Draft courseworkModule code: AFE7510Financial Technology and Blockchain InstructionsThere are four
questions each worth 25 marks. Answer all four questions. In the light of the
emergency coronavirus measures this assignment will constitute 100% of the
total marks awarded for this module and replaces the final exam. 1. In a study to
investigate regional bias a web-scraped sample of online hot
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Draft coursework
Module code: AFE7510
Financial Technology and Blockchain
Instructions
There are four
questions each worth 25 marks. Answer all four questions. In the light of the
emergency coronavirus measures this assignment will constitute 100% of the
total marks awarded for this module and replaces the final exam.
1.
In a study to
investigate regional bias a web-scraped sample of online hotel reviews s
obtained. The data thus consists of multiple ratings from each hotel but only
one rating from each reviewer. A generalised linear mixed model is then fitted
to this data to see if the probability that the survey respondent gives a high
score depends on their region. The R code to read in the data is shown below.
[Note that as presented you should be able to copy and paste this code into R
provided that you update the file location in the first line below as
required]:
scores<-read.table("G:ScoreData.txt")
rating<-scores[,1]
product<-scores[,2]
region<-scores[,3]
regionmissing<-scores[,4]
rating<-rating[regionmissing<1]
product<-product[regionmissing<1]
region<-region[regionmissing<1]
product<-factor(product)
region<-factor(region)
high<-1*(rating>3)
medium<-1*(rating==3)
low<-1*(rating<2)
(a) What R package has to
be loaded prior to fitting generalised linear mixed models?
[3
marks]
(b) What model does the
following code suggest fitting?
high2<-glmmPQL(high~region,
random=~1|product, family=binomial)
[3
marks]
(c) Using the above R
commands fit the model suggested in part (b) above. Using this model is there
any evidence of a regional effect? What is the probability that respondents
from each region give a high score using this model?
[8
marks]
(d) An additional
generalised linear mixed model is to be fitted to this data. Under this model
the probability p can be calculated
using
,
(1)
where
the right hand side in equation (1) corresponds to the fitted regression
equation. Show that in this case
[3
marks]
(e) A
generalised linear mixed model is to be fitted to this data. The required R
commands are shown below.
high3<-glmmPQL(high~region,
random=~1|product, family=binomial(link=cauchit))
Using the results obtained from the above R
code estimate the probability that respondents in each region give a low score.
Does this lead to similar estimates to those obtained in part (c)? Comment.
[8
marks]
2.
(a) Using data from the authoritative website coinmarketcap.com download historical price data for a
cryptocurrency other than Bitcoin, Ethereum, Ripple or Bitcoin Cash. Explain
why this cryptocurrency is interesting and give the precise dates over which
the data (closing prices) have been recorded. Read the data into R using the read.table command used in lectures.
Calculate the log-returns and provide a listing of all the R code used in this
subquestion.
[7
marks]
(b) Using the R-package tseries fit a GARCH(1, 1) model to this
data and provide a summary of the results.
[6 marks]
(c) Using the output
produced in part (b) is there any evidence of the ARCH effect or the GARCH
effect?
[6
marks]
(d) Give an example of a
financial time series model commonly used to extend the GARCH model. Give the
model equations and discuss how this model may be fitted in R. Give the R
package that you would need to use in order to accomplish this.
[6
marks]
Hint for part d:
You may find the following reference helpful:
Hentschel, L.
(1995) All in the family. Nesting symmetric and asymmetric GARCH models. Journal of Financial Economics 39 71-104.
3.
(a) List the stylised
empirical facts of financial time series as presented in lectures.
[7
marks]
(b) Discuss an example
from historical economics and finance that has some relevance for the future
development of Bitcoin and cryptocurrency markets.
[9
marks]
(c) Discuss an example
historical economics and finance that has some relevance for the future
adaptation of Blockchain technologies within society.
[9
marks]
4.
(a) Describe what is meant
by the Oxford Blockchain Framework?
[13
marks]
(b) Apply the Oxford
Blockchain Framework to an additional Blockchain application not discussed in
lectures.
[12
marks]
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