University of Washington
Department of Economics

Summer 2012
Eric Zivot
Econ 424/Amath 540
Midterm Exam Solutions

This is a closed book and closed note exam. However, you are allowed one page of notes(8.5” by 11” double-sided) and the use of a calculator. Answer all questions and write allanswers in a blue book or on separate sheets of paper. Time limit is 1 hours and 50
minutes. Total points = 104.

I. Return Calculations (20 pts, 4 points each)
Consider a 1-month investment in two assets: the Vanguard S&P 500 index (VFINX) andthe Vanguard Emerging Markets Stock Index (VEIEX). Suppose you buy one share ofthe S&P 500 fund and one share of the emerging markets fund at the end of June, 2010
for P vfinx ,t 1  105.06, Pveiex ,t 1  28.64 , and then sell these shares at the end of July, 2010
for P vfinx ,t  109.04, Pveiex ,t  29.49 . (Note: these are actual adjusted closing prices takenfrom Yahoo!)a. What are the simple 1-month returns for the two investments?
> p.vfinx.1 = 105.06> p.vfinx.2 = 109.04> p.veiex.1 = 28.64> p.veiex.2 = 29.49> r.vfinx = (p.vfinx.2 - p.vfinx.1)/p.vfinx.1> r.veiex = (p.veiex.2 - p.veiex.1)/p.veiex.1> r.vfinx[1] 0.03788> r.veiex[1] 0.02968

b. What are the continuously compounded (cc) 1-month returns for the two investments?
> log(1 + r.vfinx)[1] 0.03718> log(1 + r.veiex)[1] 0.02925

c. Assume you get the same monthly returns from part a. and b. every month for the nextyear. What are the annualized simple and cc returns?
> r.vfinx.a = (1 + r.vfinx)^12 - 1> r.veiex.a = (1 + r.veiex)^12 - 1> r.vfinx.a[1] 0.5624> r.veiex.a[1] 0.4204

d. Continuing with c., how much will $10,000 invested in each fund be worth after 1year?
> w0 = 10000> w1.vfinx = w0*(1 + r.vfinx.a)> w1.veiex = w0*(1 + r.veiex.a)> w1.vfinx[1] 15624> w1.veiex[1] 14204

e. At the end of June, 2011, suppose you have $10,000 to invest in VFINX and VEIEX
over the next month. Suppose you purchase $2,000 worth of VFINX and the remainder inVEIEX. What are the portfolio weights in the two assets? Using the results from parts a.
and b. compute the 1-month simple and cc portfolio returns.
> w0 = 10000> x.vfinx = 2000/w0> x.veiex = 1 - x.vfinx> x.vfinx[1] 0.2> x.veiex[1] 0.8> r.p = x.vfinx*r.vfinx + x.veiex*r.veiex> r.p[1] 0.03132> log(1 + r.p)[1] 0.03084

II. Probability Theory (20 points, 4 points each)
Let rvfinx and rveiex denote the monthly continuously compounded returns on VFINX andVEIEX and suppose that rvfinx ~ iid N (0.001, (0.05) 2 ), rveiex ~ iid N (0.01, (0.09) 2 ).

a. Sketch the normal distributions for the two assets on the same graph. Show the meanvalues and the ranges mean ± 2 sd. Which asset appears to be the most risky?
VEIEX has the larger sd and appears to be riskier.

b. Plot the risk-return tradeoff for the two assets. That is, plot the mean values of eachasset on the y-axis and plot the sd values on the x-axis. What relationship do you see?

