Curriculum

• You will interact with a series of online modules to help you transition to CMU and understand our policies and expectations around ethics and academic integrity.
• Academic readiness materials to allow you to prepare for the MSCF Prep courses in August.
• Internship and resume materials to help you prepare for internship recruitment.
• Summer events to get to know MSCF staff and your fellow class mates.

• MSCF Math Prep
• MSCF Probability Prep
• MSCF Programming Prep
• MSCF Markets Prep

• 46901 Fundamentals of Programming and CS
• 46902 Data Structures and Algorithms
• 46906 MSCF Business Communication I 
• 46907 MSCF Business Communication II 
• 46909 MSCF Practitioner Course
• 46921 Financial Data Science I 
• 46923 Financial Data Science II
• 46926 Machine Learning I 
• 46927 Machine Learning II 
• 46929 Financial Time Series Analysis 
• 46932 Simulation Methods for Option Pricing
• 46944 Stochastic Calculus for Finance I 
• 46945 Stochastic Calculus for Finance II 
• 46956 Fixed Income
• 46964 Algorithm Design and Applications for Computational Finance
• 46971 Presentations for Financial Computation
• 46971 Financial Products and Markets
• 46972 MSCF Investments
• 46973 MSCF Options

• 46912 Cryptocurrency and Blockchain: Theory to Practice
• 46915 Advanced Derivative Models
• 46924 Natural Language Processing
• 46937 MSCF Deep Learning
• 46954 Risk Management
• 46975 Macroeconomics for Computational Finance
• 46976 Financial Optimization
• 46977 MSCF Studies in Financial Engineering
• 46979 Asset Management
• 46982 Market Microstructure and Algorithmic Trading
• 46983 MSCF Machine Learning Capstone Project 

A foundational course in programming and Computer Science for Computational Finance students. Students will apply Python's core data structures to computational problems, learning to select and combine them effectively for a given task. The course emphasizes fluency in reading code, algorithmic problem solving, top-down design, and producing clear and efficient code. Problems are drawn from mathematical and applied contexts to build the computational thinking skills required for subsequent coursework in the MSCF program. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor. and you must satisfy the prerequisites.

A course in foundational data structures and algorithms for Computational Finance students. Students will study the design, implementation, and analysis of core data structures including stacks, queues, priority queues, trees, and graphs. The course also covers key algorithmic techniques including graph traversal, shortest-path algorithms, and dynamic programming. Students apply these concepts through scaffolded programming projects set in financial contexts. Emphasis is placed on evaluating trade-offs between competing approaches and translating abstract algorithmic ideas into correct, efficient Python implementations. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college (Dietrich, Tepper, SCS, or Mellon), and you must satisfy the prerequisites.

A project-based course in which students apply advanced algorithmic techniques to computational problems in finance. The specific algorithmic content is chosen to support the semester's projects and may draw from areas such as graph algorithms, linear programming, network flow, streaming algorithms, and randomized algorithms. The course emphasizes the full arc of applied problem solving: formalizing a potentially under-specified problem, selecting and adapting appropriate algorithms, building a working system, and evaluating its performance.

Financial professionals are overwhelmed with messages. The messages that break through the clutter are well-crafted and persuasive, using techniques that are proven to grab attention, create interest, and lead to action. Financial professionals who master these skills maintain productive business relationships and produce desired business outcomes. To that end, Business Communication 2 focuses on written communication, as well as negotiation, emotional intelligence, team communication, and leadership communication. Students practice writing and negotiation, as well as team activities. The class involves many interactions, including role play, team exercises, one-to-one interactions, and peer review. Other MSCF courses also connect to this course through selected writing/speaking assignments.

The MSCF Practitioner Lecture Series will provide first-year MSCF students with an opportunity to interact and learn from industry practitioners who are employed in a variety of roles within the quantitative finance industry. The course comprises two key modules. The first module (MSCF Speaker Series) provides students with access and insights on various topics and themes in the quantitative finance field from industry-leading practitioners. The second module (Corporate Presentations) features both alumni and external industry professionals and recruiters who will showcase their organization as well as supply information about the various roles that exist and insights into the summer internship process. Students will leave the corporate presentations knowledgeable about the organization’s culture and specifics pertinent to the available roles.

