Problem Set #2 BTC data

Problem Set #2

Problem 1 (35).
Consider the dataset about bitcoin (BTC) price, the time series is from

(1: Points 6) First read the BTC data, then plot the QQ plot, boxplot and
kernel density estimates. Discuss any features you see in the QQ plot, boxplot
and kernel density plot. Specifically, address the following questions: Do the
data appear to be normally distributed? If not, in what ways do they appear
non-normal? Are the data symmetrically distributed? If not, how are they
skewed? Do the data seem heavy tailed compared to a normal distribution?
How do the left and right tails compare; is one tail heavier than the other?
(2: Points 6) Next, conduct data transformation by taking the squareroot and log-arithm to get transformed data, denoted as sqrt.BTC and
log.BTC, respec-tively. Further plot QQ plots, boxplots and kernel density
estimates, discuss the features you see in the plot similar to question (1).
(3: Points 6) Conduct Box–Cox transformation to the original data and
estimate its parameter λ by maximum likelihood.
(Hint: Zoom in on the high-likelihood region with the following function:
boxcox (BTC∼ 1, lambda=seq(0, 1, 1/100)); Also use package ”MASS”).
(4: Points 6) Find a 99% confidence interval (CI) for λ.
(5: Points 6) Try to fit a skewed t-distribution including R code (use package
”fGarch” and function ”sstdFit”).
(6: Points 5) What are the estimates using the skewed t-distribution?
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Problem 2 (35).
Consider the Gold price in USD data and this time series is observed from
January 3rd, 2017 to October 18th, 2019 (notice that you might need to
clean some invalid data in Excel).
(1: Points 6) First r ead the Gold data calculate the corresponding l og r eturns
(say Y ). Plot this time series of l og r eturns and write a brief description. Do
the series l ook stationary? Do the fluctuations i n the series seem to be of constant size? If not, describe how the volatility fluctuates. ( use f unction ”diff”).
(2: Points 6) Plot the QQ plot, boxplot and kernel density estimates of
the log re-turns and give some explanations.
(3: Points 6) Fit the standardized t-distribution (std) to the log returns.
Find MLEs of the mean, standard deviation, and the degrees-of-freedom
parameter by using Maximum Likelihood Estimation. (use package ”fGarch”
and function ”Optim”).
(4: Points 6) Calculate the AIC and BIC values based on the above
optimazition.
(5: Points 6) Modify the code so that the MLEs f or the skewed t-distribution are
found. I nclude your modified code with your work. What are the MLEs?(also use
package ”fGarch” and function ”Optim”).
(6: Points 5) Which distribution is selected by AIC, the t or the skewed tdistribution?Which distribution is selected by BIC, the t or the skewed tdistribution?
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Problem 3. (30)
Consider the dataset of IBM price shares in the time series observed from
January 3rd, 2017 to October 18th, 2019.
(1: Points 4) Read the IBM dataset and calculate its sample mean, standard
devi-ation, skewness and kurtosis. (need to use package ”fGarch”).
(2: Points 4) Fit a t-distribution to the data and show the estimates (use
function ”stdFit”).
(3: Points 4) Try to bootstrap the sample mean 1000 times in two cases:
model-free and model-based on t-distribution. (use functions ”sample” and
”rstd”).
(4: Points 4) Plot QQ plot and KDEs of ModelFree mean and ModelBased
mean. Also, plot side-by-side boxplots of the two samples. Describe any major
dif-ferences between the model-based and model-free results. Include the plots
with your work.
(5: Points 5) Find 95% bootstrap c onfidence i ntervals f or the sample mean using the
model-based and model-free bootstraps with digits=5.
(6: Points 4) Estimate the bias of the sample mean of IBM based on modelfree and model-based bootstraps.
(7: Points 5) Estimate the mean squared error (MSE) of the sample mean
of data IBM. (Notice that MSE=variance+bias2
.)
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