Courses Taught

· FIN 7446: Financial Theory I

This is an introductory Ph.D.--level course in theoretical financial economics. The main purpose

is to introduce mathematical approaches to modern portfolio theory and asset pricing. These

include: the theory of choice, von Neumann – Morgenstern expected utility, Arrow-Debreu state pricing, implications of no arbitrage, multi-period exchange economies with complete and incomplete markets, stochastic discount factors, Hansen-Jagannathan bounds, the consumption-based asset pricing model, the capital asset pricing model, arbitrage pricing theory, dynamic programming, Merton’s inter-temporal CAPM, the mathematics of the efficient frontier, and option pricing (binomial, partial differential equation, and risk-neutral valuation approaches). In this course, the primary emphasis is on the practical use of mathematical tools, combined with an intuitive interpretation of assumptions and results. By the end of the semester, students are expected to be able to read effectively a high-quality research paper on asset pricing.

· FIN 6930: Investments (MBA Online & Week-end Programs)

The course introduces the concept of investing, which is to forgo consuming current wealth today and instead investing it in a variety of financial instruments in anticipation of increased wealth in the future. Key notions that will be covered include: a description of the variety of investment opportunities, such as stocks, bonds and derivatives, as well as mutual and hedge funds; the trading process and margin requirements for short sales; the variety of risk measures; the concept of trade-off between risk and expected return; portfolio selection strategies; and the multitude of ways one can conduct investment performance evaluation.

· FIN 6528: Asset Allocation

This course deals with the systematic approach to investing by considering the variety of asset classes and the unique needs of different investors such as large pension funds and sovereign wealth funds as well as hedge funds, mutual funds, and individual investors. We will contrast the risk profiles of the different asset classes, including equities, fixed income, derivatives, in which to invest as well as the risk vs. expected returns trade-offs that are driven by the objectives of the different investor categories.

· FIN 6930 - AI & ML Applications for Finance & FINTECH

This course deals with the application of data-intensive computer methods broadly known as “machine learning” to certain financial issues and also introduces fintech, a technology-driven financial environment that is reshaping finance. Topics covered include the prediction of customer credit delinquency, foreign investment risk, portfolio construction, among others. These real-world examples provide opportunities to illustrate the application of both supervised and non-supervised machine learning techniques. While machine learning (or artificial intelligence, more broadly) is inherently technical, the approach in this course will be such that anyone familiar with Excel and basic regression concepts can follow. In addition, for those interested, a gentle introduction to Python, an easy to learn and very powerful programming language widely used in machine learning, will be offered.

This course also covers decentralized finance (“DeFi”), which has become the major face of Fintech, with a particular focus on cryptocurrencies and the Blockchain paradigm.

· FIN 6425: Corporate Finance (MBA week-end program)

Course Objectives:

Introduce economic models that help firms manage financial risks, make decisions on distributions to shareholders, and determine capital structure.

Illustrate such models through applications and case analyses.

· FIN 6537: Derivative Securities

The course deals with (a) the structure and operation of derivative markets (options, forward contracts, futures, swaps and other derivatives), (b) the valuation of derivatives, (c) the hedging of derivatives, and (d) applications of derivatives in the areas of risk management, portfolio insurance, and financial engineering. The models that will be studied include the Black-Scholes model, binomial trees, and Monte-Carlo simulation. Specific topics include simple no-arbitrage pricing relations for futures/forward contracts and the put-call parity relationship; delta, gamma, and vega hedging; implied standard deviation and its statistical properties; portfolio insurance and dynamic replication strategies.

· RMI 3011: Risk Management & Insurance

This course deals with foundational notions on the nature of risk, how insurance markets operate to manage risk, how risk is priced, how to understand insurance contracts and how to assess insurer solvency. Topics covered include the various definitions and classifications of risk, principles of risk management, insurance use in individual/household and business settings, managerial aspects of underwriting and pricing, financial aspects of insurance companies and markets, public policies, and other more advanced issues.

· FIN 4414: Financial Management

This course addresses decisions facing a financial manager of a business enterprise. Major topics covered include advanced capital budgeting and capital structure (real options and Monte Carlo simulation); financing (IPO's, SEO's, warrants, and convertible bonds); payout policy (dividends and stock repurchases); risk management (when should firms hedge, and the tools for hedging) decisions; performance evaluation and compensation (EVA, MVA, and ESOs) and corporate governance. The topics that are covered will require a significant application of derivatives, and therefore I will spend about twenty percent of the class time in developing a good understanding of derivatives. Prior knowledge of derivatives is not required.

