FINS5516 Corporate Finance

Posted 2023-04-13 smpbs50

Mohamad MOURAD – Term 1, 2023 UNSW Sydney
FINS5516 – International Corporate Finance
Term 1, 2023, UNSW Sydney
Data Exercise Assignment
DUE: Sunday 16 April 2023, 11:59pm (Sydney, Australia time)
Weighting
This assessment is worth 15% of your final grade for FINS5516 – International Corporate
Finance. Next to each question is the allocation of marks. There are a total of 30 marks for
this assignment.
Assignment Learning Objectives
The purpose behind this assignment is to get students to:
1. apply and assess the relevance of the International Parity Conditions and Purchasing
Power Parity (PPP) Theory in a practical setting,
2. think outside the textbook and homework questions framework,
3. conduct their own research,
4. using actual data and statistical methods (regression and regression analysis),
5. improve their familiarity with statistical tools in Microsoft Excel.
This assignment is designed to give students an insight into how economists and analysts in
industry approach the topic of exchange rate modelling.
This assignment is individual work and must be submitted as individual work only.
IT IS RECOMMENDED THAT STUDENTS WORK ON THIS ASSIGNMENT
FREQUENTLY. CRAMMING AT THE LAST MOMENT IS A BAD STRATEGY. Mohamad MOURAD – Term 1, 2023 UNSW Sydney
2
FINS5516 – Data Exercise Assignment
The LIC will randomly assign each student one of five countries in the list below:
1. Denmark (DKK)
2. Italy (EUR)
3. Mexico (MXN)
4. New Zealand (NZD)
5. South Korea (KRW)
Once assigned a country, the student will analyse the exchange rate ?/ comprising that
country’s currency in relation to that of the United States (USD). The USD is the base
currency irrespective of which currency you have been allocated. Thus, for example, if a
student is assigned Denmark, then they are required to complete the data exercise
assignment on the DKK/USD exchange rate.
Download the Excel file uploaded on Moodle to see which country you have been allocated.
Section 0 – General Overview of the Data Exercise Assignment.
You are constructing a regression model to forecast an estimate of the exchange rate. You
expect changes in future exchange rates depend on a set of key macroeconomic variables:
1. the countries’ real GDP growth rates
2. the inflation rate differential
3. long-term interest rate differential
Section 1 – Downloading the Data and Setting up the Excel File.
1.1 – Using FactSet, obtain quarterly data from 2001Q1 to 2022Q3 on:
- The exchange rate ?/ you have been randomly assigned.
- Economic growth rates for both countries, defined as the year-on-year % change in
real GDP.
- Inflation rates for both countries, defined as the year-on-year % change in the CPI.
- Long-term interest rates for both countries.
1.2 – Using the data you collected from FactSet, calculate the following:
- The change in exchange rates over (i) 1 quarter, (ii) 1 year, and (iii) 3 years. These
must be forward looking. Calculating a forward-looking change in the exchange rate
is best illustrated by an example. Thus, for example, the one-quarter change in the
exchange rate, /, for March 2021 is:
- Economic growth rates for both countries as a decimal. This is done by dividing the
FactSet value by 100.
Mohamad MOURAD – Term 1, 2023 UNSW Sydney
3
- The inflation rate differential as a decimal (ensure that you divide the FactSet value
by 100), which for simplicity, we define as the rate of the term currency country less
the rate of the base currency country.
- The long-term interest rate differential as a decimal (ensure that you divide the
FactSet value by 100), which for simplicity, we define as the rate of the term currency
country less the rate of the base currency country.
Section 2 – Regression Modelling
2.1 – Consider the following econometric structural model of the change in the exchange rate:
is the percentage change in the exchange rate over period .
Δ is the annual percentage growth rate in real GDP over period .
is the inflation rate differential for period .
is the interest rate differential for period .
Using linear regression, obtain the coefficient estimates for each of the 3-time horizons. You
need to report for each time-horizon, ALL coefficient estimates, p-values, Adjusted R-squares,
F-statistics (and p-value) in one table, so the grader is able to see your results in your written
submission (rather than the Excel file). (4 marks)
2.2 – Analyse the statistical significance of the coefficient estimates at the 5% level. You are
to provide a summary/high-level analysis of the key results. Word limit: 150 words. (3 marks)
2.3 – Consider both the p-value associated with the F-statistic (at the 5% level of significance)
and the adjusted R-squared as the forecast horizon increases from 1 quarter to 3 years.
Provide some commentary and discuss whether such results (across the 3 models) are
consistent with PPP theory. Word limit: 150 words. (4 marks)
2.4 – Which macroeconomic variables from the model you have estimated are considered
economically important for modelling changes in the exchange rate? Are you surprised by
these results? Are they consistent with PPP theory? Word limit: 150 words. (5 marks)
2.5 – One potential issue the analyst faces when using multiple linear regression analysis is
the multicollinearity of the independent variables. Verify whether or not multicollinearity exists
among the independent variables. This is done by examining the correlation between each of
the independent variables. Think of this as a correlation matrix (must be included in your
document) which can be easily performed in Excel using the “Data Analysis” tool pack. If the
independent variables are highly correlated, then the analyst is unable to isolate the effect of
each independent variable on the dependent variable. Thus, analysis essentially becomes
pointless. Word limit: 100 words. (3 marks)
Section 3 – Forecasting
3.1 – Using the latest values of the key macroeconomic variables forecast the estimated
change in the exchange rate:
(1)
Mohamad MOURAD – Term 1, 2023 UNSW Sydney
4
a) 1-quarter ahead,
b) 1-year ahead
c) 3-years’ ahead
Report the magnitude of the forecasts for each regression model in no more than two
sentences. Provide brief commentary (no more than one sentence) as to whether the currency
you have been assigned is forecast to depreciate or appreciate against the USD over each
forecast horizon. (3 mark)
3.2 – Do you think that the structural model (Equation 1) is a useful model for modelling
changes in the exchange rate? What are some of its limitations? Irrespective of your answer,
what other independent variable would you include in Equation 1? Provide at least one
economic reason for that variable’s inclusion. You should also provide commentary indicating
what relationship this variable has with the change in the exchange rate (that is, the dependent
variable). Word limit: 150 words. (3 marks) Mohamad MOURAD – Term 1, 2023 UNSW Sydney
5
Additional Information
Note 1: Grammar, Spelling, Punctuation and Style.
1. Five marks out of the 30 marks will be allocated to grammar, spelling, professionality
of the responses and ensuring that all data and calculations in the Excel file are
expressed to 3 decimal places. You need to ensure that your work is polished and
contains NO errors. Remember you are presenting your work. When you are working
professionally, the market expects high quality output.
2. If you use sources in your answers, ensure that you formally cite them. The style of
referencing is for you to decide.
3. Plagiarism is not tolerated. Your answers must be written by you and only you. Turnitin
has a similarity indicator that reports a percentage similarity score. Submissions with
similarity scores should not be greater than 15% if they are written in your own words.
Turnitin includes the cover sheet and your references list in its calculation of its similarity score.
However, the grader will be able to filter this out and see the percentage similarity score based
only on the student’s written responses.
Note 2: Data Exercise Assignment Submissions and Responses.
1. Students will only be permitted to submit their data exercise assignment ONCE in
Turnitin. There are NO multiple submission options permitted. What is submitted first
will be graded.
2. There is NO grace period for any submissions.
3. Lengthy responses to questions will result in only the first 150 words of each part (or
whatever the word limit is for that section) being graded.
4. If a student submits their data exercise assignment on an exchange rate other than
the exchange rate they were assigned, then they have not followed instructions. The
maximum grade a student will then obtain is 60% for this assessment.
5. If a student submits their data exercise assignment via the incorrect Turnitin
submission link, then 1 mark will be deducted.
6. You must type your answers and submit as a PDF document via Turnitin. Ensure that
the cover sheet is attached with your submission. See Moodle for cover sheet. A
submission without the cover sheet will result in 1 mark being deducted. If your
submission is not submitted in PDF format, 1 mark will be deducted.
7. Submit your Excel file with the calculations. Failure to submit the Excel file will result
in a deduction of 5 marks.
8. The School of Banking and Finance’s policy stipulates late submissions will attract a
5% penalty per day following the assignment due date (weekend days included). A
submission made one week (that is, 7 days) after the specified due date will result in
a grade of 0.
The LIC reserves the right to add to this list in light of changing conditions. Any changes made
will be communicated with students as an announcement via the Moodle webpage.

