phd topics in mathematical finance

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Wharton’s PhD program in Finance provides students with a solid foundation in the theoretical and empirical tools of modern finance, drawing heavily on the discipline of economics.

The department prepares students for careers in research and teaching at the world’s leading academic institutions, focusing on Asset Pricing and Portfolio Management, Corporate Finance, International Finance, Financial Institutions and Macroeconomics.

Wharton’s Finance faculty, widely recognized as the finest in the world, has been at the forefront of several areas of research. For example, members of the faculty have led modern innovations in theories of portfolio choice and savings behavior, which have significantly impacted the asset pricing techniques used by researchers, practitioners, and policymakers. Another example is the contribution by faculty members to the analysis of financial institutions and markets, which is fundamental to our understanding of the trade-offs between economic systems and their implications for financial fragility and crises.

Faculty research, both empirical and theoretical, includes such areas as:

• Structure of financial markets
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Candidates with undergraduate training in economics, mathematics, engineering, statistics, and other quantitative disciplines have an ideal background for doctoral studies in this field.

Effective 2023, The Wharton Finance PhD Program is now STEM certified.

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Department of Mathematics

Financial mathematics.

A pioneer in its field, the Financial Mathematics Program offers 15 months of accelerated, integrated coursework that explores the deep-rooted relationship that exists between theoretical and applied mathematics and the ever-evolving world of finance. Their mission is to equip students with a solid foundation in mathematics, and in doing so provide them with practical knowledge that they can successfully apply to complicated financial models. Financial Mathematics students become leaders in their field; program alumni have gone forth to find success at companies like JP Morgan, UBS, and Goldman Sachs. Read more

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The field of finance covers the economics of claims on resources. Financial economists study the valuation of these claims, the markets in which they are traded, and their use by individuals, corporations, and the society at large.

At Stanford GSB, finance faculty and doctoral students study a wide spectrum of financial topics, including the pricing and valuation of assets, the behavior of financial markets, and the structure and financial decision-making of firms and financial intermediaries.

Investigation of issues arising in these areas is pursued both through the development of theoretical models and through the empirical testing of those models. The PhD Program is designed to give students a good understanding of the methods used in theoretical modeling and empirical testing.

Preparation and Qualifications

All students are required to have, or to obtain during their first year, mathematical skills at the level of one year of calculus and one course each in linear algebra and matrix theory, theory of probability, and statistical inference.

Students are expected to have familiarity with programming and data analysis using tools and software such as MATLAB, Stata, R, Python, or Julia, or to correct any deficiencies before enrolling at Stanford.

The PhD program in finance involves a great deal of very hard work, and there is keen competition for admission. For both these reasons, the faculty is selective in offering admission. Prospective applicants must have an aptitude for quantitative work and be at ease in handling formal models. A strong background in economics and college-level mathematics is desirable.

It is particularly important to realize that a PhD in finance is not a higher-level MBA, but an advanced, academically oriented degree in financial economics, with a reflective and analytical, rather than operational, viewpoint.

Faculty in Finance

Anat r. admati, juliane begenau, jonathan b. berk, greg buchak, antonio coppola, peter m. demarzo, darrell duffie, steven grenadier, benjamin hébert, arvind krishnamurthy, hanno lustig, matteo maggiori, paul pfleiderer, joshua d. rauh, claudia robles-garcia, ilya a. strebulaev, vikrant vig, jeffrey zwiebel, emeriti faculty, robert l. joss, george g.c. parker, myron s. scholes, william f. sharpe, kenneth j. singleton, james c. van horne, recent publications in finance, behavioral responses to state income taxation of high earners: evidence from california, beyond the balance sheet model of banking: implications for bank regulation and monetary policy, fee variation in private equity, recent insights by stanford business, “geoeconomics” explains how countries flex their financial muscles, car loans are a hidden driver of the ride-sharing economy, public pensions are mixing risky investments with unrealistic predictions.

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Why Study for a Mathematical Finance PhD?

I was emailed by a reader recently asking about mathematical finance PhD programs and the benefits of such a course. If you are considering gaining a PhD in mathematical finance, this article will be of interest to you.

If you are currently near the end of your undergraduate studies or are returning to study after some time in industry, you might consider starting a PhD in mathematical finance. This is an alternative to undertaking a Masters in Financial Engineering (MFE), which is another route into a quantitative role. This article will discuss exactly what you will be studying and what you are likely to get out of a PhD program. Clearly there will be differences between studying in the US, UK or elsewhere. I personally went to grad school in the UK, but I will discuss both UK and US programs.

Mathematical finance PhD programs exist because the techniques within the derivatives pricing industry are becoming more mathematical and rigourous with each passing year. In order to develop new exotic derivatives instruments, as well as price and hedge them, the financial industry has turned to academia. This has lead to the formation of mathematical finance research groups - academics who specialise in derivatives pricing models, risk analysis and quantitative trading.

Graduate school, for those unfamiliar with it, is a very different experience to undergraduate. The idea of grad school is to teach you how to effectively research a concept without any guidance and use that research as a basis for developing your own models. Grad school really consists of a transition from the "spoon fed" undergraduate lecture system to independent study and presentation of material. The taught component of grad school is smaller and the thesis component is far larger. In the US, it is not uncommon to have two years of taught courses before embarking on a thesis (and thus finding a supervisor). In the UK, a PhD program is generally 3-4 years long with either a year of taught courses, or none, and then 3 years of research.

A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. When you finish the program you will have gained a broad knowledge in most areas of mathematical finance, while specialising in one particular area for your thesis. This "broad and deep" level of knowledge is the hallmark of a good PhD program.

Mathematical Finance research groups study a wide variety of topics. Some of the more common areas include:

• Derivative Securities Pricing/Hedging: The technical term for this is "financial engineering", as "quantitative analysis" now encompasses a wide variety of financial areas. Some of the latest research topics include sophisticated models of options including stochastic volatility models, jump-diffusion models, asymptotic methods as well as investment strategies.
• Stochastic Calculus/Analysis: This is more of a theoretical area, where the basic motivation stems from the need to solve stochastic differential equations. Research groups may look at path-dependent PDEs, functional Ito calculus, measure theory and probability theory.
• Fixed Income Modeling: Research in this area centres on effectively modelling interest rates - such as multi-factor models, multi-curve term structure models as well as interest rate derivatives such as swaptions.
• Numerical Methods: Although not always strictly related to mathematical finance, there is a vast amount of university research carried out to try and develop more effective means of solving equations numerically (i.e. on the computer!). Recent developments include GPU-based Monte Carlo solvers, more efficient matrix solvers as well as Finite Differences on GPUs. These groups will almost certainly possess substantial programming expertise.
• Market Microstructure/High-Frequency Modeling: This type of research is extremely applied and highly valued by funds engaged in this activity. You will find many academics consulting, if not contracting, for specialised hedge funds. Research areas include creating limit order market models, high frequency data statistical modelling, market stability analysis and volatility analysis.
• Credit Risk: Credit risk was a huge concern in the 2007-2008 financial crisis and many research groups are engaged in determining such "counterparty risks". Credit derivatives are still a huge business and so a lot of research goes into collateralisation of securities as well as pricing of exotic credit derivatives.

These are only a fraction of the total areas that are studied within mathematical finance. The best place to find out more about research topics is to visit the websites of all the universities which have a mathematical finance research group, which is typically found within the mathematics, statistics or economics faculty.

The benefits of undertaking a PhD program are numerous:

• Employment Prospects: A PhD program sets you apart from candidates who only possess an undergraduate or Masters level ability. By successfully defending a thesis, you have shown independence in your research ability, a skill highly valued by numerate employers. Many funds (and to a lesser extent, banks) will only hire PhD level candidates for their mathematical finance positions, so in a pragmatic sense it is often a necessary "rubber stamp". In investment banks, this is not the case so much anymore, as programming ability is generally prized more. However, in funds, it is still often a requirement. Upon being hired you will likely be at "associate" level rather than "analyst" level, which is common of undergraduates. Your starting salary will reflect this too.
• Knowledge: You will spend a large amount of time becoming familiar with many aspects of mathematical finance and derivatives theory. This will give you a holistic view into the industry and a more transferable skill set than an undergraduate degree as you progress up the career ladder. In addition, you will have a great deal of time to learn how to program models effectively (without the day-to-day pressure to get something implemented any way possible!), so by the time you're employed, you will be "ahead of the game" and will know best practices. This aspect is down to you, however!
• Intellectual Prospects: You are far more likely to gain a position at a fund after completing a PhD than without one. Funds are often better environments to work in. There is usually less stress and a more relaxed "collegiate" environment. Compare this to working on a noisy trading floor, where research might be harder to carry out and be perceived as less important.

I would highly recommend a mathematical finance PhD, so long as you are extremely sure that a career in quantitative finance is for you. If you are still unsure of your potential career options, then a more general mathematics, physics or engineering PhD might be a better choice.

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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 credits, depending on whether the courses are 3 or 4 credits each). Students’ course choices must be approved by the Mathematical Finance Director prior to registration each semester. The following is a typical program of courses.

• GRS EC 701 Microeconomic Theory
• GRS MA 711 Real Analysis
• GRS MA 779 Probability Theory I
• QST FE 918 Doctoral Seminar in Finance
• GRS EC 703 Advanced Microeconomic Theory
• GRS MA 776 Partial Differential Equations
• GRS MA 781 Probability Theory 2
• QST FE 920 Advanced Capital Market Theory
• GRS EC 702 Macroeconomic Theory
• GRS MA 783 Advanced Stochastic Processes
• QST MF 850 Advanced Computational Methods
• QST MF 922 Advanced Mathematical Finance
• GRS EC 704 Advanced Microeconomic Theory
• GRS 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 semester 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-credit course, for each subsequent regular semester 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 semester to correct their status. Prior to the start of the semester, 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 seven 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 semester 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.

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• MS in Mathematical Finance & Financial Technology
• PhD in Business Administration & Management
• Graduate Certificate in Business Analytics
• Graduate Certificate in Financial Technology
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The Mathematical and Computational Finance Program at Stanford University (“MCF”) is one of the oldest and most established programs of its kind in the world. Starting out in the late 1990’s as an interdisciplinary financial mathematics research group, at a time when “quants” started having a greater impact on finance in particular, the program formally admitted masters students starting in 1999. The current MCF program was relaunched under the auspices of the Institute for Computational and Mathematical Engineering in the Stanford School of Engineering in 2014 to better align with changes in industry and to broaden into areas of financial technology in particular. We are excited to remain at the cutting edge of innovation in finance while carrying on our long tradition of excellence.

The MCF Program is designed to have smaller cohorts of exceptional students with diverse interests and viewpoints, and prepare them for impactful roles in finance. We are characterized by our cutting edge curriculum marrying traditional financial mathematics and core fundamentals, with an innovative technical spirit unique to Stanford with preparation in software engineering, data science and machine learning as well as the hands-on practical coursework which is the hallmark skill-set for leaders in present day finance.

Mathematical Finance Research Topics Ideas [MS PhD]

List of Research Topics and Ideas of Mathematical Finance for MS and Ph.D. Thesis.

