联系我们： 手动添加方式: 微信>添加朋友>企业微信联系人>13262280223 或者 QQ: 1483266981
这是一篇是用R语言分析财务数据并开发定量模型
MANG6297 Assessment
Module Title: Advanced Time Series Modelling
Module Leader: Tapas Mishra
Submission Due Date: @ 16:00 30 April 2020 Word Count: 3000
Method of
Submission:
Electronic via Blackboard Turnitin ONLY (You are not required to submit a hard copy)
(Please ensure that your name does not appear on any part of your work)
Any work submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see
below), unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.
University Working Days Late: Mark:
1 (final agreed mark) * 0.9
2 (final agreed mark) * 0.8
3 (final agreed mark) * 0.7
4 (final agreed mark) * 0.6
5 (final agreed mark) * 0.5
More than 5 0
This assessment relates to the following module learning outcomes:
A. Knowledge and
Understanding
A2. forecasting of financial time series
A3. competence in using an econometric software package (STATA)
B. Subject Specific Intellectual
and Research Skills
B2. evaluate model fit
B3. assess out-of-sample properties
B5. relate forecasts to strategic decisions
B6. critically evaluate statistical models and forecasting tools
C. Transferable and Generic
Skills
C1. analyse financial data
C2. develop quantitative models
3
Group Coursework Brief: You should be aware that all members of your group share responsibility for any
academic integrity breaches or other issues that may arise from your group’s coursework submission.
Coursework Brief:
Rubric
I do expect tables / graphs / diagrams in this assignment (embedded in the main text). Each
table, graph or diagram will count as 25 words. Any table/graph must be explained contextualising
the results to the context of the question. Remember that the graphs and tables you present are
properly contextualised and form an important aspect of our explanations. Additional graphs and
tables can be put in the appendix as well as the output from any statistical software you have used
for the analysis.
Groups
Please self-select groups of up to 5 students. In case, a student does not find a suitable group, the
module leader will arrange a group to the student. At the end of February, you will find an Excel
spreadsheet on Blackboard indicating your selected groups. Please check the list carefully!
Marking: The marks awarded to each member of a group will be the same as the marks awarded to
that group. For clarity over a student’s contribution, please mention in your final assignment
submission on who among you all contributed which section. Please do NOT forget to write the
name and student id of each member of the group in your assignment (from page).
SEMESTER 2 2019/20 Faculty of Business, Law & Art
There are TWO compulsory questions for this Assignment.
Question One
Background information for Question One
In Question One, we have provided cross-market time series data for Bitcoin (one of the popular
cryptocurrencies floating in the market). The Bitcoin is traded in various currencies, such as in Euros,
USD, Korea, etc. The data have been collected from Coincheck (one of the platforms that provides
aggregate price data for Bitcoin). In the Blackboard site of the course (see Assignment folder), we
have included Bitcoin price data for six exchange markets (Europe, USA, Australia, Korea, Japan,
Indonesia).
You can choose ANY file(s) depending on your interest. Eviews, Stata, R, Python or other any
econometric software may be used for empirical estimation purpose.
Tasks for Question One
(1) By plotting the selected Bitcoin price series, explain if you find any ‘trend’ in the price
behaviour. Also plot the Kernel Density (or Histogram) of the series and explain if the series
depicts a ‘non-normal’ behaviour. Plot the Autocorrelation Function and comment on the
persistence behaviour of the series.
(2) Test for (non-)stationarity in the selected series by using Augmented Dickey-Fuller, PhillipsPerron, and KPSS tests. Use options of intercept with and without trend term to compare your
results. What implications do the ‘presence or absence of a unit root’ imply for the selected
Bitcoin price regarding ‘weak, strong, semi-strong efficiency’ of the Bitcoin market
(3) Assume that the Bitcoin series you selected is neither I(1) nor I(0). Then what would an I(d)
with 0