Here we see a positive relationship between risk and return.

c. Let W0 = $100,000 be the initial wealth invested in each asset. Compute the 5%Zmonthly Value-at-Risk for each asset. (Hint: q0.05
 1.645 )> mu.vfinx = 0.001> mu.veiex = 0.01
> sigma.vfinx = 0.05
> sigma.veiex = 0.09
> w0 = 100000> q.vfinx = mu.vfinx + sigma.vfinx*(-1.645)> q.veiex = mu.veiex + sigma.veiex*(-1.645)> VaR.05.vfinx = w0*(exp(q.vfinx) - 1)
> VaR.05.veiex = w0*(exp(q.veiex) - 1)
> VaR.05.vfinx[1] -7804> VaR.05.veiex[1] -12894

d. Give an expression for the 12-month (annual) continuously compounded return, rt(12),in terms of the monthly continuously compounded returns. Using this expression
compute E[ rt (12)], and SD(rt (12)) for each asset.

rt ~ iid N (  ,  2 )
rt (12)  rt  rt 1    rt 11E  rt (12)   E  rt  rt 1    rt 11   E[rt ]  E[rt 1 ]    E[rt 11 ]  12

var(rt (12))  var(rt  rt 1    rt 11 )  var(rt )  var(rt 1 )    var(rt 11 )  12 2SD (rt (12)  12
Using the above formulas, we have
> mu.vfinx.a = 12*mu.vfinx> sigma.vfinx.a = sqrt(12)*sigma.vfinx> mu.vfinx.a[1] 0.012> sigma.vfinx.a[1] 0.1732> mu.veiex.a = 12*mu.veiex> sigma.veiex.a = sqrt(12)*sigma.veiex> mu.veiex.a[1] 0.12> sigma.veiex.a[1] 0.3118

e. Continuing with d., let W0 = $100,000 be the initial wealth invested in each asset.
Compute the 5% annual Value-at-Risk for each asset.
> w0 = 100000> q.vfinx.a = mu.vfinx.a + sigma.vfinx.a*(-1.645)> q.veiex.a = mu.veiex.a + sigma.veiex.a*(-1.645)> VaR.05.vfinx.a = w0*(exp(q.vfinx.a) - 1)
> VaR.05.veiex.a = w0*(exp(q.veiex.a) - 1)
> VaR.05.vfinx.a[1] -23885> VaR.05.veiex.a[1] -32488

III. Time Series Concepts (8 points, 4 points each)a. Let {Yt } represent a stochastic process. Under what conditions is {Yt } covariancestationary?
E[Yt ]  var(Yt )   2cov(Yt , Yt  j )   j (depends on j and not t )b. Realizations from four stochastic processes are given in Figure 1 below. Whichprocesses appear to be covariance stationary and which processes appear to be nonstationary? Briefly justify your answers.

Process 20.8
y20.0-2-10.40204060

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10004060
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Process 3

Process 480

10080

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1201y40-4 -3 -2 -1

y320Time

20020406080

Time100-4 -3 -2 -1

y1

1

21.2

Process 10204060
Time

Figure 1: Realizations from four stochastic processes.

Processes 1 and 3 appear to be covariance stationary. The means and volatilities
appear constant over time.
Processes 2 and 4 appear to be non-stationary. Process 2 has a clear
deterministic trend, so the mean is not constant over time. Process 4 appears tohave two distinct means. The first mean is roughly zero for the first half of thesample, and the second mean is roughly -2 for the second half of the sample.

Figure 2 below shows a realization of a stochastic process representing a monthly timeseries of overlapping 2-month continuously compounded returns, where the 1-monthcontinuously compounded returns follow a Gaussian White noise process.
c. Based on the sample autocorrelations, which time series process is most appropriate fordescribing the series: MA(1) or AR(1)?
Based on the sample autocovariance function (SACF), an MA(1) model looks more
appropriate than an AR(1) model. In an MA(1) model, 1 and  j  0 for j  11 2
and in an AR(1) model  j   j . The SACF shows ˆ1  0.42 and ˆ j  0 for j  1 which isconsistent with an MA(1) model.

