MFE Courses


Academic Year 2022-2023

Valentin Haddad: MFE 409: “Financial Risk Management”

  • Quantitative risk management emerged as a distinct discipline of financial economics in the 1990s, focusing mainly on trading market risk. In the early 21st century, the field expanded greatly as shareholders and regulators sought more transparency in the rapidly growing financial industry. This in turn brought about exciting developments in the modeling of credit and operational risks. The recent crisis and other colossal mishaps and catastrophic events have again highlighted the importance of risk management.  In the last five years, corporations have invested unprecedented amounts of resources in risk-management technology and its human capital. This course introduces the most modern frameworks and techniques to identify, measure and manage financial risk, both for financial and non-financial institutions.

Valentin Haddad: MFE 431: “Statistical Arbitrage”

  • This course investigates quantitative equity market-neutral strategies. At one end of the spectrum, there are high-capacity strategies with multi-year time horizons. At the other end of the spectrum, there are low-capacity strategies with milli-second time horizons. This class is squarely in the middle of these extremes.  It studies both slow and fast signals. This is the sweet spot where investors can have sufficiently high Sharpe ratio to be rewarded on their own merit rather than on their verbal ability to explain away bad performance.  Students can escape from the technologically intensive rat race (to have the fastest computer co-located closest to the Stock Exchange). Rather than giving students “a fish”, i.e., a list of alphas that are supposed to work, this class seeks to teach them how to fish.  It gives them the toolkit necessary to develop their own sources of alpha. Statistical arbitrage is less of a formula than an ongoing process: you fix the airplane as you are flying it.

Lars Lochstoer:  MFE 431: “Data Analytics and Machine Learning”

  • This is a course in data science oriented toward decision-making and predictive analytics. Topics include predictive and prescriptive models, panel regressions, text analysis, model validation and selection, models for discrete outcomes, and machine learning techniques such as Random Forest, XGBoost, Support Vector Machines, Neural Networks and Deep Learning.  Students will use Python for coding and analysis. Examples and homework will focus on finance applications, including return and earnings prediction, automated valuation models, default prediction and lending markets, portfolio choice and trading models. 

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Lars Lochstoer:  MFE 407: Empirical Methods in Finance   

  • This course provides MFE students with the quantitative tools to carry out high-quality independent, applied research in finance. We will discuss important econometric tools for analysis of time series data, such as historical data on stock returns or firm earnings, focusing on linear models for prediction and dimensionality reduction. These include ARMA models, VARs, multi-frequency volatility models, principal components analysis, factor models and Fama-MacBeth regressions. The emphasis is on applying these tools in assignments. Completing these assignments requires writing your own routines in R. In addition, the class will expose students to recent advances in the field of asset pricing. 

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Francis Longstaff: MFE 408: “Fixed Income Markets''

  • This course is an introduction to fixed income markets. The first part of the course focuses on the fundamental tools used in the markets to measure and manage interest rate risk (duration, convexity, etc.).  The second part of the course applies cutting-edge quantitative models and theory to understand the valuation and risk-return tradeoffs of fixed income swaps, contracts, and derivatives. The course has a strong emphasis on applications and quantitative techniques using many realistic examples and case studies from the markets.

Stavros Panageas: MFE xxx: Introduction to Stochastic Calculus:

  • This course covers the economic, statistical, and mathematical foundations of derivatives markets.  It teaches the basic discrete-time and continuous-time paradigms used in the analysis of financial derivatives, including an introduction to stochastic processes, stochastic differential equations, Ito's Lemma, and key elements of stochastic calculus.  It covers the economic foundations of the Black-Scholes no-arbitrage paradigm, including an introduction to Girsanov's theorem and changes of measure, equivalent martingale measures, risk-neutral valuation, fundamental partial differential equation representations of derivatives prices, market prices of risk, and Feynman-Kac representations of solutions to derivatives prices.