ECON 424/AMATH 462:  Computational Finance and Financial Econometrics

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Class Syllabus

Summer 2013

Note 1: In the Reading column below, "ZLM" refers to A Beginner's Guide to R by Zuur, Leno and Meesters; "R Cookbook" refers to R Cookbook by Teetor; "EZ" referes to lecture notes by Eric Zivot, "EG" refers to Modern Portfolio Theory by Elton and Gruber, "Ruppert" refers to Statistics and Data Analysis for Financial Engineers by Ruppert . "*" denotes optional reading.

Note 2: Recent changes to the reading list are denoted with .

Note 3: My lecture notes are preliminary and incomplete and are not guaranteed to be free of errors. Also, as the quarter progresses I will be making changes and additions to the notes so check the revision dates to make sure you have the most up to date set of notes. Please let me know if you find typos or other errors. 

Last updated on  March 14,  2013
Week Topic Reading Additional Material
1

  1. Course Introduction

  2. Computing Asset Returns

  3. Getting financial data from Yahoo!

  4. Excel calculations

  5. Introduction to R

  1. Ruppert, chapter 2 (Returns).

  2. EZ, Lecture notes on return calculations.

  3. EZ, class slides on course introduction.

  4. EZ, class slides on return calculations.

  5. ZLM, chapters 1-3, 5.

  6. R Cookbook, chapters 1 - 5, 10 (sections 1 - 15)

  7. An Introduction to R, sections 1-3, 6 and 7.

  8. R for Beginners, sections 1-3.

  9. *EG, chapters 1-3

  1. finance.yahoo.com Check out finance/quote section

  2. returnCalculations.xls

  3. returnCalculations.r

  4. msftPrices.csv, sbuxPrices.csv

  5. tablet PC notes for lecture 2

  6. returnCalculationsPowerpoint.pdf 

  7. Rintro.pdf (introduction to R covered in the Friday TA session)

  8. table PC notes for lectures 2 and 3 (return calculations)

 
2 & 3

  1. Univariate random variables and distributions

  2. Characteristics of distributions

  3. The normal distribution

  4. Linear function of random variables

  5. Quantiles of a distribution, Value-at-Risk

  6. Bivariate distributions

  7. Covariance, correlation, autocorrelation

  8. Linear combinations of random variables

  9. Time Series concepts

  10. Matrix algebra

  1. Ruppert, chapter 5 (Modeling Univariate Distributions), chapter 7 (Multivariate Statistical Models), chapter 9 (Time Series Models: Basics), Appendix (sections 1-10, 12-15, 20)

  2. EZ, Lecture notes on review of univariate random variables and probability.

  3. EZ, Lecture notes on time series concepts.

  4. EZ, Lecture notes on review of matrix algebra.

  5. EZ, class slides on probability review: Part I.

  6. EZ, class slides on probability review: Part II.

  7. EZ, class slides on time series concepts.

  8. EZ, class slides on matrix algebra.

  9. ZLM, chapters 3-7.

  10. R Cookbook, chapter 8 and chapter 14 (sections 1 - 16).

  11. An Introduction to R, section 8.

  12. R for Beginners, section 4.

  1. probReview.xls

  2. probReview.r

  3. probabilityReviewPowerPoint.pdf

  4. timeSeriesConceptsPowerPoint.pdf

  5. timeSeriesConcepts.r

  6. matrixReviewPowerpoint.pdf

  7. matrixReview.r

  8. tablet PC notes for lecture 3

  9. tablet PC notes for lecture 4

  10. tablet PC notes for lecture 5

  11. tablet PC notes for lecture 6

  12. tablet PC notes for lecture 7

  13. Working with time series data in R
4-5

  1. Descriptive statistics: histograms, sample means, variances, covariances and autocorrelations

  2. The constant expected return model.

  3. Monte Carlo simulation

  4. Standard errors of estimates

  5. Confidence intervals

  6. Bootstrapping standard errors and confidence intervals

  7. Hypothesis testing 

  8. Maximum likelihood estimation

  1. Ruppert, chapter 4 (Exploratory Data Analysis), chapter 5 sections 9 and 10 (maximum likelihood estimation), chapter 6 (Resampling), Appendix (sections 11, 16 - 18)

  2. EZ, class slides on descriptive statistics.

  3. EZ, class slides on CER model.

  4. EZ, lecture notes on the CER model.

  5. EZ, class slides on bootstrapping

  6. EZ, class slides on hypothesis testing in the CER model.

  7. EZ, class slides on maximum likelihood estimation.

  8. Bootstrap Methods and Permutation Tests, by Tim Hesterberg. Read sections 1 - 5.

  9. R Cookbook, chapter 9 (General Statistics) chapter 10 (Graphics), chapter 13 (Beyond Basic Numerics and Statistics, section 8 on Bootstrapping).