1 2 3-3-1rt(2)Overlapping 2-month cc return

020

4060

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Overlapping 2-month cc return

051015Lagd. If you think the process is an AR(1) process, what do you think is the value of theautoregressive parameter  ? If you think the process is a MA(1) process, what do you
think is the value of the moving average parameter  ?
If the processes was an AR(1), then   ˆ1  0.42 . If the process was an MA(1) then
 1  0.42    0.42  (1   2 )  0.42  0.42   2  0.42   2    0.42  01 2
This is a quadratic equation in  . Using the quadratic formula, there are two solutions1  1  4(0.42)2
 1.836 and 0.554 .
2(0.42)

IV. Descriptive Statistics (24 points, 4 points each)
Figure 3 shows monthly continuously compounded returns on the Vanguard S&P 500
index (VFINX) and the Vanguard Emerging Markets index (VEIEX) over the 5-year
period September 2005, through September 2010. For this period there are T=60
monthly observations.

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ret.z

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S&P 500
Emerging Markets20062007

2008

2009

2010

Index
Figure 2: Monthly continuously compounded returns on two Vanguard Funds.

a. Do the monthly continuously compounded returns from the two funds look likerealizations from a covariance stationary stochastic process? Why or why not?
Recall, covariance stationarity implies that the mean, variance and autocovariances areconstant over time. Both processes appear to have changing means and variances. The
correlation between the processes may also be changing over time. Clearly thevolatilities are higher and the means are lower during the crisis period. The series seemto move together more closely during the crisis period as well. Given these observations,
it does not appear that these two series are covariance stationary.
b. Comment on any common features, if any, of the two return series. Recall, thefinancial crisis reached its peak toward the end of 2008.
Both series tend to move together, indicating a positive correlation. The positivecorrelation appears to be greater during and after the crisis period. Also, the volatilities
of both series seem to increase around the same time.

The figures below gives some graphical diagnostics of the return distributions for theS&P 500 index and the emerging markets fund.

0-0.15-0.05

cc return

642cc return

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10S&P 500 monthly cc returns-0.20-0.10

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vfinx.mat

Normal Q-Q Plot
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Sample Quantiles

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Normal Q-Q Plot

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density estimate

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Emerging Markets2

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Emerging markets returns

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Vanguard Emerging M arkets Index

051015Lag

c. Do the returns on the S&P500 index and the emerging markets fund look normally
distributed? Briefly justify your answer.
The returns on both the S&P 500 index and the emerging markets index exhibit nonnormal behavior. The histograms show a long left tail (negative skewness) and theboxplot indicates a some large negative outliers (fat tails). The normal QQ-plot is not
linear in the left tail. All of this is strong evidence against the normal distribution.

d. Which asset appears to be riskier? Briefly justify your answer.
The volatility of the emerging markets index is much larger than the volatility of the S&P
500 and appears to be riskier.
e. Based on the scatterplot of returns, does there appear to be any linear dependence
between the returns on the S&P500 index and the emerging markets fund? Briefly justify
your answer.
There is a strong positive linear association in the scatterplot. When the S&P 500 returns
increase, the emerging markets returns also increase and the scatter lies quite close to astraight line.
f. Based on the sample autocorrelation functions (SACFs), do the returns show any strong
temporal dependence?
Both sample autocorrelation plots show a positive first lag autocorrelation that is
marginally statistically different from zero (the estimate of 1 is just above the dottedline). The remaining sample autocorrelations are not very large. Hence there is weakevidence of temporal dependence that lasts at most one period.

V. Constant Expected Return Model (20 points, 4 points each)
Consider the constant expected return modelrit  i   it ,  it ~ iid N (0,  i2 )cov(rit , rjt )   ij , cor (rit , rjt )  ijfor the monthly continuously compounded returns on the Vanguard S&P500 index andthe Vanguard emerging markets index presented in part V.

Below are simulated returns for the S&P 500 index and the Emerging Markets index
from the CER model calibrated using the sample estimates of the CER model parameters
for the two assets.

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Simulated Returns

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2007

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Emerging Markets

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Emerging Markets

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Simulated Vanguard S&P 500 Index

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Simulated Vanguard Emerging Markets Index

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a. Which features of the actual returns shown in part V are captured by the simulated
returns and which features are not?