· FIN 6596: Introduction to Computational Methods for Derivatives Pricing

This course will provide practical applications of MATLAB functions and programming to fundamental financial instruments, such as bonds and stocks, and their derivatives. Though this is an introductory course, where mathematical and programming tools will be kept at a basic level, students must be familiar with undergraduate calculus and be comfortable with elementary programming.

· FIN 6489: Financial Risk Management

This course is a practical introduction to the main concepts of risk management, namely market, credit, liquidity, operational, legal and regulatory, business, strategic, and reputation risk. However, the bulk of the course will focus on financial market and credit risk. The course will make little use of mathematical formalism and will emphasize intuitive quantitative arguments. We will briefly review fundamental results of modern finance including portfolio selection theory, the capital asset pricing and the Black-Scholes option-pricing models, and the Modigliani-Miller theorem of corporate finance.

· FIN 6930: Financial Econometrics

This course is designed as a first graduate course in financial econometrics. It has two main objectives: (i) directly link fundamental financial models and statistical techniques with market data, and (ii) ensure that students understand the assumptions and limitations of their models. Applications will be illustrated on classical equilibrium and arbitrage pricing models as well as on more recent issues such as statistical arbitrage, high-frequency trading, and volatility calibration.

· FIN 4243: Debt and Money Markets

This course covers the valuation of a wide variety of fixed-income securities and derivatives including discount bonds, coupon bonds, forwards and options on fixed income securities, interest rate swaps, floating rate notes and mortgages. The course focuses on analytic tools used in bond portfolio management and interest-rate risk management. These tools include yield curve construction, duration and convexity, and formal term structure models.

· ESI 4523: Introduction to Digital Simulation Techniques

The purpose of this course is to introduce undergraduate students to digital simulation techniques in industrial applications. The emphasis is on building computer-based models for real systems and performing simulation experiments to evaluate the behavior of a system under different sets of conditions. Students are required to do a term project, as detailed in a separate handout.

· ESI 6529: Digital Simulation Techniques (Ph.D. version)

The goal of the course is to introduce fundamental concepts and techniques for stochastic simulation. Both discrete-event and Monte-Carlo approaches will be covered. Topics will include random number generation, the regenerative method, variance reduction techniques, the quasi-Monte Carlo approach, and Markov Chain Monte Carlo algorithms. Methodology will be illustrated on examples drawn from communications, transportation and manufacturing systems as well as financial engineering.

· ESI 6912: Financial Risk Management (College of Engineering)

In this course we go over a set of modeling techniques that have been used in the financial markets on contracts devised to tailor risk to specific requirements. These products are at the heart of the significant growth witnessed in the last few years under the label of financial engineering. Although most of the course (roughly 80 %) will focus on financial markets, we will spend a reasonable amount of time on the application and modification of these techniques to risk strategies in the context of engineering risk management (e.g. oil field exploration, product development, etc.) and supply chain operations. As the latter applications are only beginning to emerge, the relevant material will be drawn from recent papers while the main course content will be covered mostly from the official textbook for the course.

· EIN 4354: Elements of Engineering Economy

The objective of this course is to introduce elementary principles and concepts of engineering economy at the undergraduate level. Emphasis is on practical illustrations of the evaluation of capital investment alternatives using economic concepts of investment return and time value of money.

· EIN 6357: Advanced Engineering Economy

The main goals of this course are (i) to introduce fundamental principles and concepts of engineering economy, (ii) develop necessary skills to evaluate capital investment alternatives using economic concepts and time value of money, and (iii) acquire analytical and financial techniques for economic justification of decisions.

· ESI 6321: Applied Probability Methods

The goal of this course is to apply probabilistic and statistical techniques to actual engineering situations. The emphasis is on understanding how and when to apply certain techniques. This course should be viewed as a second elementary course in probability and statistics.

· ESI 4313: Operations Research 2

In this undergraduate-level course students introduced to operations research modeling techniques including non-linear optimization, integer and dynamic programming, and stochastic modeling. probabilistic and statistical techniques to actual engineering situations. The emphasis is on understanding how and when to apply certain techniques. The goal of this course is to apply probabilistic and statistical techniques to actual engineering situations. The emphasis is on understanding how and when to apply certain techniques. This course should be viewed as a second elementary course in probability and statistics.

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