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Hedging Exchange Rate Risk at Afterpay

Table of Contents

1.0 Exchange rate.

2.0 Sensitivity analysis.

3.0 Case Analysis.

4.0 Recommendations.

5.0 References.

1.0 Exchange Rate

Many variables, such as inflation, development expectations, political stability and economic policies, affect the exchange rate between countries. It is impossible to predict many of these triggers.

An exchange rate is defined as the 'exchange rate' at which it is possible to exchange the currency of one country for another. With shifts in market forces in supply and demand from one country to another, this currency will fluctuate on a regular basis. For these reasons, it is important to consider what sets the prices when sending or receiving money abroad (Bakar & Rosbi, 2017).

This section addresses some of the main factors influencing variations and changes in exchange rates and outlines the causes for their instability, which enables you to discover the right time to ship abroad.

Changes in global inflation bring about changes in exchange rates. The value of its currency can be seen from a nation with a lower inflation rate than other economies.

Interest rate adjustments impact both the inflation rate as well as the currency exchange rate. Forex exchange rates, interest rates and inflation are all interrelated. A rise in interest rates allows the currency of a nation to appreciate, since higher interest rates offer lenders higher interest rates, drawing more international money, which in turn boosts the exchange rate (Raheem, 2020).

The current account of the nation shows the trade and foreign investment and profits balance. It encompasses the total number of sales, such as exports, imports, transfers, etc.

A government debt is a federal government owned public debt or a national debt. International money is less likely to be obtained by a nation with government debt, contributing to inflation.

The political situation and economic performance of a nation will influence its currency's strength. Countries at low risk of political instability are more attractive to foreign investors, thereby drawing investment from countries with increased political and economic stability (Nyoni, 2018).

If a nation faces a recession, the interest rates decline, leaving it less likely to attract international money. As a result, the economy weakens and lowers the exchange rate toward other nations.

If the value of the currency of a nation is projected to increase, buyers will in the near future demand more of that currency to make a profit.

The variations in foreign exchange rates are all determined by all these considerations. Continuing these variables will help you determine the best timing for foreign money transfers whether you send or receive money regularly (Tarasova, Usatenko, Makurin, Ivanenko, & Cherchata, 2020).

Graph below represents past data of exchange rates AUD/USD from 1991 to 2020, on the bases of this data future value for next six months would be extracted using ARIM model.

Above table shows exchange rate movements from 1991 to 2020, it remained between 0.5 minimum in 2002 and 1.1 maximum near 2012 and 2013.

Forecasting values and graphs has been extracted by using ARIMA model in Eview, it’s like to decrease exchange rate AUD to USD for next six month on the basis of past data sample used from 1991 to Feb 2020.

Above represents that value of exchange rate could remain up to 0.6545 till month 8 (Aug 2020), here M3 represents month of march and so on till M8 as month of Aug 2020.

2.0 Sensitivity Analysis

The analysis of sensitivity is a financial model which determines how changes to the goal variable in other variables known as the input variable are influenced. Key-analysis or simulation is called this model. This is a means to forecast the outcome of a single option using numerous unique variables. An analyst can decide how variations in a variable impact the outcomes by constructing a particular collection of variables (Arize, Malindretos, & Igwe, 2017).

When sensitivity is evaluated, aim and input variables-or different and dependent variables are thoroughly analyzed. The observer investigates how the changes occur and how the input variable influences the target.

HI FY19

HI FY20

Change%

Same Increasing pattern for next 6 months

Underlaying sales

2272.6

4758

109%

9961.5

ANZ

2009

3118.5

55%

4840.7

US

263.7

1437.4

445%

7835.1

UK

0

202.1

 

202.1

       

After pay Income

85.2

179.6

111%

378.6

       

After pay other income

18.2

32.6

79%

58.4

Total income

103.4

212.2

105%

435.5

Gross loss

27.4

47.8

74%

83.4

Net transaction loss

13.6

21.8

60%

34.9

       

Other variable transaction cost

24.8

55.9

125%

126.0

Net transaction margin

46.7

102

118%

222.8

The sensitivity study was carried out on the grounds that the exchange rate had risen 10% and the exchange rate decreased 10% from AUD to USD.

Observing the excellent exit below, the rising exchange rate increased AUD profit from 248.8 to 273.7. and the declining exchange rate lowered the Company's profit from 248.8 to 224.