  10. An Introduction to R, section 12.

  1. descriptiveStatisticsPowerPoint.pdf

  2. descriptiveStatistics.r

  3. descriptiveStatisticsDailyPowerPoint.pdf

  4. descriptiveStatisticDaily.r

  5. cerExample.csv

  6. cerModelExamples.r

  7. cerModelPowerPoint.pdf.

  8. bootStrapPowerPoint.pdf

  9. bootStrap.r

  10. hypothesisTestingCERpowerpoint.pdf

  11. hypothesisTestingCER.r

  12. maximumLikelihoodPowerpoint.pdf

  13. maximumLikelihood.r

  14. maxLike R package vignette.

  15. tablet PC notes for lecture 8

  16. tablet PC notes for lecture 9

  17. tablet PC notes for lecture 10

  18. tablet PC notes for lecture 11
6-7

  1. Midterm exam: Tuesday July 24th in Smith Hall (SMI) 102 from 4:40-6:60pm

  2. Midterm solutions and grade distribution 

  3. Introduction to portfolio theory

  4. Optimization

  5. Markowitz algorithm

  6. Markowitz Algorithm using the solver and matrix algebra

  7. Risk budgeting

 

  1. Ruppert, chapter 11 (Portfolio Theory).

  2. EZ, lecture notes on introduction to portfolio theory.

  3. Notes on using Excel's solver.

  4. EZ, class slides on Introduction to Portfolio Theory.

  5. EZ, class slides on portfolio theory with matrix algebra.

  6. EZ, lecture notes on portfolio theory with matrix algebra. 

  7. R Cookbook, chapter 13 (Beyond Basin Numerics and Statistics, sections 1 - 2)

  8. *EG, chapters 5 and 6

 

  1. introPortfolioTheory.xls

  2. 3firmExample.xls

  3. introductionToPortfolioTheory.r

  4. introductionPortfolioTheoryPowerpoint.pdf

  5. portfolioTheoryMatrixPowerpoint.pdf

  6. portfolioTheoryMatrix.r

  7. portfolio.r (R functions for portfolio analysis with short sales)

  8. testport.r (Examples of using R functions for portfolio analysis with short sales)

  9. portfoliofunctions.pdf (description of R functions for portfolio analysis with short sales)

  10. portfolioFunctionPowerPoint.pdf

  11. tablet PC notes for lecture 12

  12. tablet PC notes for lecture 13

  13. tablet PC notes for lecture 14

  14. tablet PC notes for lecture 15

  15. portfolioTheoryRpowerPoint.pdf. (updated November 12, 2008)
8 & 9

  1. Statistical Analysis of Efficient Portfolios

  2. Beta as a measure of portfolio risk

  3. The Single Index Model

  4. Estimating the Single Index Model using simple linear regression

  1. Ruppert, chapter 12 (Regression: Basics), chapter 13 (Regression: Troubleshooting)

  2. EZ, class slides on portfolio theory with no short sales.

  3. EZ, class slides on portfolio risk budgeting

  4. EZ class slides on statistical analysis of efficient portfolios.

  5. EZ class slides on the single index model.

  6. EZ class slides on estimating single index model using regression.

  7. R Cookbook, chapter 11 (Linear Regression and ANOVA)

  8. *EG, chapters 6, 7 and 9

  1. portfolioTheoryNoShortSalesPowerpoint.pdf

  2. portfolioTheoryNoShortSales.r

  3. portfolio_noshorts.r (R functions for portfolio analysis with short sales)

  4. testport.r (updated examples to include no short sales constraints)

  5. rollingPortfoliosPowerpoint.pdf (updated November 17, 2008)

  6. rollingPortfolios.r

  7. singleIndex.r

  8. singleIndexPrices.xls (added May 22, 2006)

  9. singleIndexPowerPoint.pdf

  10. tablet PC notes for lecture 16

  11. tablet PC notes for lecture 17

  12. tablet PC notes for lecture 18
9

Final Exam: Thursday, August 16, 2012, 4:40-6:50, Room Savery Hall 260

Final Project: Due Friday, August 17, 2012 by 5 pm