AFN 521 Corporate Finance
This course covers essential topics of corporate financial policy. After a brief review of the time-value of money, the course analyzes the three main decisions that corporations take: 1) the financing and capital structure decision 2) the capital budgeting decision and the associated decision-making rules (e.g., NPV, IRR, real options, etc.) 3) payout policies and ways of implementing them via dividend payments and share repurchases. Depending on time availability, the course may also discuss topics in international finance, corporate governance and financial distress.
AFN 522 Investments
This course covers the basic principles of investment analysis and valuation, with emphasis on security analysis and portfolio management in a risk-return framework. Security analysis focuses on whether a security is correctly valued in the market. Portfolio management deals with efficiently combining securities into a portfolio tailored to the investor’s preferences and monitoring/evaluating the portfolio. The course covers both the theory and practical aspects of investments.
AFN 523 Empirical Methods in Finance I
This is an introductory course on empirical methods as applied in finance. The course starts with an introduction to regression analysis and time series, covering such topics as ARMA models, stationarity, vector auto-regressions, unit roots and cointegration. The course then teaches students how to study return distributions and test asset return predictability and market efficiency. Finally, the course covers methods used in event studies. The course also provides an overview of key financial databases and uses the Python/R programming languages for instruction and assignments.
AFN 524 Empirical Methods in Finance II
This course builds on and extends the material and scope of the first course in empirical methods in finance. It covers in more detail empirical tests of asset pricing models (such as the CAPM and multifactor models). It also covers term structure models and associated applications (e.g., fitting terms structure models to the data). Finally, it covers the key empirical methods used in assessing market quality including measurements of price discovery and liquidity (such as effective and realized spreads). The course utilizes key financial databases and uses the Python/R programming languages for instruction and assignments.
AFN 525 Options and Futures
This course studies the pricing and use of derivatives such as options and futures contracts. The no-arbitrage principle and its use in pricing futures contracts and option restrictions is first developed, followed by the binomial-tree approach and the Black-Scholes model. Various extensions and applications are provided, including (1) pricing options on stock indices, currencies and futures; (2) risk management; (3) pricing options embedded in corporate securities (e.g., equity, callable bonds, warrants and convertibles; (4) fixed-income (interest-rate) derivatives.
AFN 526/626 Financial and Integrated Reporting
This course covers a range of topics essential to understanding and applying financial and integrated reporting in today’s business environment. Students will begin with an introduction to financial accounting, learning how to interpret core financial statements such as the balance sheet, income statement, and cash flow statement, along with key accounting principles. The course then explores financial statement analysis, focusing on tools like ratio analysis to assess a company’s performance and financial health. A significant component of the course is the integrated reporting framework, which combines financial data with non-financial insights to present a holistic view of organizational value creation. Ethical considerations in financial reporting are examined, emphasizing the importance of transparency, integrity, and accountability. The course also introduces Environmental, Social, and Governance (ESG) factors and their growing importance in financial decision-making and reporting. Students will gain familiarity with leading sustainability reporting standards, including GRI, SASB, TCFD, and ISSB, and learn how to apply them in practice. Finally, the course highlights the role of corporate governance and effective stakeholder communication in supporting credible and meaningful integrated reports.
AFN 528 Blockchain and Decentralized Finance
This course aims at exposing students to the technology of blockchain and its financial applications. The course reflects the rapid integration of this technology into the financial system, including on-going discussions by government entities for issuing digital currencies. By the end of the course, students are expected to be able to elaborate on whether blockchain fundamentally changes the financial system, or not, and understand different perspectives on it.
AFN 530 Central Banking Policy
This course covers the key elements of central banking theory and practice. This includes the main objectives of most central banks, namely monetary and financial stability. The course describes how central banks try to achieve these objectives using their balance sheet and their ability to “print” money. As such, the course describes conventional and unconventional monetary policies, their implementation and the lender-of-last-resort function of central banks. The course also discusses in some detail the great financial crisis of 2008-09, the regulatory response to the crisis in the years after and the role of central banks in the new regulatory environment that has emerged since then.
