| General Course Information: | |
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COMS E6998.008 IT FOR FINANCIAL RISK MANAGEMENT M 06:00P-08:00P SEELEY W. MU 633 |
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| Instructor Information | |
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Gitanjali M Swamy |
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| TA Information: | |
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Monal Sanghvi |
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| Prerequisites | |
| The student is expected to have a basic knowledge of Math, Statistics and a Familiarity with Financial Concepts. Ideally the student would have taken COMS W4995.2 or COMSW4995.3 (Financial Technology) Prior to this class. | |
| Course Objectives | |
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This
course is a follow-up to Financial Technology COMS W4995-3. While, the
prior Financial Technology course focused on understanding the basics,
this class will delve into the specific issues of Financial Risk
Management using Technology. It is expected that by the end of the course students will understand both the frameworks for risk as well as the underlying technology to implement them. This class is about the 3Ms of Financial Risk Management: Measurement, Monitoring and Managing Risk using technology. Recent Financial Crisis' have exposed the criticality of not just risk management but enterprise level risk management in the financial industry. Contrary to common perception, IT departments in financial institutions are not just about technical support to financial professionals. In fact, IT is mislabeled and the technology in the financial profession is the ONLY tangible instantiation of the financial product or service. Thus, ensuring that a Financial offering continually meets the basic risk and reward tradeoffs that it purports to, is more of a technology problem than a governance problem. Unfortunately risk is not defined uniformly; each business in a financial institution defines it differently. The equities department thinks of risk as the volatility in the stock price, the fixed income department thinks of it as the probability of default and the alternatives industry don't even have an analytical definition of risk. This course will start by defining the 3Ms of Financial Risk Management: Measuring, Monitoring and Managing. We will examine each asset class/financial business line separately and review the traditional definitions of risk in it. In addition to reviewing the traditional definitions of risk, we will delve deeper into the current technology for implementing the particular risk framework for a particular product and examine the key technology challenges in each case. Once this is complete, we will start definition a common enterprise framework for risk and show how each particular product risk framework can be translated into the common framework. We will also research the shortcomings of industry frameworks such as VAR and six-sigma. In the first section we will introduce frameworks for enterprise risk management. We will cover VAR, Six-Sigma and other industry frameworks. Next we will cover the methods for deriving these measures including simulations, bayesian networks, markov models, variance-covariance, monte carlo, analytical and parametric methods. After the introduction, the next section of the class will deal with risk in the asset management business. We will cover statistical frameworks for risk and introduce the Markowitz model for asset allocation/ risk management. We will then delve into the technical implementation of the Mean Variance Optimizer and the technical challenges. We will also examine mutual funds and the management of risk in the fund industry, including rating systems such as Morningstar. We will conclude with a summary of protocols and algorithmic methods that are used to handle these technical challenges. The second section of the class will deal with risk in the public equities market including stocks, currencies and options. We will discuss how risk is measured and basic hedging strategies for risk management. We will examine the risk issues in long, short and hybrid strategies. This part of the course will introduce simulation methods and complexity of simulation models used today. We will explain back-testing and the limitations of the approach. The section will conclude with the future areas for technical research. The third section of the class will cover risk in the credit markets. We will review the probability of default as a measure of risk in this environment and introduce rating systems. Next we will delve into modeling with a review of Artificial Neural Networks and their use in credit risk management. The section will conclude with typical implementations and areas for research. The fourth sections will focus on new products including alternatives such as private equity, real estate and hedge funds. We will review risk management processes in this industry and the frontier of new forms of risk management. The fifth section will cover integrating all the prior types of risk analysis into the single enterprise view. We will cover the translations between different measurements as well as the timing, topological, complexity and other issues around around attempting a dynamic enterprise level view. The final section summarizes the key lessons learned and the hypothesis on the future. |
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| Method of Instruction | |
| This course will use a combination of analytic and case based pedagogy. Some of the sections will be covered by guest lecturers who have built the systems under discussion. The course will introduce a wide variety of financial tools and analytical methods. The final exercise will consist of a project that demonstrates some aspect of a financial risk management system, clearing demonstrating measuring, monitoring and managing risk for a financial offering | |
| Method of Evaluation | |
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Documents You'll perform a design-it-yourself project in the second half of the class. There are five deliverables for the project: 1. A short project proposal describing in broad terms the arena you want to build a financial system for, the thesis or hypothesis, what you plan to research, what you plan to build/demo 2. A detailed project design describing in detail the functional specification, architecture of your project, functional. This should include block diagrams, algorithmic modules: everything someone else would need to understand your design. You should have done some preliminary implementation work by this point to validate your design. 3. An early demo of your project and progress. It should include the theoretical discussion and be roughly 75% working and be showing signs of life. This is to make sure you are making reasonable forward progress. 4. A presentation on your project to the class 5. A final project report Project groups should be three students or more. Grading 30 % Class participation and summaries 50 % Project 20 % Quizzes NO EXAMS Late Policy Zero 25% for anything handed in after it is due without explicit approval of the instructor. |
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References The course will not have a text book. The reading and case study material links will be provided the week before. Typically the relevant documents can be bought and downloaded from Harvard Business Publishing at www.hbsp.com. Additional references include 1. Z. Brodie, R. Merton, The Design of Financial Systems: Towards a Synthesis of Function and Structure, Harvard Business School 2. Robert C. Merton and Zvi Bodie, "Design of financial systems: towards a synthesis of function and structure" 3. Charles Mackay (Author), Andrew Tobias (foreword), "Extraordinary Popular Delusions & the Madness of Crowds" 4. Nassim Nicholas Taleb, "The Black Swan: The Impact of the Highly Improbable" 5. Robert C. Merton, "Continuous-Time Finance" 6. John Cochrane, The Risk and Return of Venture Capital, University of Chicago 7. David F. Swensen, "Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment" 8. Thomas Meyer, Pierre-Yves Mathonet, Beyond the J Curve: Managing a Portfolio of Venture Capital and Private Equity Funds 9. Richard Brealey, Stewart Myers, and Franklin Allen, "Principals of Corporate Finance" 10. Thomas Cormen, Charles Leiserson, Ronald Rivest, Clifford Stein, "Introduction to Algorithms" 11. N. Gregory Mankiw, Macroeconomics 12. George Tillmann, "The Business-Oriented CIO: A Guide to Market-Driven Management" 13. Mark L Berenson, David M. Levine, Timothy C. Krehbiel, "Basic Business Statistics" 14. Steve Maguire, Writing Solid Code: Microsoft's Techniques for Developing Bug-Free C Programs (Microsoft Programming Series) 15. California Pension Fund Alternative Investment Report, Strategic Program Review. 16. Moody's Investors Service, "Rating Methodology" 17. National Venture Capital Association Handbook, 2007 18. Philipe Jorion, Value at Risk, Mcgraw Hill, 2000 19. D.H. Stamatis, Six Sigma for Finance Professionals, Wiley 20. Tarantino and Cernaukas, Risk Management in Finance, John Wiley |
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