ECON 424/AMATH
462: Computational Finance and Financial Econometrics

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Class Syllabus
Summer 2014 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
July 8,
2014 
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,
Lecture notes on return calculations.

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

msftPrices.csv, sbuxPrices.csv

tablet PC notes for
lecture 1 (return calculations)

tablet PC notes for
lecture 2 (cc return calculations)

returnCalculationsPowerpoint.pdf

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

probReview.xls

probReview.r

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

EZ,
Lecture notes on time series
concepts.

EZ, Lecture notes on review of matrix algebra.

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

probabilityReviewPowerPoint.pdf

timeSeriesConceptsPowerPoint.pdf

timeSeriesConcepts.r

matrixReviewPowerpoint.pdf

matrixReview.r

tablet PC notes
for
lecture 3

tablet PC notes for
lecture 4

tablet PC notes for
lecture 5

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: Tentatively Tuesday
July 22nd from 1:10  3:20. Rooms TBD


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

EZ,
lecture notes on descriptive statistics.

EZ,
class
slides on descriptive statistics.

EZ, class
slides on CER model.

EZ,
lecture notes on the 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.


descriptiveStatisticsPowerPoint.pdf

descriptiveStatistics.r

descriptiveStatisticsDailyPowerPoint.pdf

descriptiveStatisticDaily.r

cerExample.csv

cerModelExamples.r

cerModelPowerPoint.pdf.

bootStrapPowerPoint.pdf

bootStrap.r

hypothesisTestingCERpowerpoint.pdf

hypothesisTestingCER.r

maximumLikelihoodPowerpoint.pdf

maximumLikelihood.r

maxLike
R package vignette.

tablet PC notes for
lecture 6

tablet PC notes for
lecture 7

tablet PC notes for lecture 8

67 

Midterm grade distribution

Introduction to portfolio theory

Optimization

Markowitz algorithm

Markowitz Algorithm using the solver and matrix algebra

Risk budgeting


Ruppert, chapter 11 (Portfolio Theory).

EZ,
lecture
notes on introduction to portfolio theory.

Notes on using Excel's
solver.

EZ,
class
slides on Introduction to Portfolio Theory.

EZ,
class
slides on portfolio theory with matrix algebra.

EZ,
lecture notes 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

introductionPortfolioTheoryPowerpoint.pdf

portfolioTheoryMatrixPowerpoint.pdf

portfolioTheoryMatrix.r

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

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

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

portfolioFunctionPowerPoint.pdf

tablet PC notes for
lecture 9

tablet PC notes for
lecture 10

tablet PC notes for
lecture 11

tablet PC notes for
lecture 12

tablet PC notes for
lecture 13

tablet PC notes for
lecture 14

tablet PC notes for
lecture 15

portfolioTheoryRpowerPoint.pdf. (updated November 12, 2008)

8 & 9 

Statistical
Analysis of Efficient Portfolios

Beta as a measure of portfolio risk

The Single Index Model

Estimating the Single Index Model using simple linear regression

Capital Asset Pricing Model (CAPM)


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

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

EZ,
class slides on portfolio risk
budgeting

EZ
class slides on statistical
properties of efficient portfolios.

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

R Cookbook, chapter 11 (Linear
Regression and ANOVA)

*EG, chapters 6, 7 and 9


portfolioTheoryNoShortSalesPowerpoint.pdf

portfolioTheoryNoShortSales.r

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

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

rollingPortfoliosPowerpoint.pdf (updated
August 20, 2013)

rollingPortfolios.r

bootstrapPortfoliosPowerpoint.pdf
(updated August 20, 2013)

bootstrapPortfolio.R

singleIndex.r

singleIndexPrices.xls (added May 22,
2006)

singleIndexPowerPoint.pdf

CAPMPowerPoint.pdf

testCAPM.r

tablet PC notes for
lecture 16

tablet PC notes for
lecture 17

tablet PC notes for
lecture 18

9 
Final Exam: Thursday,
August 21, 1:103:20,
Rooms LOW 202 and LOW 206
Final Project: Due Friday, August 22 by 5
pm 





