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Eric Zivot
Economics 483
Introduction to Computational Finance and Financial Econometrics

Class Syllabus

Fall 2004

Note 1: In the Reading column below, R denotes "Ruppert", EZ denotes "Eric Zivot", EG denotes "Elton and Gruber", "KO" denotes "Krause and Olson", and "DS" denotes David Smith. "*" denotes optional reading.

Note 2: Recent changes to the reading list are indicated in red.

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. 

Note 4: The spreadsheets I post are created using Excel 2000. These spreadsheets may not load in earlier versions of Excel. The spreadsheets with filenames filename_ver7.xls will load in Excel version 7 (Excel 95). The spreadsheets from the first edition of Financial Modeling are created in Excel version 7. Those in this second edition are created in a later version.

Last updated on  December 1,  2004

Course Outline by Week (subject to change)

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 S-PLUS

  1. R, chapter 3, sections 1 and 6.

  2. EZ, Lecture notes on return calculations.

  3. EZ, class slides on return calculations.

  4. *EG, chapters 1-3

  5. KO. Skim chapters 1-2; Go through the tuturial in chapter 3.

  6. Skim the S-PLUS Quick Start Guide. From within S-PLUS, see Help/Online Manuals.

  7. S-PLUS tutorial outline.

  8. DS, Skim chapters 1 - 3.

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

  2. returnCalculations.xls

  3. DowJonesReturns.xls

  4. SPlusTutorial.ssc

 
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

  1. R, chapter 2, sections 1-17; chapter 3, sections 2-5; chapter 4, sections 1-2; chapter 11, sections 1-2.

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

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

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

  5. EZ, class slides on time series concepts.

  6. KO. Skim chapters 1-2; Go through the tuturial in chapter 3.

  7. Skim the S-PLUS Quick Start Guide. From within S-PLUS, see Help/Online Manuals.

  8. DS, Skim chapters 1 - 3.

  1. probReview.xls

  2. timeSeriesConceptsPowerPoint.pdf

  3. timeSeriesConcepts.ssc
4

  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

  1. R, chapter 2, sections 18-20; chapter 10, sections 1-2.

  2. EZ, class slides on descriptive statistics.

  3. EZ, class slides on CER model.

  4. EZ, class slides on bootstrapping

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

  1. descriptiveStatisticsPowerPoint.pdf

  2. descriptiveStatistics.ssc

  3. cermodel.xls

  4. cerModelExamples.ssc

  5. bootStrap.ssc
5 & 6

  1. Introduction to portfolio theory

  2. Optimization

  3. Midterm Exam (Monday November 8)

  4. Midterm solutions.

  5. Matrix algebra

  6. Markowitz algorithm

  7. Markowitz Algorithm using the solver and matrix algebra

 

  1. R, chapter 5; chapter 10, section 3; chapter 11, section 3.

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

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

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

  5. EZ, class slides on matrix algebra.

  6. Notes on using Excel's solver.

  7. EZ, class slides on Markowitz algorithm.

  8. *EG, chapters 5 and 6

 

  1. introPortfolioTheory.xls

  2. 483solverex.xls

  3. 3firmExample.xls

  4. portfolio.ssc (S-PLUS functions for portfolio analysis with short sales)

  5. matrixReview.ssc (S-PLUS example of simple matrix algebra)
7 & 8

  1. Beta as a measure of portfolio risk

  2. The Single Index Model

  3. Estimating the Single Index Model using simple linear regression

  1. R, chapter 6, sections 1-4, 12.

  2. EZ class slides on the single index model.

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

  4. EZ class slides on hypothesis testing in SI model.

  5. *EG, chapters 6, 7 and 9

  1. single index class example.xls

  2. singleIndex.ssc

  3. singleIndexPowerPoint.pdf

  4. hypothesisTesting.ssc

  5. rolling.ssc

  6. siPortfolio.ssc
9 & 10
  1. The Capital Asset Pricing Model (CAPM)
  2. Testing the CAPM
  3. Course review
  1. R, chapter 7, sections 1-7, 10.
  2. EZ class slides on the single index model and CAPM
  3. *EG, chapters 13 and 15.
  1. tangency.ssc
  2. testCAPM.ssc
10

Final Exam: Tuesday 12/14/04, 8:30 - 10:20, SAV 216

Final Project: Due Friday 12/17/04 by 5 pm (in my mail box or my office)