| CODE | ARI5122 | ||||||||||||
| TITLE | Financial Engineering | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | Financial Engineering is a multidisciplinary field drawing from finance and economics, mathematics, statistics, engineering and computational methods. The study-unit is designed to blend the areas of Artificial Intelligence with finance. The students will be introduced to a range of methods starting from simple stochastic models to price derivative, portfolio and risk management, securities in various asset classes including equities, fixed income, credit and mortgage backed securities. The study-unit will also consider the role that some of these asset classes played during the financial crisis. Study-unit Aims: This study-unit provides a thorough grounding in the theory and practice of financial engineering. The emphasis is on the application of derivatives pricing and hedging methodology to equity and volatility derivatives and to structured products. Through this study-unit, students will be given the opportunity to: - Learn the relevant techniques in Financial Engineering, the reasoning behind them and when to use them; - Learn how to use a range of modeling and data analytical techniques; - Evaluate the advantages and limitations of different technologies; - Learn and work both independently and within groups; - Develop balance between theoretical and practical skills. Learning Outcomes: 1. Knowledge & Understanding: Students who complete the study-unit will begin to understand the science behind financial engineering but perhaps more importantly, they will also understand how to design and apply machine learning models in view of the particular finance data characteristics whilst also taking into consideration important aspects such as the management of risk. They will also: - Gain an in depth understanding of tools and techniques employed in Financial Engineering; - Develop methods for analyzing huge amounts of data and deduce useful conclusions; - Cover topics such as (but not limited to) Computational Finance, Financial Data Analytics, High Frequency Finance, Statistical Modeling of Large Data Sets and Forensic Asset Tracing and Tracking systems. 2. Skills: Students will be able to develop derivatives pricing models but it will also focus on asset allocation and portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. They will also: - Learn how to tackle specific data-intensive problems; - Select, use, and deploy specialised tools for Financial Engineering. Main Text/s and any supplementary readings: - Ruppert, David. Statistics and data analysis for financial engineering. New York: Springer, 2011. - Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2017. - Glasserman, Paul. Monte Carlo methods in financial engineering. Vol. 53. Springer Science & Business Media, 2013. - Neftci, Salih N. Principles of financial engineering. Academic Press, 2008. - Iba, Hitoshi, and Claus C. Aranha. Practical Applications of Evolutionary Computation to Financial Engineering. Springer, 2012. |
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| STUDY-UNIT TYPE | Lecture & Independent Online Learning | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Hani Kamel Arebi Martha Axiak Vincent Vella (Co-ord.) |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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