• A class of mesh-free algorithms for mathematical finance, machine learning and fluid dynamics
• A Mathematical Finance Database By Marek Rutkowski and Marek Musiela
• Using a Multi-criteria Decision-making Mathematical Tech-nique for the Influential and Interaction Factors in Pension Fund
• A -functional It\^o’s formula and its applications in mathematical finance
• A class of mesh-free algorithms for finance, machine learning, and fluid dynamics
• AC^{0, 1}-functional Itô’s formula and its applications in mathematical finance
• Mathematical Modeling in Finance
• Risk-sensitive benchmarked asset management with expert forecasts
• Malliavin Calculus in Finance: Theory and Practice
• A Combination of FSAW and DOE Method with an Application to Tehran Stock Exchange
• Ranking of Banks’ Risk Reporting Using Data Envelopment Analysis
• Using Fuzzy Delphi Technique to Identify Financial Factors Affecting Risk Management in Iranian Banks
• Long-Memory Models in Mathematical Finance
• Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
• The efficiency of innovative techniques in improving new and traditional standards of corporates’ performance
• Experimental Comparison of Financial Distress Prediction Models Using Imbalanced data sets
• Designing and evaluating the profitability of linear trading system based on the technical analysis and correctional property
• Pattern Explanation of Micro and Macro variables on Return of Stock Trading Strategies
• [BOOK][B] Point Processes and Jump Diffusions: An Introduction with Finance Applications
• Bitcoin in the economics and finance literature: a survey
• The Alpha-Heston stochastic volatility model
• Counter-hegemonic finance: The gamestop short squeeze
• Evaluation the profitability of dynamic investment projects by using ordered fuzzy numbers
• Portfolio Optimization Based on Semi Variance and Another Perspective of Value at Risk Using NSGA II, MOACO, and MOABC Algorithms
• Performance Analysis of Global Hedge Funds
• Explain and Prioritize Information Disclosure Factors related to Sustainable Development Accounting with Fuzzy Approach
• Option Pricing Model with Transaction Costs and Jumps in Illiquid Markets
• Combined Optimal Stopping and Mixed Regular-Singular Control of Jump Diffusions
• The Tail Mean-Variance Model and Extended Efficient Frontier
• … for the Summer School\From L evy Processes to Semimartingales| Recent Theoretical Developments and Applications to Finance”(Aarhus, August 2002)
• The Long Memory of the Jump Intensity of the Price Process
• Smart Network Price Policy for ISP Based on Traffic Prediction
• Modeling Islamic Economics and Finance Research: A Bibliometric Analysis
• Developing a Measurement Model for the Sensitivity Analysis of Asset Returns with Regard to Beta Index of Exchange Rate in the Context of the Modified …
• The Driving Factors of China’s Housing Prices Pre-and after 2012
• Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection
• Using contingency approach to improve firms’ financial performance forecasts
• Deep learning for efficient frontier calculation in finance
• On Farkas’ Lemma and Related Propositions in BISH
• Covariate Selection for Mortgage Default Analysis Using Survival Models
• Finite-Time Stabilization of a Perturbed Chaotic Finance Model
• Wild Randomness, and the application of Hyperbolic Diffusion in Financial Modelling
• Financial Performance Evaluation of Companies Using Decision Trees Algorithm and Multi-Criteria Decision-Making Techniques with an Emphasis on …
• Ranking the efficiency and soundness of business banks using a combined method of data envelopment analysis and fuzzy vikor
• The effect of JCPOA on the network behavior analysis of tehran stock exchange indexes
• Notes on Applied Probability and Stochastic Finance
• An Investigation into the Effect of CEO’s Perceptual Biases on Investment Efficiency and Financing Constraints of the Iranian Listed Firms
• Rapport sur les contributions
• Fast Pricing of Energy Derivatives with Mean-reverting Jump-diffusion Processes
• Interest and Growth
• Geographic diversity in academic finance editorial boards—A discussion
• Topics in McKean-Vlasov equations: rank-based dynamics and Markovian projection with applications in finance and stochastic control.
• Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique
• Forward indifference valuation and hedging of basis risk under partial information
• An extremely efficient numerical method for pricing options in the Black–Scholes model with jumps
• Earnings Manipulation and Adjustment Speed towards an Optimal Leverage
• Reinforcement learning in economics and finance
• Multi-stage distributionally robust optimization with risk aversion
• Citations and the readers’ information-extracting costs of finance articles
• Development of Internet Supply Chain Finance Based on Artificial Intelligence under the Enterprise Green Business Model
• FOUR NEW FORMS OF THE TAYLOR–ITO AND TAYLOR–STRATONOVICH EXPANSIONS AND ITS APPLICATION TO THE HIGH-ORDER STRONG …
• To Study the Effect of Investor Protection on Future Stock Price Crash Risk
• TODIM method based on cumulative prospect theory for multiple attribute group decision-making under 2-tuple linguistic Pythagorean fuzzy environment
• Mathematical Modeling of Stock Price Behavior and Option Valuation
• Approximation of backward stochastic partial differential equations by a splitting-up method
• Identifying and Ranking the Factors Affecting Customer Financial Behavior using Multi-Criteria Decision Making Technic (TOPSIS)
• Finance Academy Ideological Bias Case Study
• Machine learning methods in finance
• A solution to the Monge transport problem for Brownian martingales
• Optimal portfolio of an investor in a financial market
• University of Customs and Finance
• Exact simulation of gamma-driven Ornstein–Uhlenbeck processes with finite and infinite activity jumps
• Lévy processes with respect to the Whittaker convolution
• Predictability of financial statements fraud-risk using Benford’s Law
• White noise differential equations for vector-valued white noise functionals
• Real Option Technique for an Assessment of the Itakpe Iron Ore Project
• The effect of financial distress on stock returns, through systematic risk and profitability as mediator variables
• An efficient spectral method for the numerical solution to some classes of stochastic differential equations
• Exponentially fitted block backward differentiation formulas for pricing options
• Time consistency of the mean-risk problem
• Calculated Values: Finance, Politics, and the Quantitative Age by William Deringer
• Solving high-dimensional optimal stopping problems using deep learning
• Stability analysis of stochastic fractional-order competitive neural networks with leakage delay [J]
• Simplified stochastic calculus with applications in Economics and Finance
• Continuous-Time Mean-Variance Portfolio Selection with Regime Switching Financial Market: Time-Consistent Solution
• Optimal Make-Take Fees in a Multi Market-Maker Environment
• Approximating Correlation Matrices Using Stochastic Lie Group Methods
• A new approach by two-dimensional wavelets operational matrix method for solving variable-order fractional partial integro-differential equations
• Adaptive Control and Multi-variables Projective Synchronization of Hyperchaotic Finance System
• Multiple Solutions for the Klein-Gordon-Maxwell System with Steep Potential Well
• Anisotropic non-linear time-fractional diffusion equation with a source term: Classification via Lie point symmetries, analytic solutions and numerical simulation
• A comparative study of curriculum and assessment of Law, Finance, & ICT at Luarasi university vs three UK universities
• OPTION PRICING USING ROUGH REALIZED MEASURES
• Evaluation of Students Performance using Fuzzy Set Theory in Online Learning of Islamic Finance Course.
• Postcolonial Finance: The Political History of ‘Risk-Versus-Reward’Investment in Emerging Markets
• A survey of some recent applications of optimal transport methods to econometrics
• Are Delay and Interval Effects the Same Anomaly in the Context of Intertemporal Choice in Finance?
• On statistical indistinguishability of complete and incomplete market models
• Penalty Methods for Bilateral XVA Pricing in European and American Contingent Claims by a Partial Differential Equation Model
• Model-free price bounds under dynamic option trading
• Finance 4.0-Towards a Socio-Ecological Finance System: A Participatory Framework to Promote Sustainability
• Local discontinuous Galerkin method for a nonlocal viscous conservation laws
• Hedging futures performance with denoising and noise-assisted strategies
• On a multi-asset version of the Kusuoka limit theorem of option superreplication under transaction costs
• Consistent Upper Price Bounds For Exotic Options
• L0-convex compactness and its applications to random convex optimization and random variational inequalities
• Ecological finance theory: New foundations
• On the strong Markov property for stochastic differential equations driven by G-Brownian motion
• A weak law of large numbers for the sequence of uncorrelated fuzzy random variables
• The Cold War: a very short introduction
• Time-consistent reinsurance and investment strategy combining quota-share and excess of loss for mean-variance insurers with jump-diffusion price process
• Determining the premium of paddy insurance using the extreme value theory method and the operational value at risk approach
• Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions
• Brazilian stock market bubble in the 2010s
• Deep Neural Network and Time Series Approach for Finance Systems: Predicting the Movement of the Indian Stock Market
• Markov chain approximation and measure change for time-inhomogeneous stochastic processes
• Modelling tail risk with tempered stable distributions: an overview
• The CTMC–Heston Model: Calibration and Exotic Option Pricing With SWIFT
• Valuation of Third Party Litigation Finance Contracts using a Real Option Methodology
• Anticipated backward stochastic differential equations with quadratic growth
• Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models. Risks 9: 13
• Unconditional density vs conditional density functions in estimating value-at-risk
• The Kazakh University of Economics, Finance and International Trade1 Nur-Sultan ?. Almaty Management University2 Almaty ?.
• Martingale transport with homogeneous stock movements
• A relative robust approach on expected returns with bounded CVaR for portfolio selection
• Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
• Deep ReLU neural network approximation for stochastic differential equations with jumps
• Lower bound approximation of nonlinear basket option with jump-diffusion
• Ancient Egypt: a very short introduction
• The effect of religiosity on stock market speculation
• Reframing supply chain finance in an era of reglobalization: On the value of multi-sided crowdfunding platforms
• A study of the microevolution mechanism of internet finance in China from the perspective of the labour division
• The Influence of Related Party Transaction and Corporate Governance on Firm Value: An Empirical Study in Indonesia
• Thermodynamics of gambling demons
• Level-set inequalities on fractional maximal distribution functions and applications to regularity theory
• Mathematics II: Handout
• Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO)
• Calibration of the Heston stochastic local volatility model: A finite volume scheme
• Efficient simulation of generalized SABR and stochastic local volatility models based on Markov chain approximations
• Optimal Dividend Problem: Asymptotic Analysis
• The role of digital transformation to empower supply chain finance: current research status and future research directions (Guest editorial)
• Shadow couplings
• An econometric model for intraday electricity trading
• Numeraires and martingale measures in the Black-Scholes models
• The sum of two independent polynomially-modified hyperbolic secant random variables with application in computational finance
• Economic capital and RAROC in a dynamic model
• Networks in economics and finance in Networks and beyond: A half century retrospective
• Portfolio Optimization and Diversification in China: Policy Implications for Vietnam and Other Emerging Markets
• Exact first-passage time distributions for three random diffusivity models
• Multi-utility representations of incomplete preferences induced by set-valued risk measures
• Optimal bitcoin trading with inverse futures
• ??????? ?????? ????? ??? ?????? ????? ??? ??? ??????
• Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models
• Risk assessment for financial accounting: modeling probability of default
• Public spending and green economic growth in BRI region: Mediating role of green finance
• Evaluation of strategic and financial variables of corporate sustainability and ESG policies on corporate finance performance
• Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective
• Non-capital calibration of bureau scorecards
• Asymptotic behavior of expected shortfall for portfolio loss under bivariate dependent structure
• The SIPTA Newsletter
• Risk-Averse Stochastic Programming: Time Consistency and Optimal Stopping
• Monetary risk measures for stochastic processes via Orlicz duality
• Deep Reinforcement Learning for Finance and the Efficient Market Hypothesis
• Finance for SMEs and its effect on growth and inequality: evidence from South Africa
• Machine Learning for Financial Stability
• Is there one safe-haven for various turbulences? The evidence from gold, Bitcoin and Ether
• A joint inventory–finance model for coordinating a capital-constrained supply chain with financing limitations
• Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
• ????? ???????? ?????? ??????? ??? ??????? ????? ? ??????????? ?????????
• Evaluation of the effect of credit evaluation on financial performance of commercial banks in Kisii County, Kenya
• Hazardous infectious waste collection and government aid distribution during COVID-19: A robust mathematical leader-follower model approach
• [BOOK][B] Coral reefs: a very short introduction
• Effects of a government subsidy and labor flexibility on portfolio selection and retirement
• Risk arbitrage and hedging to acceptability under transaction costs
• Mean-Variance Investment and Risk Control Strategies–A Time-Consistent Approach via A Forward Auxiliary Process
• Sample average approximation of CVaR-based hedging problem with a deep-learning solution
• Efficiency measurement of Canadian oil and gas companies
• Modified inertial subgradient extragradient method with self adaptive stepsize for solving monotone variational inequality and fixed point problems
• Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study
• A Model of Market Making and Price Impact
• Solving high-dimensional parabolic PDEs using the tensor train format
• The multivariate tail-inflated normal distribution and its application in finance
• Measuring value at risk using short-term and long-term memory of GARCH models based on switching approach to form an optimal stock portfolio
• Short Rate Dynamics: A Fed Funds and SOFR perspective
• Testing by betting: A strategy for statistical and scientific communication
• The Jump Behavior of a Foreign Exchange Market: Analysis of the Thai Baht
• Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential L\’evy models
• Climate finance governance through transnational networks
• Hedging with linear regressions and neural networks
• Consistent pricing of VIX options with the Hawkes jump-diffusion model
• Big data analytics in digital platforms: how do financial service providers customise supply chain finance?
• Fuzzy decision support modeling for internet finance soft power evaluation based on sine trigonometric Pythagorean fuzzy information
• Tutorial on risk neutral, distributionally robust and risk averse multistage stochastic programming
• The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model
• Access to finance for SMEs in post-socialist countries: the Baltic States and the South Caucasus compared
• Stochastic Volterra integral equations with jumps and the strong superconvergence of the Euler–Maruyama approximation
• Robust pricing and hedging of options on multiple assets and its numerics
• Finance-led growth hypothesis for Asia: an insight from new data
• Mathematical Model of Integration of Cyber-Physical Systems for Solving Problems of Increasing the Competitiveness of the Regions of the Russian Federation
• A fitted finite volume method for stochastic optimal control problems in finance [J]
• A fitted finite volume method for stochastic optimal control problems in finance
• Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak
• Deep Learning and Mean-Field Games: A Stochastic Optimal Control Perspective
