Postgraduate Courses

On successful completion of the course, students will be able to:

• 1.
  Develop a rigorous probabilistic framework and lay a firm foundation for stochastic calculus.
• 2.
  Recognize the most important results, including Ito's Lemma, Girsanov change of measure.
• 3.
  Employ numerical PDE or Monte Carlo method to solve the stochastic differential equations.
• 4.
  Recognize the Black-Scholes models and more general diffusion models.
• 5.
  Apply the theory in stochastic calculus to financial problems, e.g. option pricing.

On successful completion of the course, students will be able to:

• 1.
  Recognize fundamental concept of randomness and analyze it using probability framework.
• 2.
  Recognize the law of large numbers and the central limit theorem and their applications.
• 3.
  Evaluate the statistical procedure of data analysis.
• 4.
  Formulate the basic statistical problems from one sample problem to linear regression.
• 5.
  Apply methodology of statistical inference to solve practical problems.

On successful completion of the course, students will be able to:

• 1.
  Formulate and evaluate pricing models for derivatives.
• 2.
  Analyze effectiveness of hedging strategies for monitoring risks in derivatives.
• 3.
  Make appraisal of the dynamics of stock prices and commodity prices.
• 4.
  Provide solution using structural derivatives in wealth management.

On successful completion of the course, students will be able to:

• 1.
  Describe the operation of fixed-income markets and the roles of the fixed-income derivative.
• 2.
  Apply major mathematical tools for fixed-income modeling.
• 3.
  Formulate major classes of fixed-income models and apply them for fixed-income pricing and risk management.
• 4.
  Evaluate the effectiveness of popular models for different sectors of derivatives.
• 5.
  Analyze exotic derivatives and choose pricing models.

On successful completion of the course, students will be able to:

• 1.
  Identify and explain the core ideas on financial data analysis by using statistical models.
• 2.
  Apply rigorous, analytic, highly numerate approaches to analyze and solve problems in daily life and at work with SAS and R, especially in finance.
• 3.
  Carry out objective analysis and prediction of quantitative information in finance with independent judgment.
• 4.
  Communicate effectively about statistical results obtained from R and SAS to both lay and expert audiences utilizing appropriate information and suitable technology.

On successful completion of the course, students will be able to:

• 1.
  Recognize market indexes, financial time series and their features.
• 2.
  Recognize the foundation of time series and basic time series models.
• 3.
  Formulate time series models to study financial data, including market returns and volatility.
• 4.
  Evaluate the relationships of different markets via the cointegration time series and ECM models.
• 5.
  Analyze the real financial data with the statistical techniques from this course via a course project.

On successful completion of the course, students will be able to:

• 1.
  Apply data analysis and statistical inference in financial applications.
• 2.
  Formulate the construction of the factor models and apply them in asset management and other applications.
• 3.
  Perform statistical analysis of investment models.
• 4.
  Analyze financial problems using the Bayesian methods and nonlinear regression models.
• 5.
  Evaluate the effectiveness of statistical trading strategies.
• 6.
  Design and evaluate statistical models of risk management and their implementation.

On successful completion of the course, students will be able to:

• 1.
  Apply investment models in financial applications.
• 2.
  Formulate quantitative investment with Mean-Variance Optimization and Black-Litterman models.
• 3.
  Analyze equity valuation using asset pricing models.
• 4.
  Evaluate the effectiveness of investment strategies.
• 5.
  Design and implement asset management strategies and structured solutions.

On successful completion of the course, students will be able to:

• 1.
  Analyze risk on derivatives and other financial products using quantitative and statistical methods.
• 2.
  Evaluate the price impact on financial products due to quantifiable risks.
• 3.
  Devise computer programs to compute the risk using numerical methods.
• 4.
  Analyze the capital requirements for financial institutions using quantitative and statistical methods.

On successful completion of the course, students will be able to:

• 1.
  Design and develop functions in C++ for financial applications.
• 2.
  Recognize the basic object oriented modeling and programming paradigm and its application on computational finance.
• 3.
  Translate financial models into C++ applications.
• 4.
  Handle the complexities in real world financial calculations.

On successful completion of the course, students will be able to:

• 1.
  Design computer algorithms for pricing structured derivatives.
• 2.
  Devise and compute hedging strategies for monitoring risks in derivatives.
• 3.
  Construct numerical algorithms for performing model calibration in pricing models of financial derivatives.
• 4.
  Provide solution using structural derivatives in wealth management.