This course will serve as a cross-disciplinary, comprehensive overview of the hottest topics in blockchains and cryptocurrencies.

Specifically, we will cover the following topics:

1. how distributed consensus forms the backbone of blockchains, allowing multiple nodes to jointly maintain a public ledger,
2. the architectural design of major blockchains such as Bitcoin and Ethereum,
3. cryptography employed by blockchains, such as digital signatures, zero-knowledge proofs, and Merkle hash trees,
4. smart contracts and DeFi applications, such as automated market makers, lending pools, and atomic swaps,
5. incentive attacks in blockchains and cryptoeconomics,
6. mechanism design for blockchains,
7. practitioners' point of view,
8. Some of the major challenges faced by blockchains and cryptocurrencies today

Even though some lectures may involve light elementary math, mathematical proofs are NOT required for the homework and exam.

We will have 2-3 homework assignments, 1 light smart-contract programming lab, and 1 final exam.

Building on the theory developed in Math 46945: Stochastic Calculus II, this course treats advanced models and methods for derivative pricing. Topics covered include local and stochastic volatility models, exotic options, and volatility derivatives. Emphasis is placed on interpretation, computational techniques, model calibration, and numerical solutions. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

The first in a two-course sequence covering methods of extracting useful information from raw financial data. Focus is placed on fundamental tools of statistical inference useful for fitting models to financial data, including those with complex multivariate forms. Topics include linear models, nonparametric models, parameter estimation, and uncertainty quantification. Methods are taught using the Python programming language. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

The second in a two-course sequence covering methods of extracting useful information from raw financial data. Focus is placed on tools of data exploration and mining, motivated by the handling of modern financial data sets. Topics include data cleaning, data visualization, dimension reduction, clustering, Bayesian methods, and making discovery claims. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

This course introduces students to natural language processing (NLP) and text mining methods relevant to the study of quantitative finance. Topics include extracting natural language features, topic models, word embeddings, and large language models. The focus is on understanding the foundations of the methods as well as introducing toolkits for implementation. Prerequisites: 46921, 46923, 46926, and 46927. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

The first in a two-part sequence covering statistical machine learning aimed at quantitative finance. This first course covers tools and approaches for prediction, including both regression and classification. The focus is on understanding the foundations of the methods so that they can be both applied and modified. Topics include foundations of supervised learning, the biasvariance tradeoff, model validation and assessment, classification and classification metrics, regularized and nonparametric regression, generalized additive models, and tree-based methods. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

The second in a two-course sequence covering statistical machine learning aimed at quantitative finance. The course further covers methods for regression and classification, along with other advanced topics in statistics and machine learning. Topics will be drawn from boosting and ensemble methods, clustering, mixture models and topic modeling, natural language processing, Markov decision processes and reinforcement learning, and neural networks/deep learning.

This course introduces time series methodology to the MSCF students. Emphasis will be placed on basic time series models (AR, MA, ARMA, and ARIMA) and their use in financial applications, including forecasting and the development of quantitative trading strategies. Topics studied in this course include univariate forecasting, seasonality, model identification, and diagnostics. In addition, GARCH and stochastic volatility modeling will be covered, as will state space models and Kalman filtering. Multivariate time series and cointegration will be introduced along with their application to trading strategies.

This course initially presents standard topics in simulation, including random variable generation, statistical analysis of simulation output, and variance reduction methods, including antithetic variables, control variables, importance sampling, conditional Monte Carlo, stratification, and martingale control variables. The course then addresses the use of Monte Carlo simulation in solving applied problems on derivative pricing, as discussed in the finance literature. Application areas include the estimation of the "Greeks," pricing of American options, pricing interest rate dependent claims, and credit risk.

This course introduces students to the foundations underlying deep learning that are relevant to the study of quantitative finance. While deep learning is a rapidly evolving field, this course focuses on the fundamental concepts that are pertinent to contemporary architectures and techniques. Topics include the basics of building deep neural networks; flexibility in modeling alternative types of data with convolutional neural networks; building recurrent neural networks (and related architectures) for autoregressive settings; as well as generative adversarial networks and autoencoders. There will be emphasis on the practical usage of techniques in quantitative finance with guided demos for implementation. Prerequisites: 46921, 46923, 46926, and 46927. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor, and you must satisfy the prerequisite.

