The course examines risk management for financial institutions. These firms continually develop new products and profit opportunities. Successful financial institutions identify the attendant risks and manage them. The course analyzes interest rate, market, credit, and foreign exchange risk; capital adequacy; securitization; synthetic securities; and the financial crisis.

Topics Covered:

• Banks
• Insurance companies, pension funds, mutual funds, and hedge funds
• Financial instruments
• How traders manage risk, interest rate risk
• Value at risk
• Volatility
• Correlation, copulas
• Market risk VaR, historical simulation and model building approach
• Credit risk, estimating default probabilities
• Credit risk losses and credit VaR
• The credit crisis, stress testing, liquidity risk, model risk
• Economic capital and RAROC, career-ending mistakes

This course provides a background in building advanced financial models, including lattice models, numerical methods, and Monte Carlo simulation; programming techniques to value complex derivatives and portfolios; and analyses of financial risk problems with Excel, VBA, and higher level programming languages.

Topics Covered:

• Modeling Linear and Nonlinear Risk Factors
• Financial Times Series, Volatility Models and Risk Estimation
• Introduction to Simulation Methods
• Modeling Risk Exposures with VaR
• Multivariate Risk Models
• Introduction to Extreme Value Theory (EVT) Applications

This course provides a background in building advanced financial models. It aims to train students to identify various risks in the financial markets and to acquire the analytical and programming abilities (Excel) to analyze related risks in different financial problems.

Topics Covered:

• Financial Time Series, Volatility Models and Risk Estimation
• Modeling Risk Exposures with Value at Risk (VaR) and Expected Shortfall (ES)
• Multivariate Risk Models
• Introduction to Simulation Methods (Monte Carlo Simulation, Historical Simulation)
• Backtesting and Stress testing

The purpose of this course is to understand risk-return relationship in the stock market, the money management industry, and stock options. The first part of this course begins with modern asset pricing models for stock returns. Then, we apply and extend our knowledge about risk-return relationship in the stock market to the money management industry and study how to evaluate the performance of money managers, particularly in the hedge fund industry. The second part of this course introduces stock options. We learn binomial trees and the Black-Scholes-Merton model as the valuation method of stock options. Finally, we examine how to measure and manage a different dimension to the risk in a option position using “Greeks” and scenario analysis.

Topics Covered:

• Index Models
• The CAPM
• Multifactor Models
• Performance Evaluation
• Hedge Funds
• Properties of Stock Options
• Binomial Trees
• Wiener Processes and Ito’s Lemma
• The Black-Scholes-Merton Model
• The Greek Letters

The goal of this course is to provide you with an understanding of the common types of fixed income securities and their valuation, the major risks associated with investing in fixed income securities, the standard measures of those risks and approaches to managing those risks. In addition, you will be introduced to the basics of modeling interest rate processes and valuing securities with embedded options.

Topics Covered:

• Terminology associated with fixed income securities
• Taxonomy of Risks
• The Federal Reserve and Fixed Income Markets
• Introduction to Repo Market and Treasury Auctions
• Measuring Bond Price Volatility
• Term Structure of Forward Rates
• Modeling Uncertain Future Discount Factors
• Introduction to Fixed Income Derivatives
• Valuing Complex Fixed Income Securities with Lattices
• Forward & Futures Contracts
• Introduction to Mortgage Pass-Through Securities (MBS)
• Structured Securities

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 the application of advanced estimation and forecasting techniques including multivariate and time series models (ARIMA) and maximum likelihood estimation to risk management, and advanced VAR topics, including computing and implementing VAR management systems, extensions and limitations of VAR (IVAR, DVAR), and stress testing.

Strategies and Risk Management in Alternative Investments: This course is the first part of a two-part seminar series on applications in risk management. The course will introduce students to the current practice in the application of various risk management tools and techniques to real life situations. The material in this class will be delivered through several sections, and include papers and articles written by both academics and practitioners.

Topics Covered:

• Introduction to Financial Regulation
• Recent US Financial Regulatory Reform
• Theory for Risk-Based Regulation of Financial Intermediaries
• The Treatment of Market Risk
• Addressing Credit Risk and its Capital Requirements
• Operational Risk
• Basel Accord Pillars I, II and III
• Towards Basel III
• National Implementation
• Comparison/Contrast between Sell-Side & Buy-Side Risk Management
• Buy-Side Firms
• Intro to Credit & Counterparty Risk Management Practices
• Margin, Collateral, and Leverage
• Prime Brokerage Risk
• Introduction to Hedge Fund Risks
• Strategies in Hedge Funds
• Cyber Risk

The purpose of this course is to study modern credit risk methodology. There are three major steps in modeling credit risk: (1) estimating the probability of default (by a statistical logit regression, a structural approach, or transition matrices), (2) estimating loss given default, and (3) estimating default correlations. After mastering three steps, we learn how to measure credit portfolio risk with the asset value approach by CreditMetrics. A Monte Carlo simulation is used to obtain the portfolio loss distribution. Finally, we examine modeling aspects in CDSs and CDOs.

Topics Covered:

• Basel II and Internal Ratings
• Estimating Credit Scores with Logit
• Structural Approach to Default
• Prediction and Valuation
• Transition Matrices; Prediction of Default and Transition Rates
• Prediction of Loss Given Default Modeling and Estimating Default
• Correlation with the Asset Value Approach
• Measuring Credit Portfolio Risk with the Asset Value Approach
• Credit Default Swaps and Risk-Neutral Default Probabilities
• Risk Analysis and pricing of Structured Credit: CDOs

Strategies and Risk Management in Alternative Investments: Continuation from 2nd Semester. The objective of the second seminar course is to examine the financial regulatory environment and explore advanced issues and strategies in financial risk management.

Topics Covered:

• Introduction to Financial Regulation
• Dodd-Frank Wall Street Reform and Consumer Protection Act
• Risk Management Toolkit
• Compliance Regimes and Strategies
• Predicting Risk
• Credit Risk Management in Action
• Framing the Risk Management Process
• Mitigating Credit Risk Exposure
• Corporate Governance and Accountability
• Ethics in Finance

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 review of the Financial Risk Management program. Reviews the accounting requirements associated with asset valuation and income recognition of complex portfolios that utilize advanced hedging techniques. Analyzes an organization’s control environment and processes within COSO and SOX frameworks and examines the control practices that organizations use to help ensure the integrity of information provided by its accounting systems. Tax-related issues and Basel II are also discussed.

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.