Curriculum

Study Plan

Overview

The MFE offers both one-year full-time and two-year part-time programs in financial engineering. Classes are taught in English and take place during the weekday evenings or weekends. The full-time program has both thesis (plan A) and non-thesis (plan B) options, while the part-time program offers only the non-thesis option. To be qualified for non-thesis option, the applicants must provide evidence of at least one year of work experience (counted up to the end of May of the admission year).

To graduate from the program, each student must complete a minimum of 36 credits and maintain a GPA of 3.00 or higher. Students in plan A must complete a thesis of 12 credits, and students in plan B must complete a research project of 4 credits. The structure of the required and elective courses is as follow:

Program Structure Plan A Plan B
Foundation Courses (Credits not counted) 4 2
Coursework 24 32
    Core Courses 22 24
    Elective Courses 2 8
Thesis 12 -
Research project - 4
Comprehensive Examination No  Yes
Total Credits 36 36
Term1: Credit
2
Stochastic theory provides the language and the key technical concepts and tools for the study of financial mathematics. This course aims to introduce the basic ideas from probability which are of most relevance in finance, and to develop the machinery required to exploit these ideas. The course will begin with simple ideas such as events and random variables, but for continuous time processes which are used in financial modeling.
2
Financial engineers need to have a thorough knowledge of the financial system and the institutions therein. This course gives a comprehensive overview of the structure of the financial markets, as well as the role of central banks, commercial banks, insurance companies, and other financial institutions. This course also covers the basic financial instruments both in the spot markets and in the markets for derivatives. The focus is on the practical knowledge of the trading mechanism, as well as an introduction on the methods of pricing.
3
This course provides students with an essential foundation of financial theories before taking any further finance courses. The course covers the topics of investment/consumption decisions, expected utility theory, portfolio theories, asset pricing theories, and key corporate finance theories such as dividend policy, ownership structure and asymmetric information.
2
This course introduces the methodology of finding local and global optimality in financial model. Students will learn to choose the appropriate method for each type of financial decision problem. We will discuss linear and non- linear programming, simplex algorithm, linear duality, sensitivity, Newton’s method, Kuhn-Tucker conditions, saddle point conditions, convergence of algorithms, and portfolio optimization.
3
Statistics is the most fundamental technique students should be able to use in drawing inferences from the data. This course introduces the method of analyzing random sample data. Topics include the basic ideas and methods of statistical inference and the practice of statistics, such as estimation, forecasting, hypothesis testing, and basic regression analysis.
1 (S/U)
The FE program at Chulalongkorn University places strong emphasis on the ethical values of its graduates. This course ingrains students with the highest standards of professional conduct and code of ethics in the investment profession.
Term2: Credit
2
Stochastic calculus fundamentals are covered with a high level of clarity and is mainly focused on the theory associated with derivative securities. We move from a binomial stock market model to the more sophisticated continuous time models driven by Brownian motion. A significant portion is devoted to background on measure theory and Ito calculus.
2
This course will help you get to grips with the tools for the assessment and management of fixed income and credit risk. On completion of this course, students should be able to understand the time value of money and to calculate interest rates and discount factors. They should be able to apply these concepts to the pricing of simple, fixed-income financial instruments and the assessment of investment projects.
2
This course provides an in-depth coverage of various financial derivatives and the theory that underlies their valuation. The topics include option pricing using binomial trees, the Black-Scholes partial differential equation, as well as an early treatment of credit models of default. Since risk management plays an important role in any financial institution decision making process and control, this course also discusses how financial corporations identify risks, and use derivatives to manage and control the risks.
2
Time series data is a sequence of data points that you have been collected over a period of time. This course introduces you to time series data analysis focusing on financial applications. Topics will cover stationary and non-stationary processes, testing for unit root and cointegration, ARMA, ARIMA, ARCH, GARCH processes for univariate and multivariate variables. Some advanced models such as stochastic volatility models and hidden Markov models are also covered.
2 (S/U)
Writing research proposals and research papers is a crucial part of communicating your research ideas and research works to others. This course walks you through every step in writing research proposals and research papers, starting from choosing a research topic, setting the research aim and objectives, developing research questions, writing literature review, formulating research problems, explaining how to solve the problems, discussing the results and findings, and summarizing your works.
