Covered warrants, equity warrants and options, subscription rights, stock index futures and options, and other equity derivatives. Issues of pricing and hedging. Institutional constraints. Portfolio management and other investment strategies. Path-dependent options such as Asian options, barrier options, lookback options, and forward-start options. Spread options, rainbow options, quantos, exchange options, basket options, as-you-like options, power options, digital options, and others. Pricing techniques and risk management purposes.

Financial Markets and Instruments. Management of foreign exchange, money market, and derivatives desks. Asset-Liability management. Regulatory issues. Corporate Valuation, restructuring, leveraged buyouts, mergers and acquisitions. Issues of deal structures and management of cashflows.

Portfolio Optimisation Theory. Capital Asset Pricing Models. Arbitrage Pricing Theories. Factor Models. Market Neutral Strategies. Abnormalities and Market Mispricing. Asset Allocation and Dynamic Portfolio Optimization. Portfolio Insurance Problems, and Global Funds Management.

The Graduate Internship in Financial Engineering course is a 4-unit course. It provides students in the Master of Financial Engineering program with a valuable opportunity to gain practical experience and apply their knowledge in real-world financial settings.

This module will cover both term structure models as well as the valuations of interest rate derivatives. The topics covered include Vasicek , Ho-Lee, Cox-Ingersoll-Ross (CIR), Heath-Jarrow-Morton (HJM) and LIBOR market models. On the numerical side it will cover Black-Derman-Toy (BDT) and Hull-White models as well as some simulation methods.

This module aims to facilitate students in developing the basic skills for independent research, and to promote their motivations and interests in finding and solving problems. During the study of a research question, students are to demonstrate their progress in acquiring techniques, and to develop presentation skills including effective oral communication and scientific research report writing. Offerings of this module in different years may have different areas of focus.

Topics relating to financial engineering.

Is there a role for financial engineering and financial risk management in facilitating the flow of funds towards economic, societal and environmental outcomes?

Climate change and the wider sustainability agenda now occupies centre-stage in the strategy and governance of banks, asset managers and financial start-ups. Financial regulators have also taken a proactive role in ensuring that sustainability risks – broadly defined – are incorporated in structured risk management practices with appropriate tools and expertise. Furthermore, there is growing demand for new financial instruments that can securitise and enable trading in environmental outcomes.   

This course will provide an introduction to the range of sustainability initiatives underway in the public and private sectors, from the perspective of a finance professional. It will link traditional approaches to financial analysis, risk framing and product design to the context of sustainable finance. It will explore methods of comparing and evaluating sustainability disclosures of companies (financial and non-financial) from the perspective of investing and risk management.  

Where possible, case-studies will be supplemented with practitioner guest speakers.

This is a course in quantitative economics with Python. It teaches students Python implementations of important models and ideas in macroeconomics and finance. Existing courses in macroeconomics and data science mostly focus on one aspect---either theory or data analysis---without integrating economic theory and computational tools. This course shows that to describe and explain data, it is necessary to have a theory as a guide; that describing is not the same as explaining; and that different types of models are useful for describing and for explaining. It also shows that to understand and apply economic theories, it’s necessary to grasp data science methods.

The topics include dynamics with matrices and their economic applications, rational expectations equilibria, and fiscal policy in a growth model. By the end of this course, students are expected to: 1) gain a deeper understanding of classic economic theories, and 2) apply computational methods and algorithms to solving economics and finance problems.

The course introduces quantitative investing with an emphasis on the development of systematic equity strategies. The topics covered include risk models, liquidity and cost of trading, technical and sentiment factors, portfolio construction and optimization, correlation and 2007 quant quake, and current trends in quant trading.

New topics and areas in financial products development and market applications.

New topic and areas in financial technologies including information technology applications, electronic commerce, and other electronic applications to finance problems.

Topics would cover various alternative investments and risk management.

The course consists of two parts - (i) statistical credit rating models and (ii) credit derivatives. The first part would cover various statistical credit rating models including Altman’s Z-score, logistic regression, artificial neural network and intensity models. The second part will cover various models used to price credit derivative as well as tools used to manage credit risk. The topics covered would include real and risk neutral probabilities of default, RiskMetricsTM, CreditRisk+, default correlation, Copula, Basket default swap, CDOs etc.

This module will provide students with the opportunity to work on real-world problems in quantitative credit analysis. The module will be project based within either a research or industry environment. Students will gain a detailed knowledge of the project subject matter, along with an overall understanding of quantitative credit analysis.

The projects will be group-based with up to three students in a group. Most of the groups will be based in RMI’s Credit Research Initiative, and students can also source for an external company to host their projects. This is a 6 Modular Credits (MCs) module.

This module aims to familiarize the students with the reality of trading within the financial markets environment. Beyond the pure trading principles, it covers the many aspects of trading decisions, in terms of risk control and limits, market and economic data and information, overall portfolio management, practical market standards and conventions, specificities of derivatives trading, trading styles and techniques to manage specific market situations. This is a 2 MCs module.

This module should prepare students to better grasp trading and financial markets and allow them to become effective in a work environment in a record short time.

This module will cover the advanced topics related to derivative pricing, including stochastic differential equations, martingale representation theorem and risk-neutral pricing, the change of numeraire argument and pricing of pathdependent options (e.g. barrier, lookback, and Asian options), optimal stopping and American options, jump diffusion processes and stochastic volatility for option pricing.

The fundamentals of financial market technologies and functionality in the Front-, Middle- and Back-offices, the interdependencies of their systems, typical user interfaces, through to typical system architecture will be taught. Principals of algorithmic trading will also be covered, and students will be challenged to design solutions for real-market trading strategies. This is a 2 MCs module.

The global financial crisis triggered a set of structural changes that continue to play out in market microstructure and market architecture. Practitioners, on both the buy-side and sell-side, are in the midst of responding to new regulations around bank capital, operational risk, supervision and other non-market factors. The backdrop is complicated further by apparent disinflation, greater potential for event risk, macro-prudential interventions and in places, negative interest rates. The risk management context is also coloured by innovation in ‘fintech’ and cyber-risk. Each year, the course will focus on a subset of these topics based on what is most “current”. The objective is to give students the ability to take the depth of technical skills acquired in core modules and apply them to the immediate context of potential employers. This is a 2 MCs module.

Targeted at graduate students with a strong interest in financial engineering topics, the course introduces the state-of-the-art machine learning approaches, from DNN to topic modeling, and the key concepts in Fintech, from cryptocurrencies to sentiment analysis. Besides lectures, AI academic researchers and industry professionals are invited to come to share their latest research, their understandings and outlooks of the main technologies behind machine learning and their applications in financial services.

The course covers C++ basic constructs (loops, variables, operators, and functions), built-in libraries, data structures, templates and object oriented programming techniques. It develops logical thinking aimed at designing algorithms to solve specific problems. Concepts are illustrated by examples drawn from the financial engineering domain. The course will ultimately provide with an overview of the components of a modern risk management system.

Targeting at graduate students with a strong interest in commodities topics, the course introduces the fundamental principles of the energy (oil, coal and gas) and hard (ferrous and base metals) commodity markets. Supply and demand dynamics for each market will be discussed, as well as the pricing structure and mechanism for each market.

We will also discuss typical financial dervitives (forward, future, swap, options and more exotic products) used by commodity market players for trading and hedging risks. Their features, applications and pricing methods will be discussed in details.