with logistics of undergraduate events including but not limited to open houses and graduation receptions. Performs other duties as assigned. Minimum Qualifications High School diploma or equivalent and three years of related experience. Preferred Qualifications Some college preferred. Three years of experience working in an academic environment, such as student affairs or program support. Other Requirements Excellent written, verbal, and interpersonal communication skills. Ability to maintain high confidentiality and professionalism with a client focus. Strong multi-tasking, prioritization, and organization skills. Attention to detail, research, critical thinking, analytical and problem-solving skills. Self-starter, a fast learner who can work independently under limited supervision. Work well in an interactive team environment. Working knowledge of PeopleSoft or similar enterprise HRIS, Microsoft Office, and relational databases. Equal Opportunity Employer / Disability / Veteran Columbia University is committed to the hiring of qualified local residents. Commitment to Diversity  Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job. Thank you - we'll send an email shortly. Other Recently Posted Jobs Deputy Chief Operating Officer Data manager, executive director, alumni relations. Refer someone to this job ©2022 Columbia University Accessibility Administrator Log in Wait! Before you go, are you interested in a career at Columbia University? Sign up here!  Thank you, for sharing your information. A member of our team will reach out to you soon! This website uses cookies as well as similar tools and technologies to understand visitors' experiences. By continuing to use this website, you consent to Columbia University's usage of cookies and similar technologies, in accordance with the Columbia University Website Cookie Notice . 200 IMAGES (PDF) Mathematical modeling in Quantitative Finance and Computational An Introduction to the Mathematics of Fi-1-34m (PDF) Mathematical Models in Finance Mathematical Financial Economics (PDF) RMIT University Master thesis for Mathematics on Financial Mathematics and Advanced S… VIDEO Financial Mathematics Mathematical Finance previous years question paper Bsc Hons Mathematics #du #delhiuniversity #math Mathematical Economics. Find Where Revenue are maximized. By Dr. Sajid Urdu Hindi Find Q Where Profit are maximized.Profit are maximized MC= MR.3 .Price charged and profit earned Mathematical Model of LoRaWAN Channel Access Projects Introduction to Mathematical Finance COMMENTS MSc Mathematical & Computational Finance: sample dissertations MSc Mathematical & Computational Finance: sample dissertations. Below are some examples of MSc dissertations from previous years, which received high marks: Optimal Strategies from forward versus classical utilities. Robust Pricing of Derivatives on Realised Variance. Log Mean-Variance Portfolio Theory and Time Inconsistency. MSc in Mathematical and Computational Finance The Mathematical & Computational Finance Group is one of the largest mathematical finance research groups in the world and has extensive connections with the financial industry (including banks, hedge funds, central banks, and financial exchanges). ... Our MSc students have won the Natixis Prize for best MSc Thesis in Quantitative Finance in ... Master Theses Prerequisites for a master's or diploma thesis at the Research Group are certificates or passed exams in. FPSO 2014. • Stochastic Analysis (MA4405) • Continuous Time Finance (MA3702) • Master's seminar at the Research Group Finance and Actuarial Science • 2 further lectures in the area of Financial Mathematics OR • 2 further ... Project and thesis Project and thesis. The MSc project is a substantial component of the programme, occupying around 4 months. It is a piece of original work undertaken by the students under the supervision of an academic researcher and, in most cases, also with an external supervisor. Most projects are carried out in association with a bank, hedge fund ... Research in Mathematical & Computational Finance 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 ... PDF Stochastic Calculus and Applications to Mathematical Finance Mathematical Finance by GREG WHITE Mihai Stoiciu, Advisor A thesis submitted in partial ful llment of the requirements for the Degree of Bachelor of Arts with Honors ... the theory of stochastic processes, and It^o calculus. We also study an application of It^o calculus in math-ematical nance: the Black-Scholes option pricing model for the ... MSc in Mathematical and Computational Finance Statistics and Financial Data Analysis (16 lectures, and 4 classes of 1.5 hours each) Computing course. Financial computing with C++ I (16 hours of lectures, plus 4 classes of 2 hours each over weeks 1-9) The second term will be a combination of core material, offering 48 hours of lectures (18 hours of classes) and 48 hours of electives ... PhD in Mathematical Finance » Academics PhD in Mathematical Finance. ... The dissertation must be based on an original investigation that makes a substantive contribution to knowledge and demonstrates capacity for independent, scholarly research. Doctoral candidates must register as continuing students for DS 999 Dissertation, a 2-unit course, for each subsequent regular term until ... Theses Theses - Chair of Mathematical Finance. Home. Theses. Theses at the Research Group. The following pages will provide you with an overview of preconditions and possible topics of theses at our Research