ECON 424/CFRM
462: Computational Finance and Financial Econometrics

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Class Syllabus
Winter 2016 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 with R 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 and Matteson .
"*" denotes optional reading.
Note 2: Recent changes to the
reading list are denoted with
.
Note 3: My Book chapters are work
in progress and are not guaranteed to be free of errors. The book manuscript
is posted on the class Canvas syllabus page. As the quarter progresses I will be making changes
and additions to the chapters 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
January 11,
2016 
Week 
Topic 
Reading 
Additional
Material 
1 
Course Introduction
Computing Asset Returns
Getting financial data from Yahoo!
Excel
calculations
Introduction to
R

Univariate random variables and distributions

Ruppert,
chapter 2 (Returns).

EZ,
chapter 1 (return calculations).

EZ,
chapter 2 (Review of Random Variables).
EZ,
class slides on course introduction.

EZ,
class
slides on return calculations.

EZ,
class slides on probability review: Part I.

ZLM, chapters 13, 5.

R Cookbook, chapters 1  5, 10 (sections
1  15)
An
Introduction to R, sections 13, 6 and 7.
R
for Beginners, sections 13.

*EG, chapters 13

finance.yahoo.com Check
out finance/quote section
returnCalculations.xls

returnCalculations.r (R code for book chapter examples)

returnCalculations.Rmd
(R markdown file for webpage examples)

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

Rintro.pdf
(introduction to R covered)

probReview.xls

probReview.r
(R code for book chapter examples)

2
& 3 

Characteristics of distributions

The normal distribution

Linear function of random variables

Quantiles of a distribution, ValueatRisk

Bivariate distributions

Covariance, correlation, autocorrelation

Linear combinations of random variables

Time Series concepts

Matrix algebra


Ruppert, chapter 5 (Modeling Univariate
Distributions),
chapter 7 (Multivariate Statistical Models),
chapter 9
(Time Series Models: Basics),
Appendix (sections 110, 1215, 20)

EZ,
chapter 2 (Review
of random variables), chapter 3 (matrix algebra review) and chapter 4
(time series concepts)

EZ,
class slides on probability review: Part I.

EZ,
class slides
on probability review: Part II.

EZ,
class slides on time series
concepts.

EZ,
class
slides on matrix algebra.

ZLM, chapters 37.

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

An
Introduction to R, section 8.

R for Beginners, section 4.


probReview.xls

probReview.r
(R code for book chapter examples)

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

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

timeSeriesConcepts.r (R code for
book chapter examples)

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

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

matrixReview.r
(R code for book chapter examples)

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

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

Working
with time series data in R

45 

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

The constant expected return model.

Monte Carlo simulation

Standard errors of estimates

Confidence intervals

Bootstrapping
standard errors and confidence intervals

Hypothesis
testing

Midterm
exam:

Midterm solutions

Grade
distribution econ 424


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

EZ, chapter 5 (descriptive statistics
for financial data), chapter 6 (constant expected return model) ,
chapter 7 (estimation of the CER model), chapter 8 (bootstrapping),
chapter 9 (hypothesis testing in the CER model)

EZ,
class
slides on descriptive statistics.

EZ, class
slides on CER model.

EZ,
class
slides on bootstrapping

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

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

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

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

An
Introduction to R, section 12.


descriptiveStatistics.r
(R code for book chapter examples)

descriptiveStatisticDaily.r

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

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

cerModel.r
(R code for book chapter examples)

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

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

cerModelEstimation.r (R code for book chapter examples)

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

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

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

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

bootStrap.r
(R code examples)

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

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

hypothesisTestingCER.r

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

hypothesisTestingCerModel.html (R examples in webpage created with
R markdown  best viewed in Chrome)

maximumLikelihood.r

maxLike
R package vignette.

67 

Introduction to portfolio theory

Optimization

Markowitz algorithm

Markowitz algorithm using the solver and matrix algebra

Markowitz algorithm with no short sales
constraints


Ruppert, chapter 11 (Portfolio Theory).

EZ,
chapter 10 (introduction to portfolio theory), and chapter 11
(portfolio theory with matrix algebra)

Notes on using Excel's
solver.

EZ,
class
slides on Introduction to Portfolio Theory.

EZ,
class
slides on portfolio theory with matrix algebra.

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

*EG, chapters 5
and 6


introPortfolioTheory.xls

3firmExample.xls

introductionToPortfolioTheory.r

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

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

portfolioTheoryMatrix.r

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

portfolioTheoryMatrix.html
( R examples in webpage created with
R markdown  best viewed in Chrome)

8 & 9 

Portfolio risk budgeting

Statistical
Analysis of Efficient Portfolios

Beta as a measure of portfolio risk


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

EZ, chapter 12 (portfolio risk
budgeting), chapter 13 (statistical analysis of portfolios), and
chapter 14 (single index model)

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

EZ
class slides on statistical
properties of efficient portfolios.

EZ,
class slides on portfolio risk
budgeting

R Cookbook, chapter 11 (Linear
Regression and ANOVA)

*EG, chapters 6, 7 and 9


portfolioTheoryNoShortSales.r

portfolioTheoryNoShorts.html (R examples in webpage created
with R markdown  best viewed in Chrome)

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

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

statisticalAnalysisPortfolios.html (R examples in webpage
created with R markdown  best viewed in Chrome)

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

riskBudgeting.html (R examples in webpage created with R
markdown  best viewed in Chrome)

rollingPortfolios.r

bootstrapPortfolio.R

singleIndex.r

singleIndexPrices.xls (added May 22,
2006)

testCAPM.r

10 

The Single Index Model

Estimating the Single Index Model using simple linear regression

Capital Asset Pricing Model (CAPM)


EZ, chapter 14 (single index model) and
chapter 15 (CAPM)

EZ class
slides on the single index model.

EZ
class
slides on estimating single index model using regression.

EZ
class slides on the Capital Asset Pricing
Model


Finals week 
Final Exam: Tuesday March 15
Smith 304.
Final Project: Due Friday March 11 by 8
pm via Canvas 