The features of the actual returns captured by the simulated returns are:

The mean of VEIEX is slightly higher than VFINXVEIEX has higher volatility than VFINX
Strong positive correlation between VEIEX and VFINXThe lack of time dependence (autocorrelation) in the returns

The features not captured by the simulated returns:
The non-normality of the observed data. Both series have negative skewness andsome large outliers.
The changing volatility and correlation during and after the crisis period.

The following R output gives the estimates of  i ,  i ,  ij and  ij for the S&P 500 index(VFINX) and the emerging markets index (VEIEX) from the T=60 months of data:> muhat.vals
vfinxveiex
0.0004694 0.0094886> sigmahat.vals
vfinx
veiex
0.05195 0.08549> covhat.vals
vfinx,veiex
0.003809> rhohat.vals
vfinx,veiex
0.8576b. For the S&P 500 index (vfinx), compute 95% confidence intervals for  ,  . Briefly
comment on the precision of the estimates. In particular, note if both positive andnegative values are in the respective confidence intervals.
The rule-of-thumb formula for a 95% confidence interval for a parameter  is

 (ˆ)
ˆ  2  SE

For VFINX, the estimated standard errors for  and  are ( ˆ )  ˆ / T  0.05195 / 60  0.0067
SE (ˆ )  ˆ / 2T  0.05195 / 120  0.0047
SEThe 95% confidence intervals for  and  arevfinx : 0.0004698  2  0.0067  [0.01294, 0.01388]
 vfinx : 0.05195  2  0.0047  [0.04247, 0.06144]
The mean is not estimated very precisely. Both negative and positive values are in theconfidence interval and the width of the interval is fairly big. The sd is estimated much
more precisely.
c. Compute a 95% confidence interval for  vfinx ,veiex . Use this interval to test
the hypotheses H 0 : vfinx ,veiex  0.5 vs. H1 : vfinx ,veiex  0.5 using a 5% significance level.
For VFINX, the estimated standard error for  vfinx ,veiex is ( ˆ )  1  ˆ  1  (0.8575)  0.03417
SET
60
22

The 95% confidence intervals for  vfinx ,veiex is ( ˆ )  0.8575  0.03417  [0.7891, 0.9258]
 vfinx ,veiex : ˆ  2  SE

This interval is quite tight around 0.8575 and indicates a precise estimate. To test
H 0 : vfinx ,veiex  0.5 vs. H1 : vfinx ,veiex  0.5 we check to see if 0.5 lies in the 95%
confidence interval. Since 0.5 is not in the confidence interval we reject at the 5%
significance level.

d. For vfinx and vieix Consider testing the hypotheses
H 0 : rt ~ normal vs. H1 : rt is not normally distributed

using a 5% significance level. The JB statistics are JBvfinx  15.11 and JBvfinx  28.82 .
What do you conclude?
Under the null of normality, the JB statistic has a chi-square distribution with 2 degreesof freedom. The 5% critical value is the 95% quantile of the chi-square distribution with

2 degrees of freedom. This quantile is 6. Hence, we reject the null at the 5% significancelevel if JB > 6. Since JB > 6 for both assets we reject the normality assumption at the 5%
significance level for both assets.
e. The 24-month rolling estimates of m and s are illustrated in and below. The CER
model assumes that m and s are constant over time. Is this a reasonable assumption? Whyor why not.

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24 month rolling means and sds for VFINX

Rolling mean
Rolling sd
Monthly returns
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Index

Figure 3 24-month rolling estimates for VFINX

2010

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24 month rolling means and sds for VEIEX

Rolling mean
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Index

The rolling estimates of  and  for both assets are very similar. The rolling estimates of start out slightly positive and then dip negative during the financial crisis. There isclearly a structural break in the mean from positive values to negative values around theend of 2008. The rolling estimates of  start to increase at the end of 2008 and then level
off at in the middle of 2009. The values at the end of the sample are almost twice as big
as the values at the beginning of the sample. The rolling estimates show a non-constantvalue for throughout the sample.