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# Class Syllabus

Spring 2015

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 book chapters of Introduction to Computational Finance and Financial Econometrics by Eric Zivot; "EG" refers to Modern Portfolio Theory by Elton and Gruber; "Ruppert" refers to Statistics and Data Analysis for Financial Engineering by Ruppert . "*" denotes optional reading.

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

Note 3: My Book chapters 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  May 18,  2015

1
1. Course Introduction

2. Computing Asset Returns

3. Getting financial data from Yahoo!

4. Excel calculations

5. Introduction to R

6. Univariate random variables and distributions

1. Ruppert, chapter 2 (Returns).

2. EZ, Book chapter on return calculations.

3. EZ, Book chapter on review of univariate random variables and probability.

4. EZ, class slides on course introduction.

5. EZ, class slides on return calculations.

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

7. ZLM, chapters 1-3, 5.

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

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

10. R for Beginners, sections 1-3.

11. *EG, chapters 1-3

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

2. returnCalculations.xls

3. returnCalculations.r (R code for book chapter examples)

4. returnCalculationsPowerpoint.pdf (old powerpoint examples)

5. returnCalculations.Rmd (R markdown file for web-page examples)

6. returnCalculations.html (R examples in web page using R markdown - best viewed in Chrome)

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

8. probReview.r (R code for book chapter examples)

2 & 3
1. Characteristics of distributions

2. The normal distribution

3. Linear function of random variables

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

5. Bivariate distributions

6. Covariance, correlation, autocorrelation

7. Linear combinations of random variables

8. Time Series concepts

9. 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, Book chapter on review of univariate random variables and probability.

3. EZ, Book chapter on time series concepts. (updated January 22 2015)

4. EZ, Book chapter 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 (R code for book chapter examples)

3. probabilityReviewPowerPoint.pdf (old powerpoint examples)

4. probabilityReview.Rmd (R markdown file used to create webpage examples)

5. probabilityReview.html (R examples in webpage created with R markdown)

6. timeSeriesConceptsPowerPoint.pdf (old powerpoint examples)

7. timeSeriesConcepts.r (R code for book chapter examples)

8. timeSeriesConcepts.Rmd (R markdown file used to create webpage examples)

9. timeSeriesConcepts.html (R examples in webpage created with R markdown - best viewed in Chrome)

10. matrixReviewPowerpoint.pdf (old powerpoint examples)

11. matrixReview.r (R code for book chapter examples)

12. matrixReview.Rmd (R markdown file used to create webpage examples)

13. matrixReview.html (R examples in webpage created with R markdown - best viewed in Chrome)

14. 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. Midterm exam: Thursday May 7th

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, Book chapter on descriptive statistics.

3. EZ, class slides on descriptive statistics.

4. EZ, class slides on CER model.

5. EZ, Book chapter on the CER model. Updated February 23, 2015

6. EZ, Book chapter on estimation of the CER model. Updated February 23, 2015

7. EZ, class slides on bootstrapping

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

9. EZ, class slides on maximum likelihood estimation. Note: will not cover this material this term.

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

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

12. An Introduction to R, section 12.

1. descriptiveStatisticsPowerPoint.pdf (old powerpoint examples)

2. descriptiveStatistics.r (R code for book chapter examples)

3. descriptiveStatisticsDailyPowerPoint.pdf (old powerpoint examples)

4. descriptiveStatisticDaily.r

5. descriptiveStatistics.Rmd (R markdown file used to create webpage examples)

6. descriptiveStatistics.html (R examples in webpage created with R markdown)

7. cerModel.r (R code for book chapter examples)

8. cerModelPowerPoint.pdf. (old powerpoint examples)

9. cerModel.Rmd (R markdown file used to create webpage examples)

10. cerModel.html (R examples in webpage created with R markdown - best viewed in Chrome)

11. cerModelEstimation.r (R code for book chapter examples)

12. cerModelEstimation.Rmd (R markdown file used to create webpage examples)

13. cerModelEstimation.html (R examples in webpage created with R markdown - best viewed in Chrome)

14. bootstrap.Rmd (R markdown file used to create webpage examples)

15. bootstrap.html (R examples in webpage created with R markdown - best viewed in Chrome)

16. bootStrapPowerPoint.pdf (old powerpoint examples)

17. bootStrap.r (R code examples)

18. rollingCerModel.Rmd (R markdown file used to create webpage examples)

19. rollingCerModel.html (R examples in webpage created with R markdown - best viewed in Chrome)

20. hypothesisTestingCERpowerpoint.pdf (old powerpoint examples)

21. hypothesisTestingCER.r

22. maxLike R package vignette.

6-7
1. Introduction to portfolio theory

2. Optimization

3. Markowitz algorithm

4. Markowitz Algorithm using the solver and matrix algebra

5. Risk budgeting

1. Ruppert, chapter 11 (Portfolio Theory).

2. EZ, Book chapter 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, Book chapter on portfolio theory with matrix algebra.

7. EZ, class slides on portfolio risk budgeting

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

9. *EG, chapters 5 and 6

1. introPortfolioTheory.xls

2. 3firmExample.xls

3. introductionPortfolioTheoryPowerpoint.pdf (old powerpoint examples)

4. introductionToPortfolioTheory.Rmd (R markdown file used to create webpage examples)

5. introductionToPortfolioTheory.html (R examples in webpage created with R markdown - best viewed in Chrome)

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

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

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

9. tablet PC notes for lecture 9

10. tablet PC notes for lecture 10

11. tablet PC notes for lecture 11

12. tablet PC notes for lecture 12

13. tablet PC notes for lecture 13

14. tablet PC notes for lecture 14

15. tablet PC notes for lecture 15

16. 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

5. Capital Asset Pricing Model (CAPM)

1. Ruppert, chapter 12 (Regression: Basics), chapter 13 (Regression: Troubleshooting), chapter 16 (CAPM)

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

3. EZ class slides on statistical properties of efficient portfolios.

4. EZ class slides on the single index model.

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

6. EZ class slides on the Capital Asset Pricing Model

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

8. *EG, chapters 6, 7 and 9

1. (R functions for portfolio analysis with short sales)

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

3. rollingPortfoliosPowerpoint.pdf (updated August 20, 2013)

4. rollingPortfolios.r

5. bootstrapPortfoliosPowerpoint.pdf (updated August 20, 2013)

6. singleIndex.r

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

8. singleIndexPowerPoint.pdf

9. tablet PC notes for lecture 16

10. tablet PC notes for lecture 17

11. tablet PC notes for lecture 18

9

Final Exam: Tuesday June 8 from 10:30-12:20 in SAV 264.

Final Project: Due Friday June 5 by 8 pm via Canvas or hardcopy in my mailbox or in my office (until 5 pm only)