• How does digital finance impact the leverage of Chinese households?
• An investigation of cryptocurrency data: the market that never sleeps
• The opportunities and challenges of utilizing alternative data in the assessment of creditworthiness in the Finnish consumer finance
• Export complexity and the product space: any role for finance?
• Chapter-7 Theoretical Review of Behavioural Finance and Investment Decision making
• How to re-conceptualise and re-integrate climate-related finance into society through ecological accounting?
• A general property for time aggregation
• Homogenization of random convolution energies
• Optimal Transport of Information
• Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning
• Spillover effects in empirical corporate finance
• A general approach to smooth and convex portfolio optimization using lower partial moments
• Option Pricing under Double Heston Jump-Diffusion Model with Approximative Fractional Stochastic Volatility
• Justice is an option: A democratic theory of finance for the twenty-first century
• Optimal control of the SIR model in the presence of transmission and treatment uncertainty
• Integral Sliding Mode Controller Design for the Global Chaos Synchronization of a New Finance Chaotic System with Three Balance Points and Multi-Stability
• CPT-TODIM method for bipolar fuzzy multi-attribute group decision making and its application to network security service provider selection
• A computational approach to hedging Credit Valuation Adjustment in a jump-diffusion setting
• Centre for Global Finance
• Deciphering the Global Private Financial Flows
• Resonance phenomenon for a nonlinear system with fractional derivative subject to multiplicative and additive noise
• Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network
• Regret-sensitive equity premium
• Understanding the impact of land finance on industrial structure change in China: Insights from a spatial econometric analysis
• Finance in the World of Artificial Intelligence and Digitalization
• Model-independent pricing with insider information: a Skorokhod embedding approach
• Modelling Volatile Time Series with V-Transforms and Copulas
• AM Kazybayeva, PhD, assoc. prof?ssor2 The Kazakh University of Economics, Finance and International Trade1 Nur-Sultan ?.
• Parameter behavioral finance model of investor groups based on statistical approaches
• Option Pricing under Double Heston Jump-Diffusion Model with Approximative Fractional Stochastic Volatility. Mathematics 2021, 9, 126
• Neural networks-based algorithms for stochastic control and PDEs in finance
• Holistic principle for risk aggregation and capital allocation
• The Proposition of a Mathematical Model for the Location of Electrical and Electronic Waste Collection Points
• Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain
• A Time-Inconsistent Dynkin Game: from Intra-personal to Inter-personal Equilibria
• THE QUANTUM THREAT TO CRYPTOGRAPHY
• BRINGING ISLAMIC FINANCE HOME THROUGH THE CIRCULAR ECONOMY-SOCIAL FINANCE (CESF) DISCOURSE
• Analysis of How to Meet the Challenges Brought by the Development of Internet Finance and The Era of Big Data
• Multi-Period Portfolio Optimization with Investor Views under Regime Switching
• The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation: An Applied Holistic Mathematical Model
• Regional income disparities, monopoly and finance
• Optimal uniform error estimates for moving least-squares collocation with application to option pricing under jump-diffusion processes
• MULTIDIMENSIONAL RISK AND RELIGIOSITY TOWARDS INDONESIAN MUSLIMS’SHARIA INVESTMENT DECISION
• Cash Waqf risk management and perpetuity restriction conundrum
• Stochastic volatility enhanced Lévy processes in financial asset pricing
• On the Feller-Dynkin and the Martingale Property of One-Dimensional Diffusions
• Modelling Volatile Time Series with V-Transforms and Copulas. Risks 9: 14
• Super poly-harmonic properties, Liouville theorems and classification of nonnegative solutions to equations involving higher-order fractional Laplacians
• Hamiltonicity, pancyclicity, and full cycle extendability in multipartite tournaments
• The mathematical structure of integrated information theory
• Reducing wind power curtailment by risk-based transmission expansion planning
• Optimal lockdown policy for vaccination during COVID-19 pandemic
• Cojump risks and their impacts on option pricing
• The valuation handbook: Valuation techniques from today’s top practitioners
• Sok: Decentralized finance (defi)
• Overshooting of sovereign emerging eurobond yields in the context of COVID-19
• The Positive Effects of Financial Innovation on the International Trade Volume
• The DOL-DFL Nexus: The Relationship between the Degree of Operating Leverage (DOL) and the Degree of Financial Leverage (DFL)
• Compressing over-the-counter markets
• Gender diversity and corporate risk-taking: a literature review
• Mathematical Optimization and Application of Nonlinear Programming
• Cutoff phenomenon for the maximum of a sampling of Ornstein–Uhlenbeck processes
• The implied volatility smirk in SPY options
• Why do banks retain unprofitable customers? A customer lifetime value real options approach
• Event studies on investor sentiment
• Policy Analysis of Individual Financial Planning Affected by Personal Bias Factors in Indonesia
• Mathematical Optimization Modeling and Solution Approaches
• Fast hybrid schemes for fractional Riccati equations (rough is not so tough)
• Exchange Rate Movements and Monetary Policies: Which Has Greater Influence on Petroleum
• Quantum Finance and Path Integrals
• Physics and Finance
• Composite Indicators of Company Performance: A Literature Survey
• Gas storage valuation in incomplete markets
• Active and passive portfolio management with latent factors
• The Effect of Managers’ Perception Bias Model on Earnings Management
• The Energy of Finance in Refining of Medical Surge Capacity
• ????? ????????????? ????? ????????? ????????? ?????????? ???????? ???????? ?????? ????
• Group classification for a class of non-linear models of the RAPM type
• Growing items inventory model for carbon emission under the permissible delay in payment with partially backlogging
• ISSUES OF EVALUATING THE EFFECTIVENESS OF COMMERCIAL BANKS
• Approximation of optimal transport problems with marginal moments constraints
• Multi-area transboundary pollution problems under learning by doing in Yangtze River Delta Region, China
• [BOOK][B] Introduction to Mathematical Systems Theory: Discrete Time Linear Systems, Control and Identification
• 1: FINANCE AND MARX
• The Risk Spillover Effect of China’s P2P (Peer-to-peer) Lending on Internet Finance
• THE 6th INDONESIAN FINANCE ASSOCIATION
• Manager Optimism Based on Environmental Uncertainty and Accounting Conservatism
• A review of studies on green finance of banks, research gaps and future directions
• Compound Poisson models for weighted networks with applications in finance
• Board attributes and corporate philanthropy behavior during COVID-19: A case from China
• A threshold for quantum advantage in derivative pricing
• Certifiable Risk-Based Engineering Design Optimization
• Portfolio selection in non-stationary markets
• Skew index: Descriptive analysis, predictive power, and short-term forecast
• On the Development of an Integrated Information System of Municipal Finance Management
• Application of Difference-in-Difference Strategies in Finance: The Case of Natural Disasters and Bank Responses
• Essays on Public Finance
• Preschoolers’ self-regulation and early mathematical skill differentials
• A Dynkin game on assets with incomplete information on the return
• Uncovering the invisible effect of air pollution on stock returns: A moderation and mediation analysis
• A note on the option price and ‘Mass at zero in the uncorrelated SABR model and implied volatility asymptotics’
• Analysis of the Parametric Correlation in Mathematical Modeling of In Vitro Glioblastoma Evolution Using Copulas
• La finance à l’heure des limites planétaires
• Lecture Notes for International Finance
• Addressing systemic risk using contingent convertible debt–A network analysis
• Precise asymptotics: robust stochastic volatility models
• Where to cut to delay a pandemic with minimum disruption? Mathematical analysis based on the SIS model
• Can finance be a virtuous practice? A MacIntyrean account
• Simultaneous water, salinity and nitrogen stresses on tomato (Solanum lycopersicum) root water uptake using mathematical models
• Between Scylla and Charybdis: The Bermudan Swaptions Pricing Odyssey
• [BOOK][B] Accounting Disrupted: How Digitalization Is Changing Finance
• Mathematical Foundations of Distributionally Robust Multistage Optimization
• Diversity, Inclusion, and the Dissemination of Ideas: Evidence from the Academic Finance Profession
• ?????????? ???? ? ??????-???????????. English for business informatics (b1-b2). ??????? ? ????????? ??? ?????????????? ????????????
• Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market
• The obstacle problem for a class of degenerate fully nonlinear operators
• LCOE: A Useful and Valid Indicator—Replica to James Loewen and Adam Szymanski
• A new framework for examining creditworthiness of borrowers: the mover-stayer model with covariate and macroeconomic effects
• Model Talk: Calculative Cultures in Quantitative Finance
• A simple approach to proving the existence, uniqueness, and strong and weak convergence rates for a broad class of McKean–Vlasov equations
• MICRO FINANCE AND WOMEN EMPOWERMENT-THEIR SPACE AND OPPORTUNITY FOR POVERTY REDUCTION IN NEPAL
• Mean-square stability and convergence of a split-step theta method for stochastic Volterra integral equations
• Disordered mean field games
• Existence of Equilibria in Infinite Horizon Finance Economies with Stochastic Taxation
• Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges
• An asset value evaluation for docking finance lease problems in the peer-to-peer platform
• Governmental incentives for green bonds investment
• The theory of inventive problem solving (TRIZ)-based strategic mapping of green nuclear energy investments with spherical fuzzy group decision-making approach
• Macro-finance determinants and the stock market development: evidence from Morocco
• Robust tests for ARCH in the presence of a misspecified conditional mean: A comparison of nonparametric approaches
• Implied Markov transition matrices under structural price models
• Valuation of options under a constant elasticity of variance process and stochastic volatility
• Utility Maximization When Shorting American Options
• Randomized time-varying knapsack problems via binary beetle antennae search algorithm: Emphasis on applications in portfolio insurance
• Assessing the impact of central bank digital currency on private banks
• Efficient Importance Sampling in Quasi-Monte Carlo Methods for Computational Finance
• Information support of the entrepreneurship model complex with the application of cloud technologies
• A meta-evaluation model on science and technology project review experts using IVIF-BWM and MULTIMOORA
• Has Land Finance Increased Local Financial Risks in China?
• Dynamic patterns of daily lead-lag networks in stock markets
• A Three-Term Gradient Descent Method with Subspace Techniques
• Beyond the Jurisprudential Quagmire: Perspectives on the Application of Digital Currencies and Blockchain Technology in Islamic Economics and Finance
• Pricing and hedging performance on pegged FX markets based on a regime switching model
• Correlated Log-Normal Random Variables under a Multiscale Volatility Model
• Instantaneous turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy
• Is there a pattern in how COVID-19 has affected Australia’s stock returns?
• Barrier swaption pricing problem in uncertain financial market
• Property valuation: the hedonic pricing model: the application of search-and-matching models
• Volatility, valuation ratios, and bubbles: An empirical measure of market sentiment
• Portfolio choice with sustainable spending: A model of reaching for yield
• A model of solitary waves in a nonlinear elastic circular rod: Abundant different type exact solutions and conservation laws
• Estimation of state-dependent jump activity and drift for Markovian semimartingales
• Mechanics of good trade execution in the framework of linear temporary market impact
• IRRELEVANCE OF INFLATION: THE 20 FAMA-FRENCH STOCKS
• MURAME parameter setting for creditworthiness evaluation: data-driven optimization
• Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model
• Bridging the Knowledge Gap: Understanding the Relationship of Corporate Finance and Defense Procurement
• The Quantitative Diversity Index in Multi-Objective Portfolio Model
• Efficient state preparation for quantum amplitude estimation
• Copulas and Tail Dependence in Finance
• Variable order nonlocal Choquard problem with variable exponents
• A multi objective model integrating financial and material flow in supply chain master planning
• Fractal statistical measure and portfolio model optimization under power-law distribution
• Pricing variance swaps under hybrid CEV and stochastic volatility
• The Economics of Biodiversity: the Dasgupta Review.
• Minimal Expected Time in Drawdown through Investment for an Insurance Diffusion Model. Risks 9: 17
• The application research of neural network and BP algorithm in stock price pattern classification and prediction
• An efficient algorithm for numerical solution of fractional integro-differential equations via Haar wavelet
• SOME PROBLEMS IN DETERMINING CREDITWORTHINESS INDIVIDUALS AND WAYS TO SOLVE THEM
• Antinoise in US equity markets
• Discrete-time macroeconomic system: Bifurcation analysis and synchronization using fuzzy-based activation feedback control
• Minimal Expected Time in Drawdown through Investment for anInsuranceDiffusionModel
• Optimal management of pumped hydroelectric production with state constrained optimal control
• Convergence rate analysis of proximal gradient methods with applications to composite minimization problems
• Analytic solution to the generalized delay diffusion equation with uncertain inputs in the random Lebesgue sense
• On singular control problems, the time-stretching method, and the weak-M1 topology
• A note on Gollier’s model for a collective pension scheme
• Numerical approach in the Hilbert space to solve a fuzzy Atangana-Baleanu fractional hybrid system
• The fast scalar auxiliary variable approach with unconditional energy stability for nonlocal Cahn–Hilliard equation
• From Fiat to Crypto: The Present and Future of Money
• Optimal constrained interest-rate rules under heterogeneous expectations
• Introduction to Financial Markets and Algorithmic Trading
• The Relationship between Sports Industry Development and Economic Growth in China.
• Forecast of the Impact of Human Resources on the Effectiveness of the Petrochemical Cyber-Physical Cluster of the Samara Region
• The impact of political stability and firm-specific variables on the performance of Islamic banks in Pakistan
• Pricing of Commodity and Energy Derivatives for Polynomial Processes
• G-expected utility maximization with ambiguous equicorrelation
• APPLICATION OF THE BLOCK MAXIMA METHOD IN ANALYSIS OF CRUDE BRENT OIL FUTURES, USING MATLAB 6
• An element-free Galerkin method for the obstacle problem
• Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry
• Justification of rational parameters of transshipment points from automobile conveyor to railway transport
• Health care finance, economics, and policy for nurses: A foundational guide
• Local Bank, Digital Financial Inclusion and SME Financing Constraints: Empirical Evidence from China
• Dynamic programming for optimal stopping via pseudo-regression
• Graph theoretical representations of equity indices and their centrality measures
• Financial Performance Reporting, IFRS Implementation, and Accounting Information: Evidence from Iraqi Banking Sector
• Heterodox Economic Cycles Theory during the COVID-19 economic crisis: Social volatility, affect and the finance market-real economy gap
• Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations
• A Fuzzy Analytic Hierarchy Process (FAHP) Based on SERVQUAL for Hotel Service Quality Management: Evidence from Vietnam
• Modeling 2018 Ebola virus disease outbreak with Cholesky decomposition
• The’COVID’Crash of the 2020 US Stock Market
• Factor Copula Model for Portfolio Credit Risk
• Analysing Bank Efficiency Incorporating Internal Risks: A Case of Jordan
• COVID-19, stock market and sectoral contagion in US: a time-frequency analysis
• Optimal group size in microlending
• Housing Finance and Inclusive Growth in Africa: Benchmarking, Determinants and Effects
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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 .