AFN 533 Bank Financial Management
This course covers the methods and tools used by banks to measure and hedge their various risks including interest rate, credit, and currency risks. The course also studies the measurement and evaluation of bank performance, basic instruments and techniques, asset/liability management, new financial strategies, and integrated decisions for bank management.
AFN 534 Financial Risk Management
The aim of this course is to illustrate the use of financial theory and applied statistics in measuring and managing risks that multinational corporations and financial institutions are currently facing. It will discuss: Basel I & II, volatility and valueat- risk, coherent risk measures; simulation of Profit & Loss distributions using Gaussian assumption for equity portfolios and bonds, market risk capital adequacy, linear and non-linear risks; time-varying volatility of market-risk factors, EWMA and GARCH process; extreme financial risks with non-Gaussian distributions, extreme value models; credit risk and rating systems; probability of default, recovery rates, credit risk capital adequacy; methods of Credit Metrics (JP Morgan), distance to default – KMV (Moody’s), actuarial approach (Credit Suisse First Boston); types of operational risk, measurements using Loss Distribution Approach, capital adequacy; mitigating and managing financial risks, capital for unexpected losses, risk transfer/hedging.
AFN 536 Business Valuation
This course aims to teach students how to value a business both from a conceptual and a practical perspective. As such, the course first discusses the importance that managers should place on creating long-term value for their shareholders and discusses this in the context of the stock market. It then explains how to use the discounted cash flow (DCF) method to value a company. In this respect, the course teaches students how to analyze historical performance, forecast free cash flows, estimate the appropriate cost of capital, and critically interpret results. The course then covers firm valuation through “multiples” and discuss how this method can be used to “supplement” the DCF method. Finally, the course covers some specialized valuation topics such as the challenges associated with valuing private companies, high-growth companies, cyclical companies, banks and foreign companies. It also discusses valuation in the context of mergers and acquisitions and divestitures.
AFN 539 Insurance Risk Management
This course introduces students to some of the more technical aspects of insurance and how it is used as a fundamental risk mitigation tool for both individuals and businesses. Topics include an overview of different types of insurance and their use, an introduction to the theory of interest, present value of random variables for contingent annuities and insurance, their distributions and the principles underlying the determination of insurance fair values. The advantages and disadvantages of private and social insurance programs will be discussed along with their place in today’s economies. References to the Basel II (Banks) and Solvency II (Insurance Companies) regulatory frameworks as well as rating agencies capital adequacy models will give the class a pragmatic approach to modern practices exercised by risk management professionals. The class provides a good foundation for students who also want to pursue professional credentials from International Risk Professional Associations (U.S.A. and U.K.) or an advanced degree in Risk Management and Insurance. The material to be covered by this course overlaps with some of the education requirements of SOA (Society of Actuaries, USA) and the Institute of Actuaries (UK).
DSC 532 Statistical Learning
Students will acquire the knowledge to conduct statistical analysis on a variety of data sets using a wide range of modern computerized methods. The students will learn how to recognize which tools are needed to analyze different types of datasets, how to apply these tools in each case, and how to employ diagnostics to assess the quality of their results. They will learn about statistical models, their complexity and their relative benefits depending on the available data. Some of the tools that will be discuussed include linear simple and multiple regression, nearest neighbors methods, shrinkage methods (ridge, lasso), dimension reduction methods (principal components), logistic regression, linear discriminant analysis, tree-based methods, model selection algorithms and clustering. The focus of the course will be less on theory and more on providing the students with as much intuition as possible and acquainting them with as many methods as possible. The course will make substantial use of the R statistical programming language and its libraries.
ECO 673 Applied Microeconometrics
Brief review of the classical linear regression model. Econometric models for cross-section data and time-series data. Economic applications and the use of specialized econometric software are emphasized. Topics will be drawn from: 1) models of multiple equations, 2) models of limited dependent variables, 3) elements of time-series analysis and models for macro and financial data.
MSc Dissertation
Allows students to work independently using the techniques learned and the skills mastered as part of the coursework. Dissertations are supervised by a full-time member of academic staff to ensure quality and continuity of supervision.
