May 16, 2015
For this project, everyone will be
using the same data.
S&P 500 index: vfinx
European stock index: veurx
Emerging markets fund: veiex
Long-term bond fund: vbltx
Short-term bond fund: vbisx
Pacific stock index: vpacx
Information on these funds is available
on the Yahoo! finance site. After typing in the sticker symbol and
retrieving the quote data, choose Profile to get a summary of the
fund. Please review each fund before doing any of the analysis below.
For the project you
will analyze 5 years of monthly
closing price data from the
end of March 2010 through
the end of March 2015.
The following R script file guides
you through the creation of the necessary R objects for the analysis of
the data in R
As in the homework assignments,
summarize your R work in a Word file. You will find it helpful to organize
your Excel results in a spreadsheet by task. That is, put all of the data in one
worksheet tab, put all the graphs in another, put the portfolio analysis
in another tab, etc. This will make it easier for you to print out
results. It is also helpful to use
names for your data and for certain results. This makes working with
formulas much easier and it also helps to eliminate errors in formulas
You will find it helpful to add text boxes in
your spreadsheet to organize comments etc.
Remember to save your work often as
Excel has a tendency to crash with large complicated spreadsheets. Also,
keep a back-up copy of your project.
I want you to give a formal write-up,
separate from the Excel spreadsheet analysis
and R statistical analysis. Treat this
write-up as a term-paper project. Typically, the
write-up is between 10 and 20 pages (double spaced with graphs and
tables). Your write up should consist of:
summary, which gives a brief summary of the main results using bullet
Sections that summarize the
results of your statistical analysis by topic (see below).
You may find it helpful to include parts of your spreadsheet and computer
output as part of your write-up. Alternatively, you
can refer to your spreadsheets for the quantitative results, graphs
You only need to turn in the formal
write-up. Turning in print-outs of your Excel spreadsheets
and R output is optional.
I have boxes of
projects from previous classes. Feel free to come by my office to look at
Exerpts from an
example class project:
Return calculations and Sample
Compute time plots of monthly
prices and continuously compounded returns
and comment. Are there any unusually large or small returns? Can you
identify any news events that may explain these unusual values? Give a
plot showing the growth of $1 in each of the funds over the five year
period (recall, this is called an "equity curve"). Which fund gives the highest future value?
Are you surprised?
Create four panel
diagnostic plots containing histograms,
boxplots, qq-plots, and SACFs for each return
series and comment. Do
the returns look normally distributed? Are there any outliers in the
data? Is there any evidence of linear time dependence? Also, create a boxplot showing the distributions of all of the
assets in one graph.
univariate descriptive statistics (mean,
variance, standard deviation, skewness, kurtosis, quantiles) for each return series and comment.
Which funds have the highest and lowest average return? Which funds
have the highest and lowest standard deviation? Which funds look most
and least normally distributed?
Using a monthly
risk free rate equal to
0.0004167 per month (which corresponds to a continuously
compounded annual rate of 0.5%),
compute Sharpe's slope/ratio for each asset. Use
the boostrap to calculate estimated standard errors for the Sharpe
these values nicely in a table. Which asset has the highest
slope? Are the Sharpe slopes estimated precisely?
Compute estimated standard errors
and form 95% confidence intervals for
the the estimates of the mean and standard deviation. Arrange these
values nicely in a table. Are these means
and standard deviations estimated very precisely? Which estimates are
more precise: the estimated means or standard deviations?
Convert the monthly sample means
into annual estimates by multiplying by 12 and convert the
monthly sample SDs into annual estimates by multiplying by the square
root of 12. Comment on the values of these annual numbers. Using these
values, compute annualized Sharpe ratios. Are the asset rankings the
same as with the monthly Sharpe ratios? Assuming
you get the average annual return every year for 5 years, how much
would $1 grow to after 5 years? (Remember, the annual return you
compute is a cc annual return).
Compute and plot all pair-wise
scatterplots between your 6 assets. Briefly comment
on any relationships you see.
Compute the sample
of the returns on your six assets and plot this correlation matrix
using the R corrplot package function corrplot(). Which assets are most highly
correlated? Which are least correlated?
Based on the estimated correlation values do you
think diversification will reduce risk with these assets?
Assume that you have $100,000 to invest
March 31, 2015. For each asset, determine the 1% and 5%
value-at-risk of the $100,000 investment over a one month investment
horizon based on the normal distribution using the estimated means and variances of your assets.
Use the bootstrap to compute estimated
standard errors and 95% confidence intervals for your 1% and 5% VaR estimates.
Create a table showing the 1% and 5% VaR estimates along with the bootstrap
standard errors and 95% confidence intervals. Using these results, comment on the precision of your VaR estimates. Which assets have the highest and lowest VaR at each horizon?
Using the monthly mean and standard deviation estimates, compute the
annualized mean (12 time monthly mean) and standard deviation (square root
of 12 time monthly std dev) and determine the 1% and 5% value-at-risk
of the $100,000 investment over a one year
investment horizon. Arrange these results nicely in a
Repeat the VaR
analysis (but skip the bootstrapping and the annualized VaR calculation), but this time use the empirical 1% and 5% quantiles of the return
distributions (which do not assume a normal distribution - this method is
often called historical simulation). How different are the results from
those based on the normal distribution?
Rolling Analysis of the
CER Model Parameters
For each asset, compute 24 month
rolling estimates of the mean and
standard deviation of the continuously compounded returns using the
R function rollapply() (see the script file
for lab 5 for examples). For each asset, graph these rolling estimates
together with the returns (so that you have just one graph for each
asset). Briefly comment on the stability of the mean and SD parameters
the constant expected return model.
With 6 assets there are 6*5/2 = 15
pairwise correlations. To keep things simple,
compute and plot 24 month rolling estimates of the sample correlation
between the S&P 500 index (vfinx) and the long-term bond index
(vbltx). Is this correlation stable over time? When is the correlation
the highest and when is it the lowest?