Discover New Knowledge in Finance

Get Your PhD in Finance

Olin’s PhD in Finance emphasizes rigorous analytical training and prepares you to pursue a career in research and teaching at leading academic institutions across the globe.

As a PhD student in Finance, you will train alongside some of the most respected and accomplished academics in the world. Students in this program have strong quantitative backgrounds and analytical abilities, typically with undergraduate training in economics, mathematics, engineering or another quantitative discipline as well as high GMAT/GRE scores.

Finance research is mostly based on economic models, which are used to address problems such as the allocation of capital, risk and rewards in the economy. Empirical work widely uses the tools of econometrics—the application of statistics to economics. Mathematical tools are extremely important in finance, helping to solve sophisticated models that reflect, as closely as possible, the important features of the market.

You have the unique opportunity to benefit from and engage in corporate collaborations with partners such as Equifax and Alibaba. These collaborations have resulted in unique access to robust datasets and have already yielded several dynamic working papers.

Our research-active faculty members are easily accessible to you. Collaboration is encouraged early in the program, with faculty/student joint research resulting in co-authored papers published in important journals.

Our finance faculty members are active and renowned researchers dedicated to advancing the understanding of financial economics. Their research interests encompass many areas of finance, both empirical and theoretical topics, including banking and financial intermediation, corporate finance, corporate control and capital structure, mergers and acquisitions, asset pricing models, investments and portfolio allocation models, and market microstructure.

Research papers by faculty members have recently been published in well-respected journals such as:

• Journal of Finance
• Journal of Financial Economics
• Review of Financial Studies
• Econometrica
• Management Science
• Journal of Financial Intermediation
• Harvard Business Review

Read about collaborative research by Finance faculty and PhD students.

As part of the program, you have access to the Wells Fargo Advisors Center for Finance and Accounting Research (WFA-CFAR). In addition to organizing a number of conferences that bring cutting-edge researchers to Olin, WFA-CFAR also funds data acquisition and student travel.

PhD Finance

Olin’s PhD program in finance emphasizes rigorous analytical training and prepares you to pursue a career in research and teaching.

Research Center Collaboration

The Wells Fargo Advisors Center for Accounting Research is dedicated to the dissemination of cutting-edge research in finance and accounting.

PhD in Finance Curriculum

Prior to the first year, we require mandatory attendance at math camp (offered through the Economics department).

Required Courses

• MEC 610 Microeconomics I (3 credits)
• MEC 611 Microeconomics II (3 credits)
• L11 511 Quantitative Methods I (3 credits)
• L11 5161 Applied Econometrics (3 credits)
• B52 620 Empirical Methods in Finance (if available first year; if not, required in the second year)
• B52 652 Introduction to Asset Pricing
• B52 655 Introduction to Corporate Finance

Prerequisites for FIN 642

• B62 538 Stochastic Foundations for Finance
• B62 539 Mathematical Finance

Olin PhD Finance courses – you will take one of the following groups of courses:

• B52 FIN 643 Info Econ & Corp Finance Theory (3 credits) – Prof. Anjan Thakor
• B52 FIN 642 Advanced Continuous Finance (1.5 credits) – Prof. Phil Dybvig (Pre requisite: B62 539 Mathematical Finance and B62 538 Stochastic Foundations for Finance)
• B52 651 Topics in Finance (1.5 credits)
• B52 654 Empirical Methods in Asset Pricing (1.5 credits) – Prof. Asaf Manela
• Compulsory attendance in all finance brown bag lunches and Friday Seminars
• First-year summer paper; papers are due by end of September after the first year.
• In the summer of the first year, students must meet with the faculty coordinator to discuss progress and complete a progress report to be submitted to the PhD Office by September 1 after the first year.
• The Micro Prelim Exam is offered in August. Students must receive a “Distinction/Honors” or “PhD pass” to continue in the PhD program. One retake of the exam is permitted.
• In August after the first year, students must attend an RA/TA orientation offered by the Center for Teaching and Learning.
• B52 FIN 615A and B Research in Finance (both semesters)
• B52 620 Empirical Methods in Finance (if not taken during the first year)
• Directed readings and/or independent studies

Olin PhD Finance courses—you will take one of the following groups of courses:

Other Electives (see below for some choices)

• B50 665 Applied Empirical Research in Accounting
• B53 620 Empirical Methods in Business
• B50 664 Doctoral Seminar in Financial Accounting
• B55 675 Empirical Methods in Structural Modeling
• B62 500R Topics in Quantitative Finance
• E35 516 Optimization in Function Space
• L11 501 Macroeconomics I
• L11 518B Seminar in Applied Econometrics II: Time Series Analysis and Macroeconomics
• B54 661 Analysis of Time Series Data
• Compulsory attendance in all finance brown bag lunches and department seminars
• Second-year summer paper; papers are due by the end of September after the second year.
• Second-year paper—Students must have paper approved by faculty to continue in the PhD program. Papers will be presented to faculty in fall of the third year.
• Field exam, given in June after the second year. Students must pass the field exam to continue in the PhD program.
• In the summer, students must meet with the faculty coordinator to discuss progress and complete a progress report to be submitted to the PhD office by September 1.
• B25 615 A and B Research in Finance (both semesters)
• B53 660 Seminar in Presentation Skills (fall semester, required)
• Improvisation Course
• Compulsory attendance in all finance brown bag lunches and Friday seminars
• Second year paper (due in September of third year) must be presented during a brown bag seminar before October 30 and must be approved by the faculty to continue in the PhD program.
• Dissertation research
• Paper presentations (brown bag seminars and conferences)
• Dissertation Proposal—Students must be able to assemble a Research Advisory Committee for the proposal of their dissertation and must submit a Title, Scope and Procedure Form as the committee’s approval of the proposed dissertation by September 30 of the fifth year.
• Paper presentations (job market paper presentations with faculty and at conferences)
• Intent to Graduate (complete form online)
• Job market and placement
• Oral defense of dissertation
• Submission of Examination Approval form, which signifies committee’s approval
• Upload of final, approved dissertation to Graduate School of Arts &Sciences
• Submission of Documented Teaching Requirements to PhD office

Download Finance PhD course descriptions

Andreas Neuhierl

Assistant Professor

• [email protected]

Full Professor

• [email protected]
• 314.935.6394

Doctoral Programs

Campus Box 1133-124-05 One Brookings Drive St. Louis, MO 63130-4899

• [email protected]
• 314-935-6340

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Math 690-82, cross-listed as.

