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.

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 eh/f   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 eh/f  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, eh/f , for March 2021 is:

-     Economic growth rates for both countries as a decimal. This is done by dividing the FactSet value by 100.

-     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:

Δeℎ/f,t  = F0  + F1ΔGDPℎ,t  + F2ΔGDPf,t  + F3 InfRDt+ F4IntRDt  + et

where

(1)

Δeℎ/f,t  is the percentage change in the exchange rate over period t .

ΔGDPt  is the annual percentage growth rate in real GDP over period t .

InfRDt  is the inflation rate differential for period t .

IntRDt  is the interest rate differential for period t .

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:

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)