3
Students in the thesis track are required to complete a research proposal, and a thesis. The thesis must make significant academic contributions to the financial engineering field.
Term 3: Credit
2
This course teaches you how to estimate risky outcomes by using predetermined probability distributions and random numbers using various simulation techniques. Students will learn to methods of stochastic modeling, continuous and discrete simulation models, data structures, algorithms, random process generation, Monte Carlo simulation. The course, then, applies the simulation techniques to price derivatives, analyze investment strategies, and evaluate asset return models.
1 (S/U)
In this course, students participate in guided discussions on selected issues that are of currency in the field of financial engineering, including new practices or developments in the industry.
Elective 2
9
Students in the thesis track are required to complete a research proposal, and a thesis. The thesis must make significant academic contributions to the financial engineering field.
Term 1: Credit
2
Stochastic theory provides the language and the key technical concepts and tools for the study of financial mathematics. This course aims to introduce the basic ideas from probability which are of most relevance in finance, and to develop the machinery required to exploit these ideas. The course will begin with simple ideas such as events and random variables, but for continuous time processes which are used in financial modeling.
2
Financial engineers need to have a thorough knowledge of the financial system and the institutions therein. This course gives a comprehensive overview of the structure of the financial markets, as well as the role of central banks, commercial banks, insurance companies, and other financial institutions. This course also covers the basic financial instruments both in the spot markets and in the markets for derivatives. The focus is on the practical knowledge of the trading mechanism, as well as an introduction on the methods of pricing.
3
This course provides students with an essential foundation of financial theories before taking any further finance courses. The course covers the topics of investment/consumption decisions, expected utility theory, portfolio theories, asset pricing theories, and key corporate finance theories such as dividend policy, ownership structure and asymmetric information.
2
This course introduces the methodology of finding local and global optimality in financial model. Students will learn to choose the appropriate method for each type of financial decision problem. We will discuss linear and non- linear programming, simplex algorithm, linear duality, sensitivity, Newton’s method, Kuhn-Tucker conditions, saddle point conditions, convergence of algorithms, and portfolio optimization.
3
Statistics is the most fundamental technique students should be able to use in drawing inferences from the data. This course introduces the method of analyzing random sample data. Topics include the basic ideas and methods of statistical inference and the practice of statistics, such as estimation, forecasting, hypothesis testing, and basic regression analysis.
1 (S/U)
The FE program at Chulalongkorn University places strong emphasis on the ethical values of its graduates. This course ingrains students with the highest standards of professional conduct and code of ethics in the investment profession.
Term 2: Credit
2
Stochastic calculus fundamentals are covered with a high level of clarity and is mainly focused on the theory associated with derivative securities. We move from a binomial stock market model to the more sophisticated continuous time models driven by Brownian motion. A significant portion is devoted to background on measure theory and Ito calculus.
2
This course will help you get to grips with the tools for the assessment and management of fixed income and credit risk. On completion of this course, students should be able to understand the time value of money and to calculate interest rates and discount factors. They should be able to apply these concepts to the pricing of simple, fixed-income financial instruments and the assessment of investment projects.
2
This course provides an in-depth coverage of various financial derivatives and the theory that underlies their valuation. The topics include option pricing using binomial trees, the Black-Scholes partial differential equation, as well as an early treatment of credit models of default. Since risk management plays an important role in any financial institution decision making process and control, this course also discusses how financial corporations identify risks, and use derivatives to manage and control the risks.
2
Time series data is a sequence of data points that you have been collected over a period of time. This course introduces you to time series data analysis focusing on financial applications. Topics will cover stationary and non-stationary processes, testing for unit root and cointegration, ARMA, ARIMA, ARCH, GARCH processes for univariate and multivariate variables. Some advanced models such as stochastic volatility models and hidden Markov models are also covered.
2
Writing research proposals and research papers is a crucial part of communicating your research ideas and research works to others. This course walks you through every step in writing research proposals and research papers, starting from choosing a research topic, setting the research aim and objectives, developing research questions, writing literature review, formulating research problems, explaining how to solve the problems, discussing the results and findings, and summarizing your works.
1
Special project is a vital part in the graduate study. This course is designed to guide students by reviewing the literature, and teaching the students the process of developing research topic, designing methodology, and producing high-quality academic work.