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Mathematical Finance Program

Mathematical finance m.s. and ph.d..

Because of the strong demand, admission is highly competitive at both the MS and PhD levels in quantitative finance. The department prepares practitioners who apply mathematical and computational methods to develop and exploit financial opportunities for return enhancement and risk control. Because of the strong demand, admission is highly competitive at both the MS and PhD levels in quantitative finance. The department prepares practitioners who apply mathematical and computational methods to develop and exploit financial opportunities for return enhancement and risk control. The Texas Tech Mathematical Finance program is focused on both risk management (‘beta’ in Wall Street terminology) and ‘alpha generation’ (the Street term for trading strategies for making money). Courses are centered on projects where students use real tick data to analyze and predict the performance of individual stocks and commodities, market indices and derivatives. Texas Tech is one of a few mathematical finance programs offering both MS and PhD training. PhD students of our faculty have taken positions both in Wall Street firms and as faculty in university mathematical finance programs. For more information about our mathematical finance courses and faculty, see MF Graduate Programs and MF People.

Course Descriptions

Focuses on the pricing and use of financial derivative securities and their role in investment management and financial risk management.

This sequence covers general measure and integration theory, Lp theory, differentiation theory, and basic functional analysis.

Stock prices and foreign currency exchange rates are time series. How should we make use of these invaluable data sets to make investment decisions? This course covers applied statistical methodologies pertaining to financial time series especially series of stock price, equity returns, interest rates, and exchange rates, with an emphasis on model building and accurate prediction. The course introduces techniques involved with forecasting key variables and how to incorporate model uncertainty into financial forecasts. 

MATH 5382 – Advanced Probability I

Measure and integration, axiomatic foundations of probability theory, random variables, distributions and their characteristic functions, stable and infinitely divisible laws, limit theorems for sums of independent random variables, conditioning, Martingales.

Coverage of important topics in modern mathematical finance at the graduate level. The emphasis is on: general principles of modelling the price dynamics of financial assets; quantitative techniques; behavioral finance; market risk and other types of financial risks; volatility modeling; and the foundations of high-frequency arbitrage trading. The topics covered will enable the student to develop the theoretical knowledge and practical skills required for successfully handling multiple types of risks in modern financial markets.

The mathematical foundation for understanding modern financial theory, starting with general probability theory and leading to basic results in pricing exotic and American derivatives. The course covers: filtrations and generalized conditional expectation; the Girsanov theorem and the Radon-Nikodym process; martingales; Brownian motion; Ito integration and processes; the Black-Scholes formula; risk neutral pricing and the Feynman-Kac theorem. Applications to financial instruments are discussed throughout.

Basic introduction into the valuation of financial options via the numerical solution of partial differential equations (PDEs). The course covers the main concepts, models, methods and results that arise in the numerical approach. The focus begins with one-dimensional financial PDEs, notably the Black-Scholes equation, and continues with a detailed discussion of the important step towards two-dimensional PDEs in finance.

MATH 6355 – Numerical Methods with Applications to Financial Data

A large class of problems cannot be analyzed with analytical tools; numerical methods are especially vital in all areas of modern finance. To learn how to use computational tools in an informed and intelligent way, this course endeavors to explain not only when and how to use various numerical algorithms but also how and why they work. This course offers an introduction to numerical analysis and quantitative finance applications. The techniques presented in this course are applicable to a wide range of financial fields (options, simulation, fixed income valuation). Special attention will also be paid to financial applications of analytic and numerical optimization, covering the different types of optimization problems. 

Essential C++ topics with applications to finance. The course focuses on numerical analysis and quantitative finance applications.

Basic introduction to probability theory and stochastic processes for financial applications. The course discusses modelling financial markets with stochastic processes, including the famous Black–Scholes–Merton (BSM) model. It introduces the pertinent mathematical concepts of ‘predictability’ in application to investment portfolios and hedging strategies, and martingales and martingale measures in application to the concepts of efficiency and absence of arbitrage in financial markets. Lévy models, which improve on the performance of BSM, are introduced to take account of different stylized features of the markets. The pricing of derivative securities in market models based on Lévy processes is also covered.

The following electives will appear as special topics from semester to semester.

• Financial Software Engineering

Need Description

Introduction to stochastic programming for asset and liability management. The theory of multistage stochastic programming, focusing on problem formulation, mathematical properties and solution methods. Applications of stochastic programming models for various types of investors, including insurance companies, pension funds, individuals, and hedge funds. During the course a multistage stochastic programming model for a specific financial application will be implemented in a programming language.

Introduction to stochastic programming models for international portfolio management. Mathematical properties and solution techniques of multistage stochastic programming models presented along with financial applications such as derivative pricing and portfolio optimization. Emphasis given to model portfolios with derivative contracts to control market and currency risks.

STAT 5328 – Intermediate Mathematical Statistics I

Probability spaces, continuous and discrete distributions, functions of random variables, expectation, conditional expectation, central limit theorem, convergence concepts, order statistics, sampling distributions.

STAT 5329 – Intermediate Mathematical Statistics II

Sufficiency and completeness, information, estimation, maximum likelihood, confidence intervals, uniformly most powerful tests, likelihood ratio tests, normal based inference, Bayesian inference.

STAT 5371 – Regression Analysis

Estimation and testing in linear regression, residual analysis, influence diagnostics, multicollinearity logistic regression, nonlinear regression.

STAT 5380 – Advanced Statistical Methods I

Theory of estimation and tests of statistical hypotheses, sequential analysis.

STAT 5386 – Statistical Computation and Simulation

Basics of computing, optimization methods, EM algorithm, simulation of random variables, Monte Carlo methods, Markov Chain Monte Carlo, additional topics (time permitting).

STAT 6352 – Bayesian Methods and Application to Financial Data 

Detailed overview of the theory of Bayesian methods and their applications to financial modeling.

MF is the area of finance in which intricate mathematical models are used to predict markets, set prices, enhance returns, and manage risk.

Academic publications.

The Department of Mathematics and Statistics at Texas Tech University offers MS and PhD training in mathematical finance. MF is the area of finance in which intricate mathematical models are used to predict markets, set prices, enhance returns, and manage risk. MF professionals are known as quantitative analysts ("quants").

Email Dr. Rachev

[email protected]

Email Dr. Lindquist

[email protected]

Texas Tech University, 1108 Memorial Circle Lubbock, TX 79409-1042

(806) 742-2566

For more information, please contact Dr. Rachev or Dr. Lindquist.

What are you looking for?

Suggested search, financial mathematics.

The mathematical finance group includes probabilists and stochastic analysts working in problems directly motivated and/or applicable to finance and economics, or supervising PhD students working in those problems. From mathematical side, the members’ specialized research areas include stochastic differential equations (both forward and backward, both ordinary and partial), their related areas such as stochastic control and stochastic filtering, stochastic numerics, and statistics. From the finance/economics side, several research topics include, but are not limited to: option pricing and hedging theory; financial markets with frictions (including transaction cost, liquidity cost, credit risk, and model uncertainty); utility optimization theory with portfolio/consumption control, and contract theory.

The faculty members in the group are also responsible for teaching and advising graduate students at both Master and Ph.D. levels. The Master program of mathematical finance at USC College, a joint venture of Mathematics department and Economics department, prepares students a careers in the quantitative finance industry. Many members of the group have been responsible for teaching courses in the program, and advising Ph.D. students specializing in mathematical finance. The biweekly Mathematical Finance Colloquium brings in experts from both academia and financial industry, providing valuable contacts and opportunities for graduate students.

REGULAR FACULTY

• Lototsky, Sergey : Stochastic partial differential equations, optimal nonlinear filtering of diffusion processes, statistical inference for continuous-time processes.
• Ma, Jin : Stochastic analysis, stochastic differential equations, stochastic control theory, mathematical finance and insurance.
• Minsker, Stanislav (Stas) : Statistical learning theory, non-parametric statistics, concentration inequalities, mathematical finance.
• Zhang, Jianfeng : Stochastic analysis, backward stochastic differential equations, stochastic numerics, and mathematical finance.

EMERITUS FACULTY

• Baxendale, Peter  (emeritus): Stochastic dynamical systems; equilibrium, stability and bifurcation for solutions of stochastic differential equations; applications to stochastic neuronal models.
• Mikulevicius, Remigijus (Remi) : Stochastic differential equations, stochastic analysis.

