ECON 424/AMATH 462:  Computational Finance and Financial Econometrics

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Final Project: Fall 2014

Note: Please check this page for updates and corrections every few days. Additions will be dated.

Last updated: November 25, 2014

Selecting Data

For this project, everyone will be using the same data.

  1. S&P 500 index: vfinx

  2. European stock index: veurx

  3. Emerging markets fund: veiex

  4. Long-term bond fund: vbltx

  5. Short-term bond fund: vbisx

  6. 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.

Downloading Data

For the project you will analyze 5 years of monthly closing price data from the end of September 2009 through the end of September 2014.

The following R script file guides you through the creation of the necessary R objects for the analysis of the data in R

Organization of Results

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 etc.

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.

Formal Write-up

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:

  1. An executive summary, which gives a brief summary of the main results using bullet points

  2. 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 etc.

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 them.

Exerpts from an example class project: 424projectExample.pdf

Analysis

Return calculations and Sample Statistics 

  • 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 data? Also, create a boxplot showing the distributions of all of the assets in one graph.

  • Compute 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. Arrange these values nicely in a table. Which asset has the highest slope?

  • 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. 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 correlation matrix 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?  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?

 

Value-at-Risk Calculations

  • Assume that you have $100,000 to invest starting at September 30, 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.  Arrange these results nicely in a table. 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 table.

  • Use the bootstrap to compute estimated standard errors and 95% confidence intervals for your 5% VaR estimates. Create a table showing the 5% VaR estimation along with the bootstrap standard errors and 95% confidence intervals. 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.

Rolling Analysis of the CER Model Parameters (Added 11/25/14)

  • 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 of 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?

Portfolio Theory (Added 11/25/14)

Use all 6 assets and the descriptive statistics computed above for the following computations.

  • Compute the global minimum variance portfolio and calculate the expected return and SD of this portfolio. Are there any negative weights in the global minimum variance portfolio? 

    • Graph the weights of the 6 assets in this portfolio using a bar chart.

  • Annualize the the monthly mean and SD by multiplying the mean by 12 and the SD by the square root of 12. Briefly comment on these values relative to those for each asset.

  • Assume that you have $100,000 to invest starting at September 30, 2014. For the global minimum variance portfolio, determine the 1% and 5% value-at-risk of the $100,000 investment over a one month investment horizon. Remember that returns are continuously compounded, so you have to convert the 1% and 5% quantiles to simple returns (see the example in the lecture notes on Introduction to Portfolio Theory). Compare this value to the VaR values for the individual assets.

  • Compute the global minimum variance portfolio with the added restriction that short-sales are not allowed, and calculate the expected return and SD of this portfolio.  

    • Graph the weights of the 6 assets in this portfolio. 

    • Annualize the the monthly estimates by multiplying the ER by 12 and the SD by the square root of 12. Compare this portfolio with the global minimum variance portfolio that allows short-sales. 

  • Assume that you have $100,000 to invest for a year starting at September 30. For the global minimum variance portfolio with short-sales not allowed, determine the 1% and 5% value-at-risk of the $100,000 investment over a one month investment horizon. Compare your results with those for the global minimum variance that allows short sales.

  • Using the estimated means, variances and covariances computed earlier, compute and plot the efficient portfolio frontier, allowing for short sales, for the 6 risky assets using the Markowitz algorithm. That is, compute the Markowitz bullet. Recall, to do this you only need to find two efficient portfolios and then every other efficient portfolio is a convex combination of the two efficient portfolios. Use the global minimum variance portfolio as one efficient portfolio. For the second efficient portfolio, compute the efficient minimum variance portfolio with a target return equal to the maximum of the average returns for the six assets (see example from lecture notes). 

    • Create a plot (based on monthly frequency) with portfolio expected return on the vertical axis and portfolio standard deviation on the horizontal axis showing the efficient portfolios. Indicate the location of the global minimum variance portfolio (with short sales allowed) as well as the locations of your six assets.

  • Compute the tangency portfolio using a monthly risk free rate equal to 0.0004167) per month (which corresponds to an annual rate of 0.5%). recall, we need the risk free rate to be smaller than the average return on the global minimum variance portfolio in order to get a nice graph.

    • Graph the weights of the 6 assets in this portfolio. In the tangency portfolio, are any of the weights on the 6 funds negative? If so, interpret the negative weights.

    • Compute the expected return, variance and standard deviation of the tangency portfolio. 

    • Give the value of Sharpe's slope for each asset as well as for the tangency portfolio. Which asset has the highest Sharpe's slope?

    • Show the tangency portfolio as well as combinations of T-bills and the tangency portfolio on a plot with the Markowitz bullet. That is, compute the efficient portfolios consisting of T-bills and risky assets.

    • Annualize the the monthly ER and SD of the tangency portfolio by multiplying the ER by 12 and the SD by the square root of 12. Briefly comment.

  • Compute and plot the efficient portfolio frontier this time not allowing for short sales, for the 6 risky assets using the Markowitz algorithm. Recall, to do this you need to create a grid of target return values, between the mean of the no short sales global minimum variance portfolio and the mean of the asset with the highest average return, and solve the Markowitz algorithm with the no short sales restriction.   

    • Compare the no short sale frontier with the frontier allowing short sales (try to plot them on the same graph)

    • Consider a portfolio with a target volatility of 0.02 per month. What is the approximate cost in expected return of investing in a no short sale efficient portfolio versus a short sale efficient portfolio?

  • Using a monthly risk free rate equal to 0.0004167 per month and the estimated means, variances and covariances compute the tangency portfolio imposing the additional restriction that short-sales are not allowed. 

    • Compute the expected return, variance and standard deviation of the tangency portfolio. 

    • Give the value of Sharpe's slope for the no-short sales tangency portfolio. 

    • Annualize the the monthly ER and SD of the tangency portfolio by multiplying the ER by 12 and the SD by the square root of 12. Briefly comment.

    • Compare this tangency portfolio with the tangency portfolio where short-sales are allowed.

Asset Allocation (Added 11/25/14)

  • Suppose you wanted to achieve a target expected return of 6% per year (which corresponds to an expected return of 0.5% per month) using only the risky assets (6 Vanguard portfolios) and no short sales. Recall, you cannot short a mutual fund. What is the efficient portfolio that achieves this target return? How much is invested in each of the Vanguard funds in this efficient portfolio?

  • Compute the monthly SD on this efficient portfolio, as well as the monthly 1% and 5% value-at-risk based on an initial $100,000 investment.

  • Now Suppose you wanted to achieve a target expected return of 12% per year (which corresponds to an expected return of 1% per month) using only the risky assets (6 Vanguard portfolios) and no short sales. What is the efficient portfolio that achieves this target return? How much is invested in each of the Vanguard funds in this efficient portfolio? How does this portfolio differ from the efficient portfolio with a 6% annual target?

  • Compute the monthly SD on this efficient portfolio, as well as the monthly 1% and 5% value-at-risk based on an initial $100,000 investment.