Term 3: Credit
2
This course teaches you how to estimate risky outcomes by using predetermined probability distributions and random numbers using various simulation techniques. Students will learn to methods of stochastic modeling, continuous and discrete simulation models, data structures, algorithms, random process generation, Monte Carlo simulation. The course, then, applies the simulation techniques to price derivatives, analyze investment strategies, and evaluate asset return models.
1 (S/U)
In this course, students participate in guided discussions on selected issues that are of currency in the field of financial engineering, including new practices or developments in the industry.
Elective 2
Elective 2
Elective 2
Elective 2
3
Students are required to register for this course before they can graduate. To satisfy the requirement for this course, students must submit a completed version of their research paper and defend it in front of a group of committees.
Term 1: Credit
2
Stochastic theory provides the language and the key technical concepts and tools for the study of financial mathematics. This course aims to introduce the basic ideas from probability which are of most relevance in finance, and to develop the machinery required to exploit these ideas. The course will begin with simple ideas such as events and random variables, but for continuous time processes which are used in financial modeling.
2
Financial engineers need to have a thorough knowledge of the financial system and the institutions therein. This course gives a comprehensive overview of the structure of the financial markets, as well as the role of central banks, commercial banks, insurance companies, and other financial institutions. This course also covers the basic financial instruments both in the spot markets and in the markets for derivatives. The focus is on the practical knowledge of the trading mechanism, as well as an introduction on the methods of pricing.
3
This course provides students with an essential foundation of financial theories before taking any further finance courses. The course covers the topics of investment/consumption decisions, expected utility theory, portfolio theories, asset pricing theories, and key corporate finance theories such as dividend policy, ownership structure and asymmetric information.
Term2: Credit
2
Stochastic calculus fundamentals are covered with a high level of clarity and is mainly focused on the theory associated with derivative securities. We move from a binomial stock market model to the more sophisticated continuous time models driven by Brownian motion. A significant portion is devoted to background on measure theory and Ito calculus.
2
This course will help you get to grips with the tools for the assessment and management of fixed income and credit risk. On completion of this course, students should be able to understand the time value of money and to calculate interest rates and discount factors. They should be able to apply these concepts to the pricing of simple, fixed-income financial instruments and the assessment of investment projects.
2
This course provides an in-depth coverage of various financial derivatives and the theory that underlies their valuation. The topics include option pricing using binomial trees, the Black-Scholes partial differential equation, as well as an early treatment of credit models of default. Since risk management plays an important role in any financial institution decision making process and control, this course also discusses how financial corporations identify risks, and use derivatives to manage and control the risks.
Term3: Credit
2
This course teaches you how to estimate risky outcomes by using predetermined probability distributions and random numbers using various simulation techniques. Students will learn to methods of stochastic modeling, continuous and discrete simulation models, data structures, algorithms, random process generation, Monte Carlo simulation. The course, then, applies the simulation techniques to price derivatives, analyze investment strategies, and evaluate asset return models.
1 (S/U)
In this course, students participate in guided discussions on selected issues that are of currency in the field of financial engineering, including new practices or developments in the industry.
Elective 2
Elective 2
Term4 Credit
2
This course introduces the methodology of finding local and global optimality in financial model. Students will learn to choose the appropriate method for each type of financial decision problem. We will discuss linear and non- linear programming, simplex algorithm, linear duality, sensitivity, Newton’s method, Kuhn-Tucker conditions, saddle point conditions, convergence of algorithms, and portfolio optimization.
3
Statistics is the most fundamental technique students should be able to use in drawing inferences from the data. This course introduces the method of analyzing random sample data. Topics include the basic ideas and methods of statistical inference and the practice of statistics, such as estimation, forecasting, hypothesis testing, and basic regression analysis.
1 (S/U)
The FE program at Chulalongkorn University places strong emphasis on the ethical values of its graduates. This course ingrains students with the highest standards of professional conduct and code of ethics in the investment profession.
Term5: Credit
2
Time series data is a sequence of data points that you have been collected over a period of time. This course introduces you to time series data analysis focusing on financial applications. Topics will cover stationary and non-stationary processes, testing for unit root and cointegration, ARMA, ARIMA, ARCH, GARCH processes for univariate and multivariate variables. Some advanced models such as stochastic volatility models and hidden Markov models are also covered.