POST DOCS and VISITORS

• Xia, Weixuan (NTT Assistant Professor):  investment decisions under incomplete preferences, exotic derivatives, cryptocurrency markets, optimal control of set-valued stochastic processes, Lévy functionals and illiquidity measures, and neural network architectures applicable in these areas.
• Tissot-Daguette , Valentin  (visiting Ph.D. student from Princeton University): American Options, Free boundary problems, Deep Stochastic Optimization, Monte Carlo methods, Exotic Derivatives

CURRENT GRADUATE STUDENTS:

• Atiqah Almuzaini (Ma)
• Thejani Gamage (Ma)
• Bixing Qiao (Zhang)
• Gaozhan Wang (Ma/Zhang)
• Mathematical Finance Colloquium
• Probability and Statistics Seminar

RECENT GRADUATES, THEIR ADVISORS, AND DISSERTATIONS

• Iseri, Melih (Zhang), Set Values for Mean Field Games and Set Valued PDEs
• Tan, Ying  (Ma), Stochastic Two-point Boundary Value Problem and Application in Kyle-Back Equilibrium Model
• Pollok, Austin (Zhang), High-Frequency Kelly Criterion and Fat-Tails: Gambling with an Edge
• Feng, Pengbin (Ma/Zhang), Dynamic Network Models for Systemic Risk
• Luo, Man (Ma), Topics on Dynamic Limit Order Book and its Related Computation
• Wu, Wenqian (Ma), Topics on Set-Valued Backward Stochastic Differential Equations
• Zhu, Zimu (Zhang), Some Topics on Continuous Time Principal-Agent Problem
• Phonsom, Chukiat (Mikulevicius), On Stochastic Integro-Differential Equations
• Ruan, Jie (Zhang), Numerical Methods for High-Dimensional Path-Dependent PDEs Driven by Stochastic Volterra Equations
• Xu, Fanhui (Mikulevicius), On the parabolic Kolmogorov integro-differential equation and its applications
• Kim, Hyun-Jung (Lototsky), Time-Homogeneous Parabolic Anderson Model
• Noh, Eunjung (Ma), Equilibrium Model of Limit Order Book and Optimal Execution Problem
• Sun, Rentao (Ma), Conditional Mean-Field Stochastic Differential Equations and Their Applications
• Wang, Jian (Lototsky), Statistical Inference For Second-Order Ordinary Differential Equation Driven by Additive Gaussian White Noise
• Sun, Rentao (Ma), Conditional Mean-Field Stochastic Differential Equations and Their Application
• Wu, Cong (Zhang), Controlled McKean-Vlasov Equations and Related Topics
• Xing, Xiaojing (Ma), Optimal Investment and Dividend under Sparre Andersen Model
• Kang, Yongjian (Lv/Zhang), Large-Scale Inference in Multiple Gaussian Graphical Models
• Karnam, Chandrasekhar (Ma/Zhang), Dynamic Approaches for some Time Inconsistent Problems
• Tsilifis, Panagiotis (Ghanem/Mikulevicius), Design, Adaptation and Variational Methods in Uncertainty Quantification
• Xie, Weisheng (Ma), Stochastic Differential Equations Driven by Fractional Brownian Motion and Poisson Point Processes
• Keller, Christian (Zhang), Pathwise Stochastic Analysis and Related Topics
• Zhang, Tian (Ma), Optimal Investment and Reinsurance Problems and Related Non-Markovian FBSDEs With Constraints
• Bessam, Diogo (Lototsky), Large Deviations Rates in a Gaussian Setting and Related Topics
• Ekren, Ibrahim (Zhang), Path-Dependent Partial Differential Equations and Related Topics
• Sokolov, Grigory (Tartakovsky/Lototsky), Multi-Population Optimal Change-Point Detection
• Zhuo, Jia (Zhang), Probabilistic Numerical Methods for Fully Nonlinear PDEs and Related Topics
• Pham, Triet (Zhang), Zero-Sum Stochastic Differential Games in Weak Formulation and Related Norms for Semi-Martingales
• Wang, Huanhuan (Ma), Asset Management with Incomplete Information
• Wang, Xin (Ma), Nonlinear Expectations for Continuous Time Model with Jumps and Applications
• Xu, Li (Lototsky), Parameter Estimate for Hyperbolic SPDE’s with Stochastic Coefficients
• Zhong, Jie (Lototsky), Second Order in Time Stochastic Evolution Equation and Wiener Chaos Approach
• Du, Jie (Zhang), Stochastic Games on Stopping Times
• Kaligotla, Sivaditya (Lototsky), Asymptotic Problems in Stochastic Partial Differential Equations: A Wiener Chaos Approach
• Lin, Ning (Lototsky), Estimation of Coefficients in Stochsatic Differential Equations
• Moers, Michael (Lototsky), Statistical Inference of Stochastic Differential Equations Driven by Gaussian Noise
• Xu, Shanshan (Lototsky/Wilcox), Initiative Non-Parametric Multivariate Regression Hypothesis Testing
• Chen, Jianfu (Ma), Regime Switch Term Structure model with Forward-Backward Stochastic Differential Equations
• Wang, Xinyang (Ma/Zhang), Dynamic Model for Limit Order Books and Optimal Liquidation Problems
• Yun, Youngyun (Ma), Analysis of Correlated Defaults and Joint Default Probability in a Contagion Model
• Liu, Wei (Lototsky), Statistical Inference for Stochastic Hyperbolic Equations
• Zhang, Changyong (Mikulevicius), Numerical Weak Approximation of Stochastic Differential Equations Driven by Levy Processes
• Knape, Mathias (Mikulevicius/Zapatero), A General Equilibrium Model for Exchange Rates and Asset Prices in an Economy Subject to Jump-Diffusion Uncertainty
• Polunchenko, Aleksey (Mikulevicius/Tartakovsky), Quickest Change Detection with Applications to Distributed Multi-Sensor Systems

THE FIRST JOB OF SOME OF OUR RECENT GRADUATES

• Melih Iseri: Postdoc at the University of Michigan
• Ying Tan: Postdoc at UCSB
• Austin Pollok: Assistant Professor of Clinical Data Science and Operations, USC
• Pengbin Feng: Research Scientist at Amazon
• Man Luo: Quant Researcher at Guotai Junan Securities Asset Management (Shanghai, China)
• Wenqian Wu: Quant Trader at Guotai Junan Securities (Shanghai, China)
• Zimu Zhu: Postdoc at UCSB
• Chukiat Phonsom: Alexandria Technology, Research and Investment
• Jie Ruan: Facebook
• Xu, Fanhui: Postdoc, Carnegie Mellon University
• Hyun-Jung Kim: Postdoc, Illinois Institute of Technology
• Eunjung Noh: Hills Assistant Professor, Rutgers University
• Rentao Sun: Data scientist, The Data Incubator
• Cong Wu: Quantitative Associate at Wells Fargo
• Xiaojing Xing: Wells Fargo (Charlotte, NC)
• Yongjian Kang: Google
• Chandrasekhar Karnam: Morgan Stanley
• Panagiotis Tsilifis: Postdoc in the Viterbi School of Engineering at USC
• Weisheng Xie: Wells Fargo (Charlotte, NC)
• Christian Keller: Postdoc at University of Michigan
• Tian Zhang: Education Management Systems
• Diogo Bessam: Postdoc at PUC-RJ/IMPA (Brasil)
• Ibrahim Ekren: Postdoc at ETH Zurich
• Grigory Sokolov: Postdoc at the SUNY Binghamton
• Jia Zhuo: Morgan Stanley
• Triet Pham: Postdoc at Rutgers
• Huanhuan Wang: Capital One
• Xin Wang: Morgan Stanley
• Li Xu: Google
• Jie Zhong: Postdoc at Ritsumeikan University (Japan)
• Jie Du: Guggenheim Partners
• Sivaditya Kaligotla: Bloomberg LP
• Ning Lin: Citigroup
• Michael Moers: Deutsche Bank
• Jianfu Chen: Union Bank of California
• Xinyang Wang: Morgan Stanley
• Youngyun Yun: Union Bank of California
• Wei Liu: American Express
• Changyong Zhang: Postdoc at Salzburg University
• Mathias Knape: Goldman Sachs
• Aleksey Polunchenko: Postdoc at USC

Financial Mathematics Graduate Programs in America

1-25 of 68 results

MIT Sloan School of Management

Cambridge, MA •

Massachusetts Institute of Technology •

Graduate School

• • Rating 4.89 out of 5   9 reviews

Current Master's student: Rigorous and engaging. Great campus with exceptionally humble and kind humans. Good locations with easy access to suburbs. ... Read 9 reviews

Massachusetts Institute of Technology ,

Graduate School ,

CAMBRIDGE, MA ,

9 Niche users give it an average review of 4.9 stars.

Featured Review: Current Master's student says Rigorous and engaging. Great campus with exceptionally humble and kind humans. Good locations with easy access to suburbs. .

Read 9 reviews.

Princeton University

Princeton, NJ •

• • Rating 4.33 out of 5   3 reviews

Master's Student: The best part of the Princeton University mechanical engineering graduate degree is the excellent faculty that teach the courses. They are incredibly knowledgeable and also very willing to help students in office hours or in sponsorship of projects. The worst part of the Princeton University mechanical engineering graduate degree is the lack of structure for the graduate research program which can leave you feeling unsure on the direction of your research. ... Read 3 reviews

PRINCETON, NJ ,

3 Niche users give it an average review of 4.3 stars.

Featured Review: Master's Student says The best part of the Princeton University mechanical engineering graduate degree is the excellent faculty that teach the courses. They are incredibly knowledgeable and also very willing to help... .

Read 3 reviews.

Owen Graduate School of Management

Nashville, TN •

Vanderbilt University •

• • Rating 4.4 out of 5   5 reviews

Master's Student: I attend my first semester in the fall of 2024. My experience so far has been amazing. I cannot wait until I start my experience. ... Read 5 reviews

Vanderbilt University ,

NASHVILLE, TN ,

5 Niche users give it an average review of 4.4 stars.

Featured Review: Master's Student says I attend my first semester in the fall of 2024. My experience so far has been amazing. I cannot wait until I start my experience. .

Read 5 reviews.

Tulane University

Graduate School •

NEW ORLEANS, LA

• • Rating 4.17 out of 5   35

University of North Texas

• • Rating 4.61 out of 5   128

Jack Welch College of Business and Technology

Sacred Heart University •

FAIRFIELD, CT

• • Rating 5 out of 5   2

Olin Business School

St. Louis, MO •

Washington University in St. Louis •

• • Rating 4 out of 5   7 reviews

Master's Student: The enrolment process was an absolute pleasure. Being able to sit in on classes and interact with faculty and students was a fantastic opportunity. It gave me a real feel for the academic dynamic and the supportive community. This firsthand experience greatly influenced my decision, and I'm eagerly looking forward to becoming a part of the institution. ... Read 7 reviews

Washington University in St. Louis ,

ST. LOUIS, MO ,

7 Niche users give it an average review of 4 stars.

Featured Review: Master's Student says The enrolment process was an absolute pleasure. Being able to sit in on classes and interact with faculty and students was a fantastic opportunity. It gave me a real feel for the academic dynamic and... .

Read 7 reviews.

Chicago Booth School of Business

Chicago, IL •

University of Chicago •

• • Rating 4.85 out of 5   13 reviews

Current Master's student: Booth is a great program where they don't teach you what to think. Rather, they teach you how to think and give you the toolkit to tackle all sorts of business problems. ... Read 13 reviews

University of Chicago ,

CHICAGO, IL ,

13 Niche users give it an average review of 4.8 stars.

Featured Review: Current Master's student says Booth is a great program where they don't teach you what to think. Rather, they teach you how to think and give you the toolkit to tackle all sorts of business problems. .

Read 13 reviews.

Marshall School of Business

Los Angeles, CA •

University of Southern California •

• • Rating 4.71 out of 5   17 reviews

Current Master's student: Marshall is a fantastic business program. So far, my academic experience has been nothing short of fantastic. I have never enjoyed school before, but almost every class I have learned new, applicable, and incredibly helpful techniques that I apply in my job every day. Most of the professors are very helpful, and passionate about their subjects. Marshall has clubs for almost every industry, so depending on your industry of interest, Marshall has clubs to prepare you for interviews and get you great exposure, and they're a great way to network as well. If your industry is not represented, you have the opportunity to create one. Marshall is still relatively underrepresented in minority categories like women and people of color, but from my understanding, they are working to improve those numbers. ... Read 17 reviews

University of Southern California ,

LOS ANGELES, CA ,

17 Niche users give it an average review of 4.7 stars.

Featured Review: Current Master's student says Marshall is a fantastic business program. So far, my academic experience has been nothing short of fantastic. I have never enjoyed school before, but almost every class I have learned new,... Marshall has clubs for almost every industry, so depending on your industry of interest, Marshall has clubs to prepare you for interviews and get you great exposure, and they're a great way to... Marshall is still relatively underrepresented in minority categories like women and people of color, but from my understanding, they are working to improve those numbers. .

