This course is divided into two main branches namely microeconomics and macroeconomics. While macroeconomics is concerned with the economy as a whole, microeconomics examines the individual parts of the economy and focuses on how individuals and firms make decisions and how these decisions interact. This course is designed to introduce learners to the fundamentals of economics by exposing them to the principal theories and models used by economists. It demonstrates how economics can be used by individuals and businesses alike to assess problems and develop solutions. It also examines the wider macroeconomic environment and the principles underlying monetary, fiscal policy and international trade.

This course introduces students to the rigorous mathematical foundations of economics. It offers students the necessary tools upon which modern economic analysis and modeling is based. This course emphasizes training in mathematics used in economics through the many economic applications in which mathematical tools are needed.

The course covers four main topics. The first part covers the basics, such as functions and set theory. The second part teaches matrix algebra in relations to equilibrium analysis, while the third part introduces the concept of comparative statics and the tools needed, derivatives. The fourth topic again uses derivatives to do a rigorous exposition on the most important skill in economics: optimization. In each part, there will be many economic applications to let students familiarize with the mathematical concepts in an economic context and learn their real-world usefulness.

This course continues from Principles of Economics 1 to cover government interventions in markets, consumer demands, firms and market structures in the first part.

The second part of the course introduces students to the basic micro-foundations of macroeconomics, namely the consumption-leisure and consumption-saving trade-offs. The third part of the course covers the foundational theories of macroeconomics: the classical and Keynesian theories.

This course trains students in the rigorous mathematical foundations of economics. It offers students the necessary tools upon which modern economic analysis and modeling is based. This course emphasizes training in mathematics used in economics through the many economic applications in which mathematical tools are needed.

The course continues from ECON6018 by first covering the important techniques of constrained optimization. The second part extends the topics of matrix algebra to include further topics of linear algebra. The third part deals with integral analysis and probability theory. In each part, there will be many economic applications to let students familiarize with the mathematical concepts in an economic context and learn their real-world usefulness.

This course introduces students to the rigorous mathematical foundations of economics. It offers students the necessary tools upon which modern economic analysis and modeling is based. This course emphasizes training in mathematics used in economics through the many economic applications in which mathematical tools are needed.

The course covers five main topics. The first part covers matrix algebra in relations to equilibrium analysis, while the second part introduces the concept of comparative statics and the tools needed, derivatives. The third topic again uses derivatives to do a rigorous exposition on the most important skill in economics: optimization. The fourth topic revisits matrices in the wider context of linear algebra, covering the important concepts of eigenvalues, eigenvectors and quadratic forms. The fifth part gives an introduction into the definition and meaning of integrals, which is important for understanding economic dynamics. In each part, there will be many economic applications to let students familiarize with the mathematical concepts in an economic context and learn their real-world usefulness.

This course is divided into two main branches namely microeconomics and macroeconomics. While macroeconomics is concerned with the economy as a whole, microeconomics examines the individual parts of the economy and focuses on how individuals and firms make decisions and how these decisions interact. This course is designed to introduce learners to the fundamentals of economics by exposing them to the principal theories and models used by economists. It demonstrates how economics can be used by individuals and businesses alike to assess problems and develop solutions. It also examines the wider macroeconomic environment and the principles underlying monetary, fiscal policy and international trade.

Statistics abounds in our everyday lives, from economics to physics, accountancy to medicine, business to technology, geography to law, social science to politics, a veritable all-embracing giant. No discipline does well that disavows it or belittles it. Though many occupations may not require specialized statistics degrees, they demand at least a working application of statistics, and able competency with data crunching, statistical software and results management.

This course focuses heavily on running statistical analyses and applications through Microsoft Excel, with interpretation of results for sound decision making in practical cases, covering the basics to higher-level statistics. By the end of this course, students will:

• know the principles and elements of basic statistics;
• understand the need for data collection and summarize data sets into meaningful information;
• match and execute correct statistical procedures to different data types and write sound interpretations for use in practical decision-making

The main purpose of this course is to provide students with the necessary econometrics tools and techniques used to analyse data sets in economics, business and finance. The focus is on understanding the methods involved, using statistical software to provide the results and then interpreting and commenting on these results. The course introduces linear and multiple regression analysis, which remains the most commonly used statistical technique in econometrics. Other topics include model specification diagnostics, nonlinear regressions, multicollinearity, heteroskedastictiy, simultaneous equations, instrumental variable estimation, binary dependent variable, and OLS in matrix form.

The course covers the foundations of Microeconomics, which constitute the fundamentals of economic analysis. The topics covered are: consumer choice (individual and aggregate), producer theory, decision making under uncertainty and principles of strategic interaction (game theory). In particular, the course will provide an introduction to normal form games, extensive form games of perfect and imperfect information and to the solution concepts that are developed for these. The last part of the course will introduce the principles of general equilibrium theory and study the fundamental theorems of welfare economics.

The course is structured in a formal manner, typical for its level, and makes use of language and tools from Mathematics. Proofs of results will be provided when needed, so as to get the student acquainted with the rigorous thinking and systematic approach that is an integral part of economic analysis. Knowledge of the mathematical tools needed by an Economist is required. A nonexhaustive list is: Calculus (continuity, differentiation, integration), Constrained and Unconstrained optimization (Kuhn-Tucker conditions), Probability theory, Linear Algebra.

This course explores major macroeconomic topics using the general equilibrium frameworks with micro-foundations. We take a rigorous approach to address income, consumption, saving, investment, economic growth, business cycles and economic policy applications. The main objective of this course is to introduce you the workhorse models of modern macroeconomics and illustrate how they are used in conducting macroeconomic research as well as implementing government policies.

