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Last updated:
November 4, 2009
The final exam will cover all the material from the course syllabus. The final exam will be
concept oriented but there will also be some calculation involved so bring a calculator. I
will not ask you to do proofs or long derivations. The best place to start your studying
is with the homework assignments. Solutions for the homework assignments are on the
homework page. Check the Notes page for
updates of the class lecture notes.
The exams will be closed book and closed note exam. However, I will
allow one page (double sided) of handwritten or typed notes
Topics to be covered for the Midterm
- Return calculations
- simple and continuously compounded returns
- time aggregation
- Review of random variables
- Shape characteristics (mean, variance,
skewness, kurtosis)
- quantiles
- normal distribution
- linear functions of random variables
- covariance and correlation
- characteristics of portfolios with risky
assets
- Matrix Algebra
- Compute portfolio expected return and variance
using matrix algebra
- Time Series Concepts
- covariance stationarity
- Autocorrelations
- MA(1) and AR(1) models
- Descriptive statistics
- histograms, boxplots, qq-plots
- sample statistics
- Constant expected return model
- model assumptions and interpretation
- relationship to random walk model
- estimation of parameters
- properties of estimators - bias and precision
- estimating standard errors - analytic
formulas and the bootstrap
- confidence intervals
- bootstrapping
Topics to be emphasized on
the Final Exam
Introduction to portfolio theory
- characteristics of portfolios with risky and
riskless assets
- portfolio frontier
- efficient portfolios
- global minimum and tangency portfolios
Portfolio Theory with Matrix
Algebra
- Express Markowitz algorithm for finding efficient
portfolios using matrix algebra
Single Index Model
- Can you interpret the single index regression: Ri
= ai + bi*Rm
+ ei ?
- How do you compute the covariance matrix using the single index
model?
- Estimating beta from observed return data
- How do you use least square regression?
- How do you interpret R-square from the regression?
- How do you interpret the standard error of the regression?
- How are regression results for portfolios different than
regression results for individual
assets?
- How do you compute the beta for a portfolio?
- How do you interpret beta as a measure of portfolio risk? That
is, what does it mean for an asset to have a beta less than 1, a
beta greater than 1 and a beta equal to 1?
- Suppose you compute the beta
of an asset with respect to the tangency portfolio. What is the
relationship between the expected return on the asset and the beta
with respect to the tangency portfolio?
CAPM
- How is the CAPM related to
portfolio theory?
- What are the assumptions behind the CAPM?
- What are the main implications of the CAPM?
Final Exams
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