2
Writing research proposals and research papers is a crucial part of communicating your research ideas and research works to others. This course walks you through every step in writing research proposals and research papers, starting from choosing a research topic, setting the research aim and objectives, developing research questions, writing literature review, formulating research problems, explaining how to solve the problems, discussing the results and findings, and summarizing your works.
1
Special project is a vital part in the graduate study. This course is designed to guide students by reviewing the literature, and teaching the students the process of developing research topic, designing methodology, and producing high-quality academic work.
Term6: Credit
Elective 2
Elective 2
3
Students are required to register for this course before they can graduate. To satisfy the requirement for this course, students must submit a completed version of their research paper and defend it in front of a group of committees.
2
This course aims to provide both a theoretical and a practical understanding of numerical methods in finance, in particular those related to simulations of stochastic processes. In addition, the course will give an introduction to programming. The syllabus will cover a range of topics and aims to provide both a theoretical and a practical understanding of methods for solving partial differential equations by computer. It will stress the benefits and shortcoming of various methods for solving problems and teach the importance of program reliability testing. In particular, this course will impart a general computer competency.
2
This course aims to introduce various types of instruments traded in financial markets, the concepts of no-arbitrage pricing and hedging, and the mathematics of finance It gives a simple but rigorous treatment of the subject including a range of advanced topics, it is useful for students who wish to advance their knowledge in advanced theoretical results.
2
Practitioners often have to work with mathematical models of interest rates, credit risks, and portfolios of correlated instruments. This course covers these widely-used models, including the modeling of exotic derivatives and structured products.
2
This course introduces you to quantitative techniques to manage portfolio or a collection of investments. Topics include portfolio theory, portfolio optimization, utility maximization, strategic asset allocation, investment constraints, risk modeling and measurement, return modeling, economic indicators, estimating and forecasting returns and risk, trading cost modeling.
2
Algorithmic trading uses a computer program that follows a set of predetermined rules or algorithms to place orders. Machine learning algorithms find patterns and features in data to make better decisions or predictions. This course covers applications of machine learning algorithms and learning theory to algorithmic trading. We discuss how to estimate, select and cross-validate model parameters. The market microstructure and limit order book models are also covered. We test our trading algorithms using various performance measures and a backtesting method.
2
Topics in this course will be based on interests of students which may vary from year to year.
2
Credit risk is an important part of any financial contracts. This course covers various types of credit risk models, starting from basic to advanced models. Examples are Merton model, hazard rate models, latent factor models, copula, and credit rating migration models. Those models are used to price credit instruments and credit derivatives such as corporate bonds, credit default swaps, collateralized debt obligations and default baskets. Credit risk measurement and management is also covered in this course.
2
For students who would like to take further steps in their research works, this course provides opportunities for them to expand their research project scopes or develop new research problems and to work closely with their advisors to solve the problems.
2
Performance of mutual funds can be measured in different ways. However, funds with good performance may not imply that the fund managers are skillful. Performance evaluation and attribution helps differentiate skillful mangers from lucky managers. This course covers return-based and holding-based performance evaluation and attribution for equity, fixed-income and hedge funds.
2
Human decisions do not always follow rationality assumptions as in the classical financial theories. This course studies the behaviors of investors and corporations, and their impacts on financial markets and corporate actions. Sample topics covered in this course are overconfidence, representative heuristic, attribution theory, anchoring and prospect theory.
2
The course is designed to be a practical introduction to financial modeling. You will learn how things are really done in investment banking, and not just the theory that is taught and encourage you to probe the rationale and benefits of financial innovation to the various parties of the transaction. You will achieve an advanced knowledge of the basic tools of strategies traders utilize, the limits to arbitrage, and learn to investigate the behavioral and volatility techniques observed in financial markets.
2
This course provides tools of financial risk management, risk management with options, credit derivatives, interest rate derivatives, modeling credit risk. The course also includes applications of risk management for financial institutions, developments and current issues in risk management. Condition: Prerequisite 2604622
2
Computer programming is the essential tool for the students to enhance prebuilt financial model or develop your own model using programming techniques. This course covers programming techniques such as data manipulation, software project management, and spreadsheet application. Topics will cover important techniques of implementing financial models.
2
This elective course provides the foundation of macroeconomic theories that are closely linked to finance. The course discusses on topics of financial system and institutions, money creation, roles of expectation on markets and policy, monetary and fiscal policies, and economic indicators.