The course covers the activities of different types of financial institutions and the standard methods in which financial institutions identify their risks and manage them. We analyze various dimensions of financial risk such as market risk, credit risk, volatility risk, and model risk. Furthermore, the course introduces banking regulations such as capital requirement, Basel I, II, III, and discusses the 2008 financial crisis.

The mathematical foundation for modeling financial risk as well as key concepts in algebra, statistics, calculus, time series and econometrics principles with applications to modeling risk management as a dynamic process over time.

This course aims to provide the participants a solid background to identify various risks in the financial markets using advanced financial models and to acquire the analytical and programming abilities to analyze related risks in different financial settings. The course covers the following topics: (i) financial time series, volatility models, and risk estimation; (ii) modeling risk exposures with value at risk and expected shortfall; (iii) multivariate risk models; (iv) introduction to simulation methods (Monte Carlo simulation, historical simulation); (v) backtesting and stress testing.

Strategies for security selection and asset allocation and evidence on returns and volatility, equity price behavior and asset pricing models, portfolio performance evaluation methods, valuation models, equity risk measurement and optimal allocation of funds. Students will learn pricing of equity derivatives and their applications in risk management of equity-linked products and strategies.

Bond fundamentals and risk, models of term structure, the use of interest rate derivative in hedging interest rate risk, the use of mortgage-backed and other asset-backed securities (MBS, CMBS), and other debt instruments (CDOs, CLOs etc.) to manage credit and cash flow risks, in addition to valuation and trading strategies of pooled assets and derivative bonds using Monte Carlo and option pricing techniques.

In a world in which addressing financial risks is becoming ever more complicated, what do you need to know from the standpoint of law and ethics to advance and protect your company’s interests?  Risk management is a key driver for business in the modern developed economy.  How do legal and ethical considerations affect decision-making in these areas?  For example, what are the consequences of “too big to fail” and how does the Dodd-Frank Act and stress-testing seek to address those issues?  How do the SEC’s and CFTC’s regulatory activities work?  How do anti-money-laundering laws and regulations and internationally directed U.S. anti-bribery and anti-corruption laws like the FCPA work?  What are specific financial crimes and U.S. government enforcement?  How should corporate governance be structured to best accomplish risk management?  How does antitrust law impact (a) joint action by competitors to set industry standards or seek action with respect to risk management generally and (b) financial market trading practices specifically?  How do foreign laws affect U.S.-based companies in these areas?  For the legally astute person, assessing how key fields of law affecting one’s company will likely be evolving is always highly important, and especially so in areas like financial risk management that are so central to political as well as business concerns.  How might evolution of the law play out in these areas, and how could one anticipate — and properly, as a matter of both law and ethics -- influence those developments?

This course covers topics from quantitative finance and include probability theory, Monte Carlo simulation, and partial differential equations. On the application side, special attention is paid to exotic derivatives, the impact of skew and correlation on pricing and risk analysis, and derivatives from energy, equity, and rates that involve multiple underlying tradable assets. Throughout the semester, these topics are considered in the context of enterprise risk management systems, and they motivate discussions of advanced VAR and stress testing techniques.

First in a two-part series on applications in Enterprise Risk Management. Introduces students to the current practice in the application of various enterprise risk management tools and techniques to real life situations. Material will be delivered through several sections, and include papers and articles written by both academics and practitioners.

Pricing, measurement, and management of credit risk; credit risk modeling; use of credit derivatives to manage and control credit risk; building and managing portfolios, including long/short, and market neutral strategies; measurement of credit risk, including Actuarial, Merton, and Copula function; and portfolio construction, performance evaluation, asset allocation, and portfolio risk management (VAR, Hedging, Portfolio insurance).

Second in a two-part series; a continuation of Applications in Enterprise Risk Management I. Examines the financial regulatory environment and explores advanced issues and strategies in financial risk management. Analyzes an enterprise’s governance, control environment and processes within COSO and SOX frameworks.

1.50 credits. Students must complete work on projects sponsored by businesses and other organizations. Projects vary from applied research outputs to tangible products such as software.

1.5 credit course.  A real-world approach to Enterprise Risk Management (ERM) in identifying, managing, and mitigating risk. A review of enterprise risks will include strategic, operational (e.g., supply chain), technological, and regulatory risk. The steps necessary to achieve an effective ERM process through a methodology for identifying, prioritizing, quantifying, decision-making and messaging of enterprise risks. This will include industry tools, reports and case studies providing a practical guide for implementing ERM. There will also be a discussion of the value of ERM to the enterprise and its various stakeholders.

The MSFRM Program is designed to provide a “Real World Approach to Risk Management Theory.” The most important way the Program delivers on this is via its Experiential Learning Requirement, which is a Milestone that must be met by each student. Though there is a wide range of options that can be chosen by the student, they all deliver the real world experience that this requirement demands.

For more information, visit Experiential Learning.

The course trains students with advanced knowledge of financial risk management to build risk measurement and management tools by using Excel VBA. It assumes prior knowledge of the VBA language. It provides an advanced learning forum for students to develop specific applications on their own. These deliverable risk management applications are graded.

This course will introduce the students to a wide variety of algorithms that are used in machine learning applications.   Students will code a few algorithms completely and learn to use software packages that implement others.  Instruction and assignments will use the R and Python programming ecosystems.  Students will be exposed to Machine Learning at scale using the Keras and TensorFlow libraries.  Throughout the course, special attention will be given to applications of these algorithms to finance.