ECON 405: Quantitative Finance Course Description Winter 2009 Instructor: Tony S. Wirjanto Office: 204 Hagey Hall Telephone #: 519 888 4567 x 35210 Email: twirjant at uwaterloo dot ca
Location: PAS1229 Time: 05:30-06:50TTh Office Hours: 9:30-11:30W
This course introduces students to data analysis and statistical modeling in finance. It is a course designed to focus on statistical and computational aspects of finance. The emphasis of the course is on making a transition from a financial model to a statistical model using financial data. It involves specification of a financial model; estimation of a statistical model; testing of the assumptions of the statistical model; testing the implications of the financial model; forecasting from the statistical model. The modeling process involved in this course requires the use of finance theory, probability models, optimization techniques, statistical theory and analysis and computational skill. Topics in finance include asset-return calculations, portfolio theory, index models, factor pricing models, investment performance analysis, asset-return predictability, asset-return volatility modeling, Value at Risk, etc. Mathematical topics include optimization techniques involving equality and inequality constraints, basic matrix algebra, etc. Statistical topics include probability and statistics, such as distributions, shapes and characteristics of distributions, sampling distributions, estimation, hypothesis testing, forecasting etc.- with the use of calculus, descriptive statistics and data analysis, regression, time series methods, simulation of random data and resampling methods..
Course Requirements • • •
Problem sets with computer labs (50%) Midterm test (25%) Final Examination (25%)
The problem sets and computer labs constitute the core of the course and have been weighted accordingly in the grading scheme.
Pre-requisites Formally, the prerequisite is Econ321 or the equivalents. Econ371 is a co-requisite. More realistically, the ideal prerequisites are a course in calculus (through partial differentiation and constrained optimization using Lagrange multipliers), some familiarity with matrix algebra, a course in probability and statistics, an interest in finance (Econ371/372 or the equivalents would be helpful) and/or experience in working at a financial institution.
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Required Textbook Statistics and Finance: An Introduction, by David Ruppert, 2004, Springer-Verlag, New York.
Recommended Textbooks • •
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Introductory Statistics with R, Second Edition (Statistics and Computing, Paperback), by Peter Dalgaard, Springer-Verlag, New York. Modern Portfolio Theory and Investment Analysis, Sixth Edition, by E.J. Elton and M.J. Gruber, Wiley, New York, 2002. This text gives a very detailed treatment of portfolio theory. Financial Modeling, Second Edition, by Simon Benninga. MIT Press, 2000. This textbook covers financial modeling using Microsoft Excel. Value at Risk – The New Benchmark for Managing Financial Risk, 3rd Edition, by Philippe Jorion, McGraw–Hill, 2007. Risk Management and Financial Institutions, by John C. Hull. Prentice Hall, 2006. This textbook deals with the ways in which risks are quantified and managed by financial institutions. Among the topics covered are market risk, credit risk, operational risk, the regulation of banks, and the credit derivatives market. The Econometrics of Financial Markets by John Y. Campbell, Andrew W. Lo, & A. Craig MacKinlay, Princeton University Press, 1996. Software
The course will utilize Microsoft Excel for spreadsheet modeling, and R or Matlab for data analysis and statistical modeling. However you are free to use the software of your choice (such as SHAZAM, STATA, etc) with the permission of the instructor. R is a free open-source statistical modeling and graphical analysis language built upon the S language developed at Bell Labs, R can be downloaded from www.r-project.org. We will be using several user-created packages (libraries of R functions) specifically designed for the analysis of financial time series data.
Lecture Topics 1. Asset Return Calculations 2. Probability Review: univariate random variables and probability distributions; bivariate distributions, linear combinations of random variables 3. Time Series Concepts: stationarity, AR, MA and ARMA models 4. Review of Matrix Algebra 5. Descriptive Statistics: histograms, sample statistics, qq-plots, boxplots, scatterplots, sample autocorrelations 6. Constant Expected Return (CER) Model: Representation, Bootstrapping, and Hypothesis Testing 7. Portfolio Theory: Introduction, Matrix Algebra Representation 8. Single Index Models: Representation, Linear Regression Framework, Rolling Analysis, Capital Asset Pricing Model 2
9. Multi Index Models 10. Measuring Portfolio Performance 11. Asset Return Predictability 12. Asset Return Volatility Modeling 13. Concepts and Tools of Quantitative Risk Management
Policies Notes on problem sets and a midterm test: Problem sets will be both analytical and involving computer applications. Problem sets are due at the beginning of class on the specified due date. While collaborative work is encouraged, you must write the answers to the assignments in your own word, submit your own copy of the assignment, including a copy of your printed computer output for any computer assignment. There is no make-up midterm test in this course. For students with a valid medical certificate for the missed test, the weight of the test will be transferred to the final examination. For this to take effect, you must provide supporting documentations within 14 calendar days of the day of the test. Note on avoidance of academic offenses. All students registered in the courses of the Faculty of Arts are expected to maintain a culture of academic integrity by upholding honesty, trust, fairness, respect and responsibility. Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4, http://www.adm.uwaterloo.ca/infosec/Policies/policy70.htm Discipline: A student is expected to know what constitutes academic integrity, to avoid committing academic offenses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. When misconduct has been found to have occurred, disciplinary penalties will be imposed under Policy 71 – Student Discipline. For information on categories of offenses and types of penalties, students should refer to Policy 71 - Student Discipline, http://www.adm.uwaterloo.ca/infosec/Policies/policy71.htm Appeals: A student may appeal the finding and/or penalty in a decision made under Policy 70 Student Petitions and Grievances (other than regarding a petition) or Policy 71 - Student Discipline if a ground for an appeal can be established. Read Policy 72 - Student Appeals, http://www.adm.uwaterloo.ca/infosec/Policies/policy72.htm Note for students with disabilities. The Office for Persons with Disabilities (OPD), located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate 3
accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the OPD at the beginning of each academic term. Note on student access to a final examination paper. Students can review their final examination papers informally without instituting a formal appeal procedure. Such review will take place under supervised access only, and will be arranged in a way that is mutually convenient for the instructor and the student. Note on accommodation for religious or cultural observances. Students can request for accommodation for a final examination for religious or cultural reasons. This request must be made with Professor William Chesney, Associate Dean of Arts, Undergraduate Studies, at
[email protected], within a week of the posting of the final exam dates.
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