Read 17 reviews.

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Whiting School of Engineering

Baltimore, MD •

Johns Hopkins University •

• • Rating 4.83 out of 5   12 reviews

Master's Student: Just starting now but the onboarding process is great! The student orientation was very helpful and the school has an amazing network. ... Read 12 reviews

Johns Hopkins University ,

BALTIMORE, MD ,

12 Niche users give it an average review of 4.8 stars.

Featured Review: Master's Student says Just starting now but the onboarding process is great! The student orientation was very helpful and the school has an amazing network. .

Read 12 reviews.

The Graduate School of Arts & Sciences - University of Virginia

Charlottesville, VA •

University of Virginia •

• • Rating 4 out of 5   1 review

Alum: Very good in some areas, excellent in other areas, many academic choices available in all areas of study ... Read 1 review

University of Virginia ,

CHARLOTTESVILLE, VA ,

1 Niche users give it an average review of 4 stars.

Featured Review: Alum says Very good in some areas, excellent in other areas, many academic choices available in all areas of study .

Read 1 reviews.

College of Sciences - Georgia Institute of Technology

Atlanta, GA •

Georgia Institute of Technology •

• • Rating 5 out of 5   1 review

Other: Georgia Tech is among the top colleges you can attend. My journey there had its highs and lows, but looking back, every experience contributed significantly to my growth. A piece of advice I'd offer to newcomers is to prioritize building friendships early on. One regret I have as I reflect on my year is not connecting with more people. I initially thought I should always work independently, but that was a misstep in my academic journey. Remember, it's essential to strike a balance between learning and having fun. ... Read 1 review

Georgia Institute of Technology ,

ATLANTA, GA ,

1 Niche users give it an average review of 5 stars.

Featured Review: Other says Georgia Tech is among the top colleges you can attend. My journey there had its highs and lows, but looking back, every experience contributed significantly to my growth. A piece of advice I'd offer... .

Haas School of Business

Berkeley, CA •

University of California - Berkeley •

• • Rating 5 out of 5   3 reviews

Master's Student: I am a new EWMBA admit at Haas and so far the experience has been fantastic! It's a close-knit community of intellectual, humble, and talented working professionals. The entire team of program directors, admissions officers, etc. are SO willing to help! ... Read 3 reviews

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University of California - Berkeley ,

BERKELEY, CA ,

3 Niche users give it an average review of 5 stars.

Featured Review: Master's Student says I am a new EWMBA admit at Haas and so far the experience has been fantastic! It's a close-knit community of intellectual, humble, and talented working professionals. The entire team of program directors, admissions officers, etc. are SO willing to help! .

Tandon School of Engineering

Brooklyn, NY •

New York University •

• • Rating 4.73 out of 5   15 reviews

Master's Student: As a bioinformatics master's student at the NYU Tandon School of Engineering, I've had the opportunity to explore the fascinating intersection of biology and computer science. The program has provided a robust curriculum, covering topics such as proteomics, transcriptomics, NGS, and data analysis, which have equipped me with the skills needed to analyze and interpret complex biological data. The faculty at Tandon are experienced and supportive, and I've had the chance to collaborate with fellow students on exciting research projects. The interdisciplinary nature of bioinformatics has allowed me to gain insights into cutting-edge technologies and methodologies that are shaping the future of the field. Overall, my experience at NYU Tandon has been enriching, and I look forward to applying the knowledge and skills acquired during my master's program to contribute meaningfully to the field of bioinformatics. ... Read 15 reviews

New York University ,

BROOKLYN, NY ,

15 Niche users give it an average review of 4.7 stars.

Featured Review: Master's Student says As a bioinformatics master's student at the NYU Tandon School of Engineering, I've had the opportunity to explore the fascinating intersection of biology and computer science. The program has... The faculty at Tandon are experienced and supportive, and I've had the chance to collaborate with fellow students on exciting research projects. The interdisciplinary nature of bioinformatics has... Overall, my experience at NYU Tandon has been enriching, and I look forward to applying the knowledge and skills acquired during my master's program to contribute meaningfully to the field of... .

Read 15 reviews.

Leonard N. Stern School of Business

New York, NY •

• • Rating 4.82 out of 5   28 reviews

Master's Student: As a part-time MBA candidate at NYU Stern, I'm deeply impressed by the program's blend of academic rigor and practical application. The faculty, industry leaders in their own right, provide invaluable insights, fostering a rich learning environment. The diversity among my peers enhances our discussions, offering varied perspectives on business challenges. Stern's location in NYC is ideal for networking and accessing career opportunities, which is crucial for a working professional like me. The flexibility of evening and weekend classes allows me to balance my professional and academic commitments effectively. Stern equips students with advanced business knowledge and fosters personal and professional growth, making it an exceptional choice for anyone aspiring to excel in the business world. ... Read 28 reviews

NEW YORK, NY ,

28 Niche users give it an average review of 4.8 stars.

Featured Review: Master's Student says As a part-time MBA candidate at NYU Stern, I'm deeply impressed by the program's blend of academic rigor and practical application. The faculty, industry leaders in their own right, provide... .

Read 28 reviews.

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Carroll School of Management

Chestnut Hill, MA •

Boston College •

• • Rating 5 out of 5   2 reviews

Master's Student: I am currently enrolled in the part time, professional evening MBA program. So far it has brought me to communicate with like minded individuals and the professors truly want to see you succeed so they will go out of their way to connect with you and help you. ... Read 2 reviews

Boston College ,

CHESTNUT HILL, MA ,

2 Niche users give it an average review of 5 stars.

Featured Review: Master's Student says I am currently enrolled in the part time, professional evening MBA program. So far it has brought me to communicate with like minded individuals and the professors truly want to see you succeed so... .

Read 2 reviews.

College of Natural Sciences

Austin, TX •

University of Texas - Austin •

University of Texas - Austin ,

AUSTIN, TX ,

Questrom School of Business

Boston, MA •

Boston University •

• • Rating 4.29 out of 5   7 reviews

Master's Student: Boston University's MBA program transformed me. Faculty, experts in their fields, nurtured my learning. A diverse student body broadened my perspective. Experiential learning honed skills and purpose. BU's strong alumni network opened doors. The sense of community is invaluable. BU has equipped me for success and instilled a passion for growth. Grateful for this transformative journey. ... Read 7 reviews

Boston University ,

BOSTON, MA ,

7 Niche users give it an average review of 4.3 stars.

Featured Review: Master's Student says Boston University's MBA program transformed me. Faculty, experts in their fields, nurtured my learning. A diverse student body broadened my perspective. Experiential learning honed skills and... .

College of Liberal Arts & Sciences - University of Illinois

Urbana, IL •

University of Illinois Urbana-Champaign •

University of Illinois Urbana-Champaign ,

URBANA, IL ,

Lally School of Management

Rensselaer Polytechnic Institute •

Rensselaer Polytechnic Institute ,

College of Arts and Sciences - University of Miami

Coral Gables, FL •

University of Miami •

• • Rating 4.67 out of 5   6 reviews

Master's Student: I am in graduate school and needed something online but also wanted something that was going to challenge me and provide me with a step further than my undergrad school provided. I compared many MPA programs and chose the University of Miami because the program was so similar to the in-person MPA program, taught by the same professors, and included the same courses. While entirely online, I have come to know my fellow graduate students and come to know the faculty in each of the courses I have taken. I'm currently half-way through the program and cannot wait to complete this degree! ... Read 6 reviews

University of Miami ,

CORAL GABLES, FL ,

6 Niche users give it an average review of 4.7 stars.

Featured Review: Master's Student says I am in graduate school and needed something online but also wanted something that was going to challenge me and provide me with a step further than my undergrad school provided. I compared many MPA... .

Read 6 reviews.

Dedman College of Humanities and Sciences

Dallas, TX •

Southern Methodist University •

Southern Methodist University ,

DALLAS, TX ,

Tulane University School of Science and Engineering

New Orleans, LA •

Tulane University •

Master's Student: I am currently in the materials science and engineering 4+1 program which will allow me to earn my MS in one year instead of two. There are some really amazing professors here despite the department being pretty small. I can think of only one professor that made the class unnecessarily difficult. There are opportunities for research and extracurriculars too. ... Read 3 reviews

Tulane University ,

NEW ORLEANS, LA ,

Featured Review: Master's Student says I am currently in the materials science and engineering 4+1 program which will allow me to earn my MS in one year instead of two. There are some really amazing professors here despite the department... .

Babson College

Babson Park, MA •

• • Rating 4.58 out of 5   12 reviews

Master's Student: The best part of my experience at Babson has been the people. I have met some of the most talented and motivated people in the world. Babson is known for its entrepreneurial focus. Babson offers a variety of hands-on learning opportunities, such as internships, consulting projects, and case competitions. Babson has a strong alumni network and they are always willing to help current students and graduates. And they also provide 1-on-1 tutoring which I found to be very insightful. However, there have also been some challenges - the program is very rigorous, and the workload can be demanding. It is also important to be self-motivated and to take advantage of the resources that Babson has to offer. The MBA program at Babson is expensive. Overall, I have had a very positive experience at Babson College. I have learned a lot, and I have grown both personally and professionally. I would highly recommend Babson to anyone interested in pursuing a career in business and entrepreneurship. ... Read 12 reviews

BABSON PARK, MA ,

12 Niche users give it an average review of 4.6 stars.

Featured Review: Master's Student says The best part of my experience at Babson has been the people. I have met some of the most talented and motivated people in the world. Babson is known for its entrepreneurial focus. Babson offers a... However, there have also been some challenges - the program is very rigorous, and the workload can be demanding. It is also important to be self-motivated and to take advantage of the resources that... Overall, I have had a very positive experience at Babson College. I have learned a lot, and I have grown both personally and professionally. I would highly recommend Babson to anyone interested in... .

College of Science and Engineering - University of Minnesota Twin Cities

Minneapolis, MN •

University of Minnesota Twin Cities •

• • Rating 4 out of 5   2 reviews

Doctoral Student: I started graduate school at UMN during the height of the COVID pandemic. Despite not being able to go to classes in person or get into the lab, the UMN community has been very inclusive and I have felt very supported and welcome. I would recommend UMN to any STEM graduate student because of this great community. ... Read 2 reviews

University of Minnesota Twin Cities ,

MINNEAPOLIS, MN ,

2 Niche users give it an average review of 4 stars.

Featured Review: Doctoral Student says I started graduate school at UMN during the height of the COVID pandemic. Despite not being able to go to classes in person or get into the lab, the UMN community has been very inclusive and I have... .

The Ohio State University College of Arts and Sciences

Columbus, OH •

The Ohio State University •

Graduate Student: Not a bad place, good reputation but the College is cutting funds every year. Cutting funds within sociales sciences and humanities has been a problem that the college face every year. ... Read 1 review

The Ohio State University ,

COLUMBUS, OH ,

Featured Review: Graduate Student says Not a bad place, good reputation but the College is cutting funds every year. Cutting funds within sociales sciences and humanities has been a problem that the college face every year. .