The focus of this course is to understand the basics of arbitrage-free pricing and the mathematical tools used to price securities. We will begin with a rapid introduction to the multiperiod Binomial model, and then mainly focus on continuous time markets. The mathematical tools we will use include conditional expectation, martingales, Brownian motion, Itô integrals and Itô's formula, exponential martingales, the Girsanov theorem, and risk-neutral measures. The course will cover the Black-Scholes option pricing model in detail and may touch upon the fundamental theorems of asset pricing. Prerequisite: MSCF Math and Markets Prep, MSCF Probability Prep. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

This course uses stochastic calculus to develop models for equity and fixed income derivatives. Change of measure is critical in such models, and the course begins with a detailed discussion of that, including the role and limitations of risk-neutral pricing. The use of forward measures in fixed income models and the change of measure associated with a change of currency will be developed. There is also a discussion of the effect of funding and collateral on the Black-Scholes pricing formula. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission

This course covers quantitative methods in risk management, both on the buy-side and the sellside. Since the 2007-2008 financial crisis, risk management has taken on a large role in financial firms because of the need to satisfy regulators (sell-side) and for internal decision making (sellside and buy-side). Efficient risk capital allocation can have an effect on revenue that is an order of magnitude greater than the effect of trading desk operations. The quantitative skills needed to perform this function are at least as great as those required anywhere in the finance industry. The course begins with an overview of risk and common measures of risk. It provides methods for calculating Value-at-Risk (VaR) in the context of market risk. This is followed by a discussion of buy-side risk management. Next, credit risk is covered in depth, including structural and reduced-form models for default, credit and debit valuation adjustments (CVA and DVA), and portfolio credit risk. Swap trades will be used as a case study. Further valuation adjustments involving funding and margining considerations are covered. The course ends with a discussion of the FRTB (Federal Review of the Trading Book) and Basel 3 risk capital rules. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

This course introduces the most important securities traded in fixed income markets and the valuation models used to price them. Payoff characteristics and quotation conventions will be explained for treasury bills and bonds, STRIPS, callable bonds, mortgage-backed securities, and derivative securities like swaps, caps, floors, swaptions, and options on bonds. Basic concepts will be explained, such as the relation between yields and forward rates, duration, convexity, and factor models of yield curve dynamics. Key concepts for interest rate derivative valuation will be introduced using discrete time versions of the Ho-Lee and Black-Derman-Toy models. Text: Bruce Tuckman, Angel Serrat. "Fixed Income Securities," 3rd ed., (University Edition) ISBN# 0-470-90403-8 (paperback contains exercises) 0-470-89169-6 (hardcover does not contain exercises but will be posted on course site). Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

You are never more visible in a financial organization than when you present. Clients and top management make conscious and unconscious decisions about your capabilities based upon your ability to communicate your ideas persuasively and succinctly. This course provides intensive training and practice in planning, preparing, and delivering the financial arguments your audiences will demand. Assignments focus on nonverbal, vocal, and verbal training; rhetorical strategies that drive problem-solving deliverables; and visualization and framing strategies for financial data-rich content.

MSCF Investments gives students a foundation for quantitative portfolio management and for understanding market price determination. Key concepts include risk measurement, risk-reward trade-offs, equilibrium asset pricing, portfolio optimization, benchmarking, price discovery, market efficiency, and pricing anomalies. Specific portfolio management tools include meanvariance optimization, CAPM and APT asset pricing, general equilibrium asset pricing theories, and cash-flow/discount-rate decompositions, factor models (e.g., Fama-French), momentum strategies, and performance evaluation. The course presents essential theories and formulas and reviews important institutional and empirical facts about equity, bond, and commodity markets. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an

MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

The goal of the Options course is to develop tools to price, hedge, and understand the risk exposures of any contingent claim on any underlying variable. In addition, important empirical facts about option pricing are also reviewed. The types of options considered include exchangetraded calls and puts, OTC exotic options, interest rate options, volatility derivatives, corporate securities such as callable bonds and warrants, and "real options" like power plants and mines. The option pricing techniques to be studied include Black-Scholes, local vol and stochastic volatility models, Hull and White interest rates, binomial option pricing, and the option pricing super-theory known as Risk Neutral Valuation. Some specific topics are Geometric Brownian Motion and the mathematics of continuous-time stochastic processes; put-call parity and other arbitrage-free price option restrictions; Greeks; Monte Carlo Simulation; implied standard deviations and their statistical properties; heteroscedasticity, exotic options; interest rate and commodity options, static and dynamic option replication trading strategies; and implied stochastic processes. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college (Dietrich, Heinz, Tepper, or Mellon). PhD students with relevant research may be eligible with the instructor's permission.

