July 15, 2014
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 May 2009 through
the end of May 2014.
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
Turn in the formal
write-up, as well as a print-out of your Excel spreadsheets
and R output, in a bound folder.
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 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 (This is called an "equity curve"). Which fund gives the highest future value?
Are you surprised?
Create four panel
diagnostic plots containing histograms, smoothed density plots,
boxplots and qq-plots for each return
series and comment. Do
the returns look normally distributed? Are there any outliers in the
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 for each asset. Which asset has the highest
Compute estimated standard errors
and form 95% confidence intervals for
the the estimates of the mean and standard deviation. 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. Assuming
you get the average annual return every year for 5 years, how much
would $1 grow to after 5 years?
Compute and plot all pair-wise
scatterplots between your 6 assets. Briefly comment
on any relationships you see.
Compute the sample covariance matrix
of the returns on your six assets and comment on
the direction of linear association between the asset returns.
Compute the sample
of the returns on your six assets. Which assets are most highly
correlated? Which are least correlated?
Compute estimated standard errors and 95% confidence
intervals for your estimates. How precise are these correlation
estimates? Finally, based on the estimated correlation values do you
think diversification will reduce risk with these assets?
Assume that you have $100,000 to invest
starting at May 31, 2014. 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.
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
Use the bootstrap to compute estimated
standard errors and 95% confidence intervals for your 5% VaR estimates.
Using these results, comment on the precision of your VaR estimates.
Repeat the VaR
analysis (but skip the bootstrapping), 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? Note: it may not seem obvious how to
annualize the empirical quantile.