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Financial Engineering

MS in Financial Engineering

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The hallmark of the Master of Science in Financial Engineering (MSFE) is the close integration of mathematics and finance with the most recent computational developments. Our MSFE was the first to be established in California, and has long offered a strong curriculum in the foundational skills that will make you successful in industry. Beyond these skills, we embrace the fundamental changes that machine learning is bringing to the modern world.

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The MSFE program provides you with the expertise to create and evaluate complex financial products to help you become a strategic leader in the field. You will draw on tools from applied math, statistics, and financial and economic theory which you will convert into (i) software programs in the most modern programming languages and (ii) successful decision-making. Our program ranks among the top financial engineering programs nationally and gives our graduates excellent preparation for careers in portfolio management and risk management in various financial services institutions (banking, hedge funds, and financial technology companies).

The Claremont Colleges are proud to offer small class sizes. The MSFE program embodies this principle. We believe that students learn better with personalized teaching.

 

Program Highlights

Program At a Glance

UNITS
32 units

ESTIMATED COMPLETION TIME*
1–year

*Actual completion times will vary and may be higher, depending on full- or part-time course registration, units transferred, and time to complete other degree requirements.

COURSES BEGIN
Fall

DEGREE AWARDED
MS in Financial Engineering

Featured Courses

MATH 358
Mathematical Finance: Fixed Income and Derivatives

Pricing of equities, fixed income, credit, commodities, and currency derivatives, using stochastic calculus and numerical methods.

MATH 364
Machine Learning for Asset Pricing and Management

Basic optimization, machine learning, asset pricing and portfolio theory. Topics include mean-variance, mean-variance with learning, strategic asset allocation, linear factor models, linear programming, PCA, clustering, and deep learning.

MATH 458A
Quantitative Risk Management

Value-at-Risk, expected shortfall, coherent measures of risk, risk aggregation, and capital allocation. Special focus is on credit risk, but different topics will vary according to current market needs.

MATH 453
Financial Time Series

Stationary and non-stationary processes, seasonal models, univariate ARIMA and GARCH models. Some multivariate models along with state-space models and Kalman filter will also be discussed.

MGT 402
Asset Management Practicum

We will study how asset management firms establish and review investment policy, conduct investment research, determine strategies to be implemented, select securities, enter and track orders, measure and report performance, and manage client relations.

MATH 454
Statistical Learning

This course teaches statisticians and financial engineering practitioners cutting-edge statistical learning techniques to deal with vast and complex data in fields ranging from biology and finance to marketing and astrophysics.

Curriculum

The Financial Engineering Program provides a flexible curriculum. Thirty-two units are needed to complete the degree. Twenty-four units are required courses. Eight units are elective courses. Recommended electives are offered every year during the semester listed. Other electives are not offered during a fixed semester; they are listed at the end of this curriculum section.
 

Fall Semester

Required Courses

Corporate Finance
This course develops understanding of financial decision making—including investment decisions, financing decisions, and their interaction—and examines the underlying framework of corporate finance, including valuation, market efficiency, portfolio theory, agency costs, and information costs.
Applied Probability and Statistics
The goal of this course is to study the fundamental concepts in the theory of statistical inference. Students will learn how to construct point and interval estimators and test statistical hypotheses based on data. Students will also learn the concepts of sufficiency and completeness as they relate to forming optimal point estimators, and how to apply the Neyman-Pearson theory to find optimal hypothesis tests.
Stochastic Processes
This first part of the course covers Markov processes: Markov chains and pure jump processes. Ergodic theory and martingales are analyzed. The second part of the course covers second order processes such as Brownian motion. Stochastic integration and stochastic calculus (Ito’s lemma) are covered. Applications are given to queueing systems and default risk modeling.

Recommended Elective Courses

Statistical Learning
This course teaches statisticians and financial engineering practitioners cutting-edge statistical learning techniques to deal with vast and complex data in fields ranging from biology and finance to marketing and astrophysics.

Spring Semester

Required Courses

Mathematical Finance: Fixed Income and Derivatives
This course starts by describing financial derivatives in the following markets: equities, fixed income, credit, commodities, currency. Emphasis is then on the pricing of these securities by formulae or numerical techniques (Monte Carlo, trees, finite differences). We analyze the following models: Black-Scholes, stochastic volatility, Vasicek, SOFR and others. Stochastic calculus is used extensively. Real options are also discussed.
Financial Time Series
This course studies standard methods in handling time series data. These include stationary and non-stationary processes, seasonal models, univariate ARIMA and GARCH models. Some multivariate models along with state-space models and Kalman filter will also be discussed.
Quantitative Risk Management
This course focuses on developing tools to quantify and manage the different sources of risk in financial markets. In particular, we will develop tools to forecast volatility, calculate different measures of risk such as Value-at-Risk and Expected Shortfall by Monte Carlo (with or without copulae) or other methods, such as quadratic VaR. We will also study coherent measures of risk, risk aggregation, and capital allocation. Special attention will be given to credit risk: pricing risky bonds, counterparty risk, credit default swaps, securitization, and consumer credit risk. Topics will vary according to current market needs and may include ESG, climate, and cryptocurrency derivatives.

Recommended Elective Courses

Machine Learning for Asset Pricing and Management
This course covers basic optimization, machine learning, asset pricing and portfolio theory. Various types of investments such as stocks and bonds are described at the MBA level. Topics include mean-variance, mean-variance with learning (Black-Literman), strategic asset allocation, linear factor models, linear programming, PCA, clustering, and deep learning for asset pricing and management.
Asset Management Practicum
Theories of asset management are presented via textbook and other readings, lectures, case studies, and student and guest speaker presentations. Students will be responsible for inviting some of the guest speakers with consultation by the instructor. Asset management firms establish and review investment policy, conduct investment research, determine strategies to be implemented, select securities, enter and track orders, measure and report performance, and manage client relations. We will study all these activities in the course.

Other Electives

The following courses may be offered in different semesters each year. Depending on their availability, they can be taken as electives:

  • Quantum Computing and Applications
  • Partial Differential Equations
  • Math Clinic (two-semester sequence)
  • Linear Statistical Models
  • Discrete Mathematical Modeling
  • Optimization
  • Mathematics of Machine Learning
  • Advanced Big Data Analysis
  • Computational Statistics
  • Financial Strategy & Valuation (Drucker)
  • Selected Topics in Finance: Fixed Income (Drucker)
  • Investments (Claremont McKenna College)

Faculty & Research

Where You Can Find Our Alumni

Financial Engineering exchange program - view of Lausanne bridge and cathedral in background

Exchange Program

The Financial Engineering program offers its students the exclusive opportunity to participate in an exchange program at the University of Lausanne, located in the beautiful country of Switzerland.

Known for its banking system and situated at the heart of Europe, Switzerland offers our students the opportunity to participate in financial forums and engage in stimulating financial discussions, both with world-renowned faculty and seasoned professionals.

Classes offered in the exchange program are comparable to those offered at CGU. These courses include Asset Pricing, Econometrics, International Finance, Probability, Stochastic Processes, Applied Corporate Finance, and Derivatives.

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