This course is concerned with the structure and functioning of financial markets and products, with a focus on areas that are not covered, or only partially covered, elsewhere in the MSCF curriculum. The course will be organized into modules, with each module primarily taught by an industry expert in the topic area of that module. Emphasis will be placed on issues of practical importance in an industry context.

This class is designed to give students an understanding of the functioning of the US economy and how it relates to the global economy. This knowledge will enable students to grasp the enormous differences in economic environments faced by businesses around the world. The course will utilize analysis of economic environments through the lenses of open-economy macroeconomics. This is the field of economics concerned with how national performance and economic policies are affected by the presence of trade and international capital flows. The course will use this approach to study issues such as economic indicators and forecasting (where are we headed?); economic growth (why are some countries more productive than others?); business cycles (why do we have booms and recessions?); fiscal and monetary policy (how does policy affect labor and credit markets?); international trade, capital flows and foreign exchange rates. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

Optimization techniques play an increasingly important role in a range of financial and data science problems. Many computational finance problems, ranging from asset allocation to risk management, from option pricing to model calibration, can be efficiently solved using modern optimization techniques. Similarly, many models and tasks in data analysis, including a variety of regression models, maximum likelihood estimation, clustering, and deep learning models, can be formulated and tackled as optimization models. This course covers several classes of optimization models (linear, quadratic, stochastic, and dynamic optimization) encountered in financial and data science contexts. For each model class, after a survey of the main theory and solution methods, we will discuss some applications in mathematical finance that are amenable to that problem class. The course will also cover first-order methods, which are currently the most popular and effective class of algorithms to solve large-scale optimization problems. NonMSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

This is a course about using Financial Engineering to solve practical risk management and trading problems, and about the sales process for selling derivative deals. The focus is on designing and pricing derivative securities to trade on and hedge customized risk exposures, particularly those involving non-linear, path-dependent, and/or multi-variable exposures to interest rates, equity prices, credit events, and commodity prices, pitching these exotic securities to clients, and managing any associated risks. The valuation frameworks used to price these derivatives are Risk-Neutral Valuation and Monte Carlo Simulation. Specific models include local volatility and stochastic volatility equity option pricing models, Hull-White style interest rate models, HJM term structure model, and statistical credit risk models. The course also highlights practical issues about model calibration, model risk, and static and dynamic hedging. The highlight of the course is a series of in-class team case presentations. While pricing and hedging techniques are important, so too are practical issues such as deciding which risks to share contractually and knowing how to pitch a derivative deal. The in-class presentations are a chance to practice standing in front of a client or boss and sell/explaining complicated financial products. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

This course presents a modern treatment of the buy side of quantitative finance. The course follows a modern factor perspective: assets earn high returns (risk premiums) to compensate owners for the losses they incur during bad times, i.e., because of their exposure to underlying factor risks. The main components of the course develop and build on this factor perspective. The course starts with a high-level discussion of asset management, both from an academic and from a practical perspective. This will include a description of the main building blocks: factors, portfolio construction, and players in the asset management ecosystem. The subsequent components of the course develop each of these components in more detail. We will discuss general factor models and specific risk factors for different asset classes that have been documented in academic research. We will describe how to use statistical methods to validate the existence of premiums associated with these factors; how to construct portfolios based on underlying factor models; and how to back-test these portfolios. We will also discuss practical aspects related to transaction costs, liquidity, taxes, cash management, and delegated investing. The course will conclude with a component on artificial intelligence and machine learning developments in asset management, a subject that is a focus of a great deal of activity and that continues to develop at a rapid pace. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

Trading is central to the investment process. This course presents foundational concepts and current issues relating to trading in financial markets, including algorithmic and high-frequency strategies, optimal order execution, execution quality analysis, the dynamics of limit order markets, the regulatory and institutional landscape, programming and IT infrastructure, and the economics of market microstructure. Important empirical methodologies and concepts, such as price decomposition using vector autoregression, VWAP benchmarking, information asymmetry metrics such as PIN and VPIN, and the price impact of order flow imbalance (OFI), will be introduced. The course will consider trading in equity, options, futures, and crypto markets. In hands-on course assignments, you will utilize the industry-standard Kdb+ and Python languages along with actual intraday quote, trade, and order book data to perform analysis in Jupyter notebooks. NonMSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.

The capstone builds on the concepts and skills taught in the five-course data science curriculum during the first year of the program. As a second-year MSCF student, you will work with fellow students on data mining, modeling, and visualization techniques, along with statistical and machine learning methods to address a real-world challenge at a financial firm. Throughout the 14-week initiative, you will be supported by an MSCF faculty member and guided by company mentors to help achieve the project goals. The capstone concludes in December with a final report and presentation to faculty and client representatives. Non-MSCF students may not take this course.

First-hand Industry Experience

The MSCF Summer Internship is a requirement of the degree.MSCF Summer Internships provides students the opportunity to put the skills they have learned in the MSCF program into practical application in the financial services industry.

Market Prep introduces students to the institutional structure of modern financial markets and the roles played by major financial firms. The course examines how asset managers, banks, trading firms, and market infrastructure interact, and how incentives, regulation, capital constraints, and technology shape market behavior. Students develop a working understanding of the financial ecosystem in which quantitative models, pricing frameworks, trading strategies, and investment decisions are applied.

The course uses major market events and episodes of market stress to develop economic reasoning, professional judgment, and clear communication about markets. Students study how risk, liquidity, valuation, market structure, and arbitrage-based pricing relationships interact in practice, and how these dynamics inform quantitative analysis, firm behavior, and regulatory outcomes. By grounding quantitative learning in an understanding of real markets and institutions, Market Prep prepares students to engage more effectively with the MSCF core curriculum and applied coursework.

Thisis in part a refresher course in calculus, covering some of the calculations that arise in subsequent MSCF courses, but it also covers related mathematics topics and helps students practice mathematical thinking and problem-solving skills. The course is setup as a problem-working seminar. Problems will be assigned and these and similar problems will be worked in class. The following is an example of the material you may see:

• Learn to accurately compute the limits, derivatives and integrals that arise in subsequent courses in the Master's program in Computational Finance. Problems will be chosen to emphasize the calculations that have caused students difficulties in these courses in the past.
• Practice formulating problems mathematically and solving brain teasers similar to those commonly asked in job interviews.
• The course will not present theory except as it is needed to do the assigned problems.

The objective of this course is to introduce the basic ideas and methods of calculus-based probability theory and to provide a solid foundation for other MSCF courses based on probability theory. Topics include basic results on probability and conditional probability, random variables and their distributions, expected values, moment generating functions, multivariate distributions, transformations of random variables and vectors, laws of large numbers and the central limit theorem.

A technical introduction to the fundamentals of programming including 1D and 2D lists, and top-down design. This course assumes prior knowledge of the content covered in MSCF Python Basics, including data, expressions, variables, functions, conditionals, loops, strings, and style.

The goal of this course is to refresh and expand your knowledge of several important topics of the Master’s Program, such as object-oriented programming with C++, theory of pricing and hedging of derivative securities, numerical analysis, and stochastic calculus. The course is organized around a project of design and implementation of a powerful C++ library for the pricing of derivative securities. You will learn important principles of the implementation of financial models and master algorithms of the evaluation of different types of derivative securities: European, American, standard, barrier, and path-dependent options on stocks and interest rates. Non-MSCF students may not take this course without the instructor's written permission. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible for permission from the instructor.

In Financial Computing III, we implement in C++ the key numerical routines used in the financial industry. The topics include spline interpolation, least-squares fitting, finite differences, Fast Fourier Transform, and adjoint differentiation. As usual, examples and homework exercises will be drawn from finance-related sources.