Group Finance and Actuarial Science: Bachelor Theses. Master Theses. Dissertations. PDF Overreaction Behavior and Optimization Techniques in Mathematical Finance MATHEMATICAL FINANCE Ahmet Duran, PhD University of Pittsburgh, 2006 Overreactions and other behavioral efiects in stock prices can best be examined by adjusting for the changes in fundamentals. We perform this by subtracting the relative price changes in the net asset value (NAV) from that of market price (MP) daily for a large set of closed-end Essays on mathematical finance Essays on mathematical finance Vichos, Georgios (2019) Essays on mathematical finance. PhD thesis, London School of Economics and Political Science. Text - Submitted Version Download (562kB) Abstract. The first part of this thesis deals with the consideration of thin incomplete financial markets, where traders with heterogeneous preferences and ... Dissertations If you are interested in a dissertation at the Chair of Mathematical Finance, please send your application to [email protected]! Current and completed Dissertations ... Gauß-Award for the paper "Closed-form solutions for Guaranteed Minimum Accumulation Benefits" resulted of the dissertation. Bannör, Karl Friedrich Incorporating parameter risk ... Applications of Stochastic Calculus to Finance This thesis will present the mathematical background for these pricing models with comprehensive proofs, show the development of the models, and test the reliability of the models with historical data. Most of this thesis is an exposition of well established methodologies used in finance DPhil (PhD) studies in Mathematical Finance @ Oxford The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world. We combine core mathematical expertise with interdisciplinary approach. We foster lively interactions between researchers coming from different backgrounds and a truly impressive ... Mathematical modeling in Quantitative Finance and Computational The second part of my PhD Thesis deals with the problem of Optimal Control in Quantitative Finance and Labour Economics. Even if the fields of application are hugely different, they share the same ... PDF The Concepts and Practice of Mathematical Finance We introduce forward-rate agreements and swaps, and their optional analogues the caplet and the swaption. We develop pricing formulas under simple assumptions. In Chapter 14, we study the pricing of exotic interest rate derivatives using the LIBOR market model. Our study includes both calibration and implementation. Brownian motion and its applications in financial mathematics 1.2 Hitting Time. The rst time the Brownian motion hits a is called as hitting time. To show that PfTa < 1g = 1 and E(Ta) = 1 for a 6= 0 Consider, X(t) Normal(0; t) Let, Ta =First time the Brownian motion process hits a. When a > 0, we will compute PfTa Tg by considering PfX(t) ag and conditioning on whether or not Ta t. Harvard Mathematics Department Harvard Department of Mathematics PhD Harvard University. Department of Mathematics. Science Center Room 325. 1 Oxford Street. Cambridge, MA 02138 USA. Tel: (617) 495-2171 Fax: (617) 495-5132. Department Main Office Contact. Digital Accessibility. Legacy Department of Mathematics Website. PDF MATHEMATICAL MODELS IN FINANCE Keywords: Mathematical Finance, stochastic calculus and modelling, options 1. INTRODUCTION Mathematics, as the language of science, has always played a relevant role ... But in addition, Bachelier's thesis marks the beginning of the theory of option pricing, now an integral part of modern finance. Thus the year Bachelor Theses Bachelor Theses. The prerequisite for a bachelor's thesis in the research group are certificates or passed examinations in at least 2 of 3 of the following courses: Financial Mathematics 1 (MA3407) Insurance Mathematics 1 (MA3405) Bachelor's seminar in the financial and actuarial research group. Please send your complete application documents ... Mathematical and Computational Finance @ Oxford The Oxford Mathematical and Computational Finance Group is one of the world's leading research groups in the area of mathematical modeling in finance.. Research Topics include stochastic processes, derivative pricing, multi-level Monte Carlo methods, computational methods for PDEs, credit risk modelling, quantitative risk management, data-driven modeling and machine learning, market ... Mathematics and Statistics Theses and Dissertations Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains, Patrick Armand Assonken Tonfack. PDF. Prevalence of Typical Images in High School Geometry Textbooks, Megan N. Cannon. PDF. On Extending Hansel's Theorem to Hypergraphs, Gregory Sutton Churchill. PDF Mathematical finance and stochastic analysis Mathematical finance and stochastic analysis. Our research interests span a broad range of topics in continuous and discrete time. In mathematical finance our areas of research activity include: applications of optimal stopping, singular control, and game theory to investment problems in the real economy ("real options"). Academic Coordinator Position Summary. Reporting to the Director of Academic Administration & Finance (DAAF) in the Department of Mathematics, the Academic Coordinator is responsible for the smooth operation of the Department of Mathematics academic programs by providing a wide range of support to faculty, students, and University Administration.The Academic Coordinator strives to maintain the high standards of ... assignment essay homework paper phd report resume review speech thesis