The course is rigorously structured and is conducted at a fast pace. You need to clarify any doubts at the earliest by contacting me, or through group discussions with your fellow classmates. Problem sets are an integral part of the course and it is imperative that you attempt all problems.

This course introduces the essentials of financial decision-making. The objective is to inculcate an understanding of the elements of strategic financial management and to build proficiency in their application to real world problems. Participants will be provided with a rigorous introduction to the foundations of business finance in competitive financial markets. The course is designed to equip managers and leaders with the basic tools that are necessary for managing a firm in a dynamic global financial environment. Emphasis will be placed on apply-ing these tools to arriving at optimal decision strategies that will maximize the value of the firm.

The application of econometric methods to the characterization of financial data, and to the estimation and testing of selected models of modern finance theory. Areas to be covered include: the statistical modelling and forecasting of financial time series, with application to share prices, exchange rates and interest rates; the specification, estimation and testing of asset pricing models including the capital asset pricing model and extensions; the modelling of volatility; practical application of volatility forecasting.

This course is aimed to help students to obtain an in-depth understanding of the major empirical finance issues, particularly in investments. This course would also cover the current theories of portfolio management and provide a conceptual framework for the evaluation of investment strategies. It aims to provide students with exposure to the process of investment management including identification of investment objectives and constraints, determining asset allocations, and measuring portfolio performance.

Topics include models of stock returns, multifactor asset pricing models, asset allocation, stock screening, predictability, performance evaluation, anomalies, and limits to arbitrage. It will also cover the fundamental concepts of investments, including risk and return, market efficiency, portfolio diversification, and risk management. Based on recent research articles and cases, students are required to learn the empirical facts, theories and the associated statistical tools to analyze financial data. The coverage will include applications and implementation.

This is an introductory course for continuous-time methods in economic and econometric applications. Topics covered include stochastic calculus, option pricing, stochastic control, microstructure theory, volatility estimation, forecast evaluation, and event study.

This course provides an overview of Machine Learning (ML) principals. The goal of the course is to teach you the basics of the ML theory and practice of ML algorithms. The course will build upon the knowledge and skills you gained in econometrics and statistics courses. Students will learn how to interpret, design, and execute empirical projects using software and skill building in critical thinking and problem solving will also be emphasized. We will additionally explain important issues and topics that recently happened in the ML literature.

Part A: This is a course on the mathematics of interest rates and bond management. Students will go through the basic theories of interest rates, including the computation of the future and present values of annuities. There will be an introduction to portfolio investment performance evaluation techniques, including internal rate of return, dollar-weighted return, time-weighted return, geometric mean rate of return, arithmetic mean rate of return and portfolio return. Estimation and theory of spot rate, forward rate and term structure will be discussed.

Part B: Financial derivatives have applications across many areas of finance, such as hedging, swaps, convertible claims, and corporate decision making. The course objective is to provide students with the necessary tools and techniques to determine the fair valuation of forward, futures, options and other derivative securities, and apply derivative securities in hedging risk exposures, such as commodity price risk, currency risk, interest rate risk, stock portfolio risk, and so forth. Furthermore, students will be given the opportunity to explore a comprehensive online financial markets simulation system (Stock-Trak) to obtain hands-on experience (of a fund manager :) in trading in the “real” market. For instance, students can trade futures and options on commodities such as gold, silver, corn, oil etc at the ‘market prices’ through Stock-Trak.

The aim of this course is to introduce students to alternative investment principles, products and the respective distribution capabilities. We will ground the discussions by laying out the view and practice of how new Alternative Investments reshape the institutional and private investment advice, now and into the future. Furthermore, we contextualize the product distribution capability through digital advisory platforms, and analyze it relative to traditional methods, considering their enhanced characteristics, principal advantages, and challenges to their further development.

The course is targeted at students with a basic understanding of investment management processes and alternative investments, interested in further understanding digital distribution frameworks and the emergence of robo-advisors as evolved means of customer engagement. The course is constructed in such a way that academic research and theoretical knowledge are supported by real life practice.

It provides the students with means to understand core alternative products and processes, important building blocks of related digital-advisory platforms, the purpose and needs for robo-advisors as innovative technologies. The students will be immersed in a real life investment exercise by allocating to suitable investments in the alternative space, practically defining and designing modules of the digital wealth advice eco-system.

Understanding an investment portfolio’s risk and performance characteristics is critical for sustainable top performance of investment portfolio. The problem is particularly challenging for investment portfolio that contains both public and private investments. This course looks to introduce course participants foundational skills (e.g. risk measurement, performance attribution, etc.) that provide insights to an investment portfolio. In addition to laying the theoretical grounding, the course also covers the practical aspects and implementation considerations..

This introductory course explores the world of blockchain, providing students with a qualitative understanding of its economic significance and real-world applications. Students will gain a critical appreciation of the underlying principles of blockchain technology and how these are applied in cryptocurrencies (e.g., Bitcoin, Ethereum) and Web 3.0 innovations (e.g., tokens, dApps, DeFi, DAO, NFTs, RWA tokenization, IAM, CBDCs, and more). The course blends theoretical foundations with real-world case studies to offer a well-rounded view of both the potential and challenges of blockchain and Web 3.0.

This course is designed to give students a thorough understanding of high-frequency trading (HFT) - the use of sophisticated algorithms and high-speed technology to execute trades in milliseconds. The course covers the evolution of HFT, explores the technological infrastructure required for low-latency trading, and teaches various HFT strategies such as market-making, arbitrage, and momentum trading.

Students will also investigate the impact of HFT on market liquidity, price discovery, and volatility, while considering the regulatory landscape that governs high-speed trading.

Group Project topics are current industry practices. Through practical programming exercises, case studies, and simulations, students will develop the ability to design and evaluate HFT strategies, leveraging Python for algorithmic execution.