WX：codehelp mailto: [e-mail]

使用C#实现欧姆龙PLC FINS协议读取

由于采用欧姆龙的PLC进行设计和开发，所以需要与它进行协议通讯。

之前也有同事写过这样的协议，但是使用起来不是很方便，因此重新写了一份FINS协议，在开发的过程中，也碰到一些问题，比如C#结构体对齐问题，导致发送数据不对，后来仔细检查才解决了这个问题。

又由于对欧姆龙的PLC区域不是很了解，一直不了解读写位和读写字的区别，后来经过反复验证，才发现读写位与字的区别是在于区域码会不一样，也就是说，即使是同一个区，也是分为两个区码，比如W区，按位读写就是0x31, 按字读写就是0xB1，其它发送的字节是一样，仅这里的区别。

另外PLC里的字都是网络顺序，因此要记得转换为小端格式，才能准确地读取值。

同时，在这里也使用了动态地构造数据包的方式，与固定填写有点区别，这个可以参考之前使用BufferStream的文章。

在位与字的区分，就写了下面的代码：

 private byte GetAreaTypeFlag(OmronAddressType omronAddressType, bool isBitProcess)
        
            if (isBitProcess)
            
                //位处理
                switch (omronAddressType)
                
                    case OmronAddressType.CIO:
                        return 0x30;
                    case OmronAddressType.WR: