Course Topics: This course will expose students to fairly complex financial modelling topics across a wide range of modern finance viz. corporate finance, stock markets, bond markets, derivatives, investments etc. and will take examples from multiple asset classes viz. equities, fixed income, FX and commodities. Areas taught in this course will cover a gamut of financial engineering and modelling applications:
1. Construction of optimal stock portfolios 2. Active Portfolio Management Theory and Practice 3. Modelling risk of large equity portfolios using economic and statistical techniques 4. Monte-Carlo Simulation to price European equity options 5. Risk Management and Trading of Options 6. Hedging bonds and swaps – Parallel and non-parallel shifts of the curve 7. Models of the yield curve 8. Order book and Working with stock market data 9. Linear Models and their applications
2. Student Learning Outcomes (typically 3-5 bullet points)
· Be able to understand the building-blocks of financial analysis and financial modelling · How to develop sophisticated financial models starting from simple ones, which describe reality · Be able to apply Excel and Python for financial modeling · Be able to understand limitations of models · Be able to tweak text-book models so that they apply in real-life · Be able to understand limitations of models and avoid potential pitfalls
3. Required Text Books and Reading Material
a) Pandas for Everyone: Python Data Analysis by Daniel Y Chen
· ISBN-10 : 9352869168 · ISBN-13 : 978-9352869169 b) Options, Futures and Other Derivatives by John C Hull – 9th or 10th edition whichever is easily available
Reference Text Book:
1) Financial Modelling (MIT Press) – Simon Beninga – A good reference for financial modelling topics in general 2) Financial Analysis and Modeling Using Excel and VBA (Wiley Finance) – Chandan Sengupta – A good reference for Excel and VBA 3) Financial Analysis with Microsoft Excel – Timothy R Mayes – A good reference for Financial Analysis 4) Modern Portfolio Theory and Investment Analysis – Elton and Gruber – An excellent reference for portfolio construction and optimization 5) Python for Finance: Analyze big financial data – Yves Hilpisch – A good reference for using Python in Finance
4. Tentative Session Plan
5. Evaluation
Prerequisites:
1) Previous programming experience is NOT required. We will cover all the required elements of VBA and python programming in class. 2) There are no other prerequisites. Financial modelling and empirical work will be needed both in classroom and in form of assignments. Assignments will be designed to give you an opportunity to implement models studied in the class.
Along with an end-term, main mode of learning and evaluation would be assignments. Grading will be on following criteria:
1) Assignment – 30% 2) In-Class Performance – 10% 3) Quizzes– 20% 4) End-Term – 40%
6. Academic Integrity
Code of Ethics:
You must abide yourself by the (unwritten) Code of Ethics for Students. For individual (group) assignments/examinations/projects, it is unethical to seek any direct help from others (other groups), whether or not you make use of the help. Besides, other forms of dishonesty (like plagiarism) would also invite severe punishment. Moreover, for a group assignment, all members of the group should contribute to the preparation of the report (no free-riders), and no direct help should be sought or taken from persons outside the group. Discussion among individual students and groups (except in the examination hall or class-room) is, of course, always encouraged. But, the final report or solution should be totally in your (your group’s) own style and language; any form of copying from another student (or group) or from any outside source is a serious offense. Moreover, you (or, wherever relevant, each member of your group) must fully and clearly understand every word and every step written in your (your group’s) report.
Your basic purpose should be to learn, without resorting to any unfair means for getting a higher score/grade. If you resort to any unfair means, including, but not limited to, the ones mentioned above, you would receive an F in the course; I may also recommend to the Institute for further penalty.
Academic Integrity (taken verbatim from the University of Texas, Dallas, format):
The faculty expects from its students a high level of responsibility and academic honesty. Because the value of an academic degree depends upon the absolute integrity of the work done by the student for that degree, it is imperative that a student demonstrate a high standard of individual honor in his or her scholastic work.
Scholastic dishonesty includes, but is not limited to, statements, acts or omissions related to applications for enrollment or the award of a degree, and/or the submission as one’s own work or material that is not one’s own. As a general rule, scholastic dishonesty involves one of the following acts: cheating, plagiarism, collusion and/or falsifying academic records. Students suspected of academic dishonesty are subject to disciplinary proceedings.
Plagiarism, especially from the web, from portions of papers for other classes, and from any other source is unacceptable and will be dealt with under the university’s policy on plagiarism (see general catalog for details). This course will use the resources of turnitin.com, which searches the web for possible plagiarism and is over 90% effective.
Created By: Alora Kar on 11/23/2020 at 03:33 PM Category: BM 19-21 T-VI Doctype: Document