AU Doller Sensitivity Analysis

US Doller Sensitivity analysis

Same Increasing pattern for next 6 months

27 Feb Exchange rate

10% plus

10% minus

27 Feb Exchange rate

10% plus

10% minus

 

Underlaying sales

9961.5

6547.7

7202.5

5892.9

9961.5

10957.7

8965.4

 

ANZ

4840.7

3181.8

3500.0

2863.6

4840.7

5324.8

4356.7

 

US

7835.1

5150.0

5665.0

4635.0

7835.1

8618.6

7051.6

 

UK

202.1

132.8

146.1

119.6

202.1

222.3

181.9

 
             

After pay Income

378.6

248.8

273.7

224.0

378.6

416.5

340.7

 
             

After pay other income

58.4

38.4

42.2

34.5

58.4

64.2

52.6

 

Total income

435.5

286.2

314.9

257.6

435.5

479.0

391.9

 

Gross loss

83.4

54.8

60.3

49.3

83.4

91.7

75.0

 

Net transaction loss

34.9

23.0

25.3

20.7

34.9

38.4

31.4

 
             

Other variable transaction cost

126.0

82.8

91.1

74.5

126.0

138.6

113.4

 

Net transaction margin

222.8

146.4

161.1

131.8

222.8

245.1

200.5

 
 

2/27/2020

0.6573

0.72303

0.59157

 

Net transaction margin has been increased with increased in dollar rate and decreased with decrease in exchange rate. Base rate has been considered from 27 Feb 2020 for all calculations.

3.0 Case Analysis

 

Forecast A$ net profit

Impact*

 

Exchange Rate (A$/$)

Exchange Rate (A$/$)

 

x-10%

x

x+10%

x-10%

x

x+10%

Unhedged

0.72303

0.6573

0.59157

     

100% Forward

0.72303

0.6573

0.59157

     

100% Option

 

0.6573

0.59157

     

75% Forward, 25% Option

0.690165

         

50% Forward, 50% Option

0.6573

         

25% Forward, 75% Option

0.624435

         

The choice is to put options, but only for better US$, should be used at higher or lower prices in the process of forward contract transactions. In the case of 75% forward and 25% choice prices, 75% of total weighting and 25% of the optional rate were considered for the total weighting etc.

Scenario analysis

 

Forecast A$ net profit

Impact*

 

Exchange Rate (A$/$)

Exchange Rate (A$/$)

 

x-10%

x

x+10%

x-10%

x

x+10%

Unhedged

160.51

145.92

131.33

60.51266

45.9206

31.32854

100% Forward

160.51

145.92

131.33

60.51266

45.9206

31.32854

100% Option

160.51

145.92

131.33

 60.51266

45.9206

31.32854

75% Forward, 25% Option

153.22

   

53.21663

   

50% Forward, 50% Option

145.92

   

45.9206

   

25% Forward, 75% Option

138.62

   

38.62457

   

For the next six months sales are expected to rise by 222 million based on the last six months. Multiplying the exchange rate by 222 million earnings, and subtracting 100 million from 160.50 (222 * 0.7230), the effect is determined.

4.0 Recommendations on Autoregressive Integrated Moving Average Model

Under higher dollar market prices and the same for 100% forward currency contracts, higher gains could be made. Otherwise, an adequate profit of $45.92 million would continue, and lowering the premiums would still minimize profit. More portion of the upstream price will be advantageous under shifting forward and option ratios.

5.0 References for Autoregressive Integrated Moving Average Model

Arize, A. C., Malindretos, J., & Igwe, E. U. (2017). Do exchange rate changes improve the trade balance: An asymmetric nonlinear cointegration approach. International Review of Economics & Finance, 49, 313-326.

Bakar, N. A., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 237311.

Nyoni, T. (2018). Modeling and Forecasting Naira/USD Exchange Rate In Nigeria: a Box-Jenkins ARIMA approach.

Raheem, I. D. (2020). Global Financial Cycles and Exchange Rate Forecast: A Factor Analysis. Borsa Istanbul Review.

Tarasova, T., Usatenko, O., Makurin, A., Ivanenko, V., & Cherchata, A. (2020). Accounting and features of mathematical modeling of the system to forecast cryptocurrency exchange rate. Accounting, 6(3), 357-364.

Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Accounting and Finance Assignment Help

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