On successful completion of the course, students will be able to:

• 1.
  Design and develop programs in Java for financial application.
• 2.
  Work with large sophisticated software facilities.
• 3.
  Translate financial models into spreadsheet applications.
• 4.
  Use VBA to build user defined functions for computational finance.
• 5.
  Handle the complexities in real world financial calculations.

On successful completion of the course, students will be able to:

• 1.
  Apply mathematical theory of market microstructure in trading risk management.
• 2.
  Conduct data analysis with computer programming languages such as R and/or Python.
• 3.
  Analyze problems with real industry data (such as tick market data and trading data).
• 4.
  Draw meaningful implications to capture market behavior at micro level in algorithmic and low-latency trading.
• 5.
  Conduct independent research to handle the complexities in real-world trading applications.

On successful completion of the course, students will be able to:

• 1.
  Apply financial mathematical models in studying capital market transactions, especially for markets in greater China region.
• 2.
  Analyze cross-border financial transactions with real industry data at both macro- and micro-levels.
• 3.
  Apply risk models to assess and hedge currency exposure (in particular RMB/RMH).
• 4.
  Draw meaningful implications to tap the market opportunities in both onshore and offshore markets in financing, investment and risk management.
• 5.
  Conduct independent research to handle the complexities in real-world finance applications.

On successful completion of the course, students will be able to:

• 1.
  Apply mathematical theory of portfolio optimization in trading and risk assessment.
• 2.
  Implement different portfolio methods with the programming language R.
• 3.
  Execute realistic backtesting to assess strategies with real industry data (such as market price data).
• 4.
  Draw meaningful implications to capture market behaviors in trading.
• 5.
  Conduct independent research to handle the complexities in real-world trading applications.

On successful completion of the course, students will be able to:

• 1.
  Design computer algorithms for pricing structured derivatives.
• 2.
  Devise and compute hedging strategies for monitoring risks in derivatives.
• 3.
  Construct numerical algorithms for performing model calibration in pricing models of financial derivatives.
• 4.
  Provide solution using structural derivatives in wealth management.

On successful completion of the course, students will be able to:

• 1.
  Describe and explain machine learning methods.
• 2.
  Conduct data analysis with computer programming languages such as R and/or Python.
• 3.
  Analyze problems with real industry data (such as trading data).
• 4.
  Interpret the data analysis results.
• 5.
  Derive machine learning algorithms.

On successful completion of the course, students will be able to:

• 1.
  Apply mathematical theories to derive trading algorithms and implement using Python.
• 2.
  Conduct data analysis using Python.
• 3.
  Analyze problems with real industry data (such as trading data).
• 4.
  Draw meaningful implications to capture market behaviors in trading.
• 5.
  Conduct independent research to handle the complexities in real-world trading applications.

On successful completion of the course, students will be able to:

• 1.
  Develop and evaluate quantitative models/strategies for emerging financial technologies.
• 2.
  Perform effective scenario simulations using statistical techniques in financial mathematics.
• 3.
  Program and develop applications for analysis of financial data and design numerical methods for calibration of model parameters from market data.
• 4.
  Analyze problems from finance in quantitative terms and develop strategies for effective solution of the problems.

On successful completion of the course, students will be able to:

• 1.
  Develop and evaluate quantitative models/strategies for emerging financial technologies.
• 2.
  Perform effective scenario simulations using statistical techniques in financial mathematics.
• 3.
  Program and develop applications for analysis of financial data and design numerical methods for calibration of model parameters from market data.
• 4.
  Analyze problems from finance in quantitative terms and develop strategies for effective solution of the problems.

On successful completion of the course, students will be able to:

• 1.
  Identify and apply the theories and methods related to the course topic.
• 2.
  Recognize the major issues and updated development in the area of the course topic.
• 3.
  Relate the concepts and methodologies in the course topic to practical implementation in the financial industries.

On successful completion of the course, students will be able to:

• 1.
  Critically analyze a selected topic to identify and formulate practical problems in financial mathematics.
• 2.
  Recognize the key issues analyzed in the models and their limitations.
• 3.
  Design effective analytical and computational solution methodologies to solve the formulated models.
• 4.
  Provide quantitative interpretations of various financial phenomena in the formulated problems from the numerical results.
• 5.
  Communicate solution concepts and methods effectively to a range of audiences, both orally and in writing.