NC State College of Sciences

Raleigh, NC •

North Carolina State University •

North Carolina State University ,

RALEIGH, NC ,

Charles V. Schaefer, Jr. School of Engineering & Science

Hoboken, NJ •

Stevens Institute of Technology •

Stevens Institute of Technology ,

HOBOKEN, NJ ,

Bentley University McCallum Graduate School of Business

WALTHAM, MA

• • Rating 4.7 out of 5   20

Lehigh University

BETHLEHEM, PA

• • Rating 4.42 out of 5   19

College of Business - Lehigh University

Lehigh University •

Showing results 1 through 25 of 68

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Recent advances in mathematical methods for finance

• Open access
• Published: 04 April 2024

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• Giorgia Callegaro 1 ,
• Claudio Fontana 1 ,
• Martino Grasselli 1 ,
• Wolfgang J. Runggaldier 1 &
• Tiziano Vargiolu 1  

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In recent years, Mathematical Finance has witnessed the emergence of new research directions spurred by developments of financial markets, technological advances, and societal challenges. On the one hand, financial markets have seen the introduction of new financial products, regulatory frameworks, and trading infrastructures. On the other hand, artificial intelligence and machine learning techniques are introducing revolutionary changes in numerical methods in finance, overcoming computational challenges considered insurmountable until recently. In addition, new types of risks, such as climate-related and cyber-risks, have gained prominence, significantly impacting financial institutions and society at large.

This special issue on Recent Advances in Mathematical Methods for Finance provides a comprehensive overview of some of the latest developments in Mathematical Finance. We decided to launch this special issue on the occasion of the 10th General AMaMeF Conference, organised by the Guest Editors at the University of Padova and held in a virtual format on June 22–25, 2021. AMaMeF is the acronym for Advanced Mathematical Methods for Finance, and was born as a programme network of the European Science Foundation from 2005 to 2010, under the Sixth Framework Program for research and technological development of the European Union. AMaMeF now represents a European network of research promoting the exchange and diffusion of knowledge in the field of Mathematical Finance, spanning more than 20 countries. The biannual general conference stands as the flagship event of the AMaMeF network. The 10th General AMaMeF Conference spanned a broad range of topics in mathematical finance, including algorithmic trading and financial technologies, asset pricing under market frictions, collateralization and XVA, credit risk and interest rate modeling, energy and commodity markets, equilibrium and principal-agents models, climate risk, green and sustainable finance, machine learning and computational methods in finance, market microstructure, mean-field games and McKean–Vlasov equations, model uncertainty, model risk and robust finance, risk measures, stochastic control and portfolio optimization, stochastic volatility modeling, systemic risk and financial networks. These topics were specifically targeted by the call for papers for the special issue, which was open to the entire scientific community and not restricted to papers presented at the conference.

The special issue contains 44 papers, which underwent a rigorous peer review process under the supervision of the Guest Editors. Coherently with the title of the special issue, in the selection of the submitted papers emphasis was placed on the originality and interest of the mathematical methods employed, alongside the relevance of their financial applications. The selected papers encompass theoretical contributions as well as more applied research, offering a comprehensive view of promising research directions in mathematical finance.

We are thankful to Prof. Endre Boros, Editor-in-Chief of Annals of Operations Research , for giving us the opportunity to edit this special issue and to the Springer staff for their assistance throughout the production process. We are grateful to the referees for their valuable feedback and constructive criticisms, which aided in the selection of the submissions and enhanced the quality of accepted papers. Finally, our most sincere gratitude goes to the authors of the submitted papers, for contributing their work to this special issue. We hope that this collection of papers will stimulate further research on several emerging topics in Mathematical Finance.

Open access funding provided by Università degli Studi di Padova within the CRUI-CARE Agreement.

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Department of Mathematics “Tullio Levi-Civita”, University of Padova, Padua, Italy

Giorgia Callegaro, Claudio Fontana, Martino Grasselli, Wolfgang J. Runggaldier & Tiziano Vargiolu

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Callegaro, G., Fontana, C., Grasselli, M. et al. Recent advances in mathematical methods for finance. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05959-w

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Published : 04 April 2024

DOI : https://doi.org/10.1007/s10479-024-05959-w

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Complete Captcha

• 7 years - 21 posts - Latest
• Thread: 1 Goods vs 0 No Goods

Mathematical Finance PhD

Economist 1b8d

How is it different than finance PhD? Do they get better jobs?

Economist f7ae

As the name implies, it's more mathy..You use very heavy math tools to solve finance-related problems. If you want to do a PhD in this area, I suggest your programming and math skills are strong. You don't need an impeccable understanding of finance a priori, although some understanding of how the financial markets work won't hurt.

My chick friend just concluded a PhD in this area..Has interviewed with GS, MS and JPM. Currently doing a postdoc...Her area of experience is applying PIDEs (partial integro-differential equations) to nonstandard methods in asset pricing models :).

Economist 25a4

As the name implies, it's more mathy..You use very heavy math tools to solve finance-related problems. If you want to do a PhD in this area, I suggest your programming and math skills are strong. You don't need an impeccable understanding of finance a priori, although some understanding of how the financial markets work won't hurt. My chick friend just concluded a PhD in this area..Has interviewed with GS, MS and JPM. Currently doing a postdoc...Her area of experience is applying PIDEs (partial integro-differential equations) to nonstandard methods in asset pricing models :).

How much does she make? How's the income potential?

Postdoc, between $85K - $90K...Will potentially make way higher in the long run if/when she ports to the strat team of any of these banks..

Economist 3d07

How much higher?

Economist 7042

No one at banks actually uses partial integro differential equations...

Economist 10e1

a terrible idea. in practice nobody gives a sh*t about this. no great economic idea ever came out of solving a partial integro differential equation.

and I should add that the people at banks who make the money and run the show don't know, and don't care (and shouldn't care) about this. ditto for any decision maker at any other place.

Economist 077a

The people who earn Math Finance PhDs are very smart of course and as a group, are technically much stronger than Finance PhDs.

The issue is that the needs of the marketplace have shifted a lot. Quants used to mostly work in derivatives pricing and hedging. For exotics, these issues are very difficult. These days software does a lot of this very well.

The new areas of quant finance are around high frequency trading. A dual CS masters and statistics masters may be more useful for these types of roles. A physics PhD would cover everything you need to know, but is largely overkill for a finance job.

My understanding is that the exotics business in rates, credit, commodities and even FX has slowed down a lot since the Credit Crisis. Mortgage servicer hedging and structured retail products for high net worth clients are not the growth businesses they once were.

TheCommodore Rep: -2

Finance/Econ PhD already mathematical. Just an exuse to put another word on to the degree. You will probably do more coding, numerical work in the program. Pretty much what many macro & finance phds already do.

Economist e687

Mathematical finance PhD carries the stigma that you can't get into a real finance/econ program. Input quality is much lower

Economist e030

Mathematical Finance PhD = too dumb to do Math PhD.

Um no it doesn't.

I'm not sure if many finance PhDs could complete PhD courses in the math department.

Um no it doesn't. I'm not sure if many finance PhDs could complete PhD courses in the math department. Mathematical finance PhD carries the stigma that you can't get into a real finance/econ program. Input quality is much lower

@ cebfThis is correct. And it makes for a laughter when finance and econ PhD try to equate their rigour to what is obtainable in math, math-finance or stat. It does not come close.

For those who assume that math-finance PhDs are too dumb to do math PhD, I'll assume unfamiliarity with the issue at hand. Please be informed that a math-finance PhD is sometimes done in the math departments of some universities..It is sometimes classified as an area of applied mathematics, a specialization that PhD students can focus on.

And for the person who said math-finance is pretty much what an econ/finance PhD already do, again I assume some degree of unfamiliarity with the issue at hand. Finance/eco PhD do not come close to math-finance in terms of mathematical or programming rigor.

Finally, the bro who said PIDEs are not applied in banks is probably correct. It's very theoretical and is the area where my chick friend specialises..Stochastic volatility models are however very well used. I can confirm SVM models are used a lot at Morgan Stanley. Anyone eyeing strat roles at banks would not go wrong doing a thesis in Stochastic Analysis (applied to a problem in finance).

Economist 45d9

'Makes for a laughter' 'rigour' 'please be informed'

Do you even Engrish bro? How small is your peepee?

@ cebfThis is correct. And it makes for a laughter when finance and econ PhD try to equate their rigour to what is obtainable in math, math-finance or stat. It does not come close. For those who assume that math-finance PhDs are too dumb to do math PhD, I'll assume unfamiliarity with the issue at hand. Please be informed that a math-finance PhD is sometimes done in the math departments of some universities..It is sometimes classified as an area of applied mathematics, a specialization that PhD students can focus on. And for the person who said math-finance is pretty much what an econ/finance PhD already do, again I assume some degree of unfamiliarity with the issue at hand. Finance/eco PhD do not come close to math-finance in terms of mathematical or programming rigor. Finally, the bro who said PIDEs are not applied in banks is probably correct. It's very theoretical and is the area where my chick friend specialises..Stochastic volatility models are however very well used. I can confirm SVM models are used a lot at Morgan Stanley. Anyone eyeing strat roles at banks would not go wrong doing a thesis in Stochastic Analysis (applied to a problem in finance).

I assume you're open minded, that's why I want to respond to your above submission.

Now, if you checked the background of people doing/who have done math-finance, you would see that they're, more often than not, very quantitative..Physics/math/stat/computer science..Econ and finance PhD on, the other hand, are made up of students who might have quant backgrounds but not to the same extent as the students attracted by math-finance PhD programs. How then can one conclude that people who do math finance are rejects from eco and finance PhD when we all know that econ and finance PhD programs love students with quant backgrounds?

Anyway, judging by the backgrounds of math-finance PhD people, it is safe to conclude that they went into math-finance because they wanted something with a high degree of mathematical rigour and a mix of some finance. At least, this is true for the cases I've seen over the years. Econ and finance, at their peaks of mathematical rigour, do not come close. With this, it's very hard to believe math-finance PhD students are rejects of econ and finance.

'Makes for a laughter' 'rigour' 'please be informed' Do you even Engrish bro? How small is your peepee? Um no it doesn't. I'm not sure if many finance PhDs could complete PhD courses in the math department. Mathematical finance PhD carries the stigma that you can't get into a real finance/econ program. Input quality is much lower

Concentrate on the debate at hand and be civil. If you spot errors in my sentences, highlight them and if you are right, I'll take the corrections. If you have anything meaningful to say, please say it. There is no need being uncouth when no one has attacked you in an inappropriate manner.

Economist 05fc

Generally correct. For the most part, we're mathematicians who want financially inspired problems. Most of our s**t isn't very applicable in the real world though.

I assume you're open minded, that's why I want to respond to your above submission. Now, if you checked the background of people doing/who have done math-finance, you would see that they're, more often than not, very quantitative..Physics/math/stat/computer science..Econ and finance PhD on, the other hand, are made up of students who might have quant backgrounds but not to the same extent as the students attracted by math-finance PhD programs. How then can one conclude that people who do math finance are rejects from eco and finance PhD when we all know that econ and finance PhD programs love students with quant backgrounds? Anyway, judging by the backgrounds of math-finance PhD people, it is safe to conclude that they went into math-finance because they wanted something with a high degree of mathematical rigour and a mix of some finance. At least, this is true for the cases I've seen over the years. Econ and finance, at their peaks of mathematical rigour, do not come close. With this, it's very hard to believe math-finance PhD students are rejects of econ and finance.

Fair enough. I think if you want to be grounded in the real world, you should read the WSJ or FT to understand markets. Finance academia is not that relevant.

For example, I doubt many finance papers provide the details of profitable trading strategies.

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