Deakin University: International Finance Report on USD/AUD Currency

This report, prepared for a university-level international finance course, analyzes the USD/AUD currency pair. It begins by examining the distribution features of USD/AUD returns and assesses their normality using the 68-95-99.7 rule. The report then compares continuous and artificial returns, provides a directional analysis of AUD/USD, and advises on investment strategies for the next 30 days based on regression analysis. Furthermore, it identifies the efficiency of the FX market using Autoregressive of order 1 and Random Walk Models, evaluating which model is best for forecasting. Finally, it identifies the risk implications of the investment on a daily basis. The report concludes with a recommendation to short USD/AUD, providing a detailed statistical and graphical analysis to support the findings.

Running head: INTERNATIONAL FINANCE
International Finance
Name of the Student:
Name of the University:
Author’s Note:

2INTERNATIONAL FINANCE
Table of Contents
Introduction:...............................................................................................................................3
a) Detecting the kind of distribution features of USD/AUD and graphically computing its
return:.........................................................................................................................................3
b) Using 68-95-99.7 normal thumb rule for deriving the evidence that the distribution is
normal:.......................................................................................................................................4
c) Providing characteristics of the returns for gauging the difference in two returns:...............4
d) Institution and statistic data for providing directorial move of AUD/USD:..........................5
e) Advising the client whether to invest or not in USD/AUD for the next 30 days:.................6
f.1) Identifying the efficiency of FX market in context with the Autoregressive of order 1
model and Random Walk Model:..............................................................................................7
f.2) Analysing the best model that could be used for forecasting:.............................................7
g) Identifying the risk implications of the investment on a daily basis:....................................8
Conclusion:................................................................................................................................9
References and Bibliography:..................................................................................................10

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Introduction:
The assessment aims in evaluating the significance of Currency forecasting, which
allows investors to improve their decision-making capability. The different forecasting model
is used for detecting the future currency value of USD/AUD. Moreover, relevant normal
distribution method is detected under USD/AUD for detecting the return conditions. The data
has been used from 2003 to current date for detecting the return distribution condition of
USD/AUD.
a) Detecting the kind of distribution features of USD/AUD and graphically computing
its return:
1/2/2003
8/2/2003
3/2/2004
10/2/2004
5/2/2005
12/2/2005
7/2/2006
2/2/2007
9/2/2007
4/2/2008
11/2/2008
6/2/2009
1/2/2010
8/2/2010
3/2/2011
10/2/2011
5/2/2012
12/2/2012
7/2/2013
2/2/2014
9/2/2014
4/2/2015
11/2/2015
6/2/2016
1/2/2017
8/2/2017
3/2/2018
10/2/2018
-8.00000
-6.00000
-4.00000
-2.00000
0.00000
2.00000
4.00000
6.00000
8.00000
10.00000
USD/AUD-Continues Compound Return
The above figure provides information regarding the continuous return of USD/AUD
from 2003 to 2019. The above figure indicates that the continuous return distribution of
USD/AUD is at the levels of non-symmetric distribution approach. The figure indicates that
the distribution of returns does not follow normal distribution attributes. This attribute
indicates that current return distribution of USD/AUD is not appropriate and in accordance
with the normal distribution conditions (Lettau, Maggiori and Weber 2014).

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b) Using 68-95-99.7 normal thumb rule for deriving the evidence that the distribution is
normal:
Mean -0.00569200
SD 0.79975595
Ruling From To Number of observations
At 68% of th rule -0.8054479 0.794063945 3087
At 95% of th rule -1.6052039 1.593819892 810
At 97.7% of th
rule
-2.4049598 2.393575839 133
Total 4030
The above table provides relevant evidence for the 68-95-99.7 normal thumb rule,
which is considered adequate for USD/AUD. In accordance with normal thumb rule the
number of observations in 68% is higher than other rulings. Moreover, 95% ruling is higher
than 97.7%, which indicates that the distribution of the USD/AUD follows the normal thumb
rule.

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c) Providing characteristics of the returns for gauging the difference in two returns:
1/2/2003
8/2/2003
3/2/2004
10/2/2004
5/2/2005
12/2/2005
7/2/2006
2/2/2007
9/2/2007
4/2/2008
11/2/2008
6/2/2009
1/2/2010
8/2/2010
3/2/2011
10/2/2011
5/2/2012
12/2/2012
7/2/2013
2/2/2014
9/2/2014
4/2/2015
11/2/2015
6/2/2016
1/2/2017
8/2/2017
3/2/2018
10/2/2018
-8.00000
-6.00000
-4.00000
-2.00000
0.00000
2.00000
4.00000
6.00000
8.00000
10.00000
Continues Compound Return Artificial return
The comparison between the continuous and abnormal returns has been depicted in
the above figure. The probability has been used for detecting the artificial returns of the
USD/AUD, which is compared with the continuous return of USD/AUD. The graph indicates
that the artificial returns do not follow the continuously compounded returns of USD/AUD.
d) Institution and statistic data for providing directorial move of AUD/USD:
1/2/2003
8/2/2003
3/2/2004
10/2/2004
5/2/2005
12/2/2005
7/2/2006
2/2/2007
9/2/2007
4/2/2008
11/2/2008
6/2/2009
1/2/2010
8/2/2010
3/2/2011
10/2/2011
5/2/2012
12/2/2012
7/2/2013
2/2/2014
9/2/2014
4/2/2015
11/2/2015
6/2/2016
1/2/2017
8/2/2017
3/2/2018
10/2/2018
0.8000
1.0000
1.2000
1.4000
1.6000
1.8000
2.0000
USD/AUD

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The above figure provides information regarding the trend of USD/AUD, which has
been detected to be in an uptrend. However, the values of USD/AUD are reaching previous
high and resistance line, which can be identified as a negative attribute for the current value
of the currency (Du and Schreger 2016). Hence, after reaching the previous resistance line
formed during 2016 the USD/AUD values can retrace back, which indicates that the investors
need to short the position. The mean values of USD/AUD are mainly at the levels of -
0.005692002, while the SD value is 0.799755947, which indicates that the current valuation
can decrease in future.
e) Advising the client whether to invest or not in USD/AUD for the next 30 days:
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.3719
431
R Square
0.1383
417
Adjusted R
Square
0.1075
681
Standard
Error
0.0075
279
Observations 30
ANOVA
df SS MS F
Signific
ance F
Regression 1
0.000254
75
0.00
025
4.495
4784 0.04298
Residual 28
0.001586
73
5.7E
-05
Total 29
0.001841
48
Coeffic
ients
Standard
Error
t
Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
1.4090
513
0.001376
57
102
3.59
1.416
E-65 1.40623
1.4118
7 1.40623 1.41187
Compoundin 0.0061 0.002906 2.12 0.042 0.00021 0.0121 0.00021 0.01212

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g return 621 33 025 9784 2
The above table provides information regarding the regression analysis, which is
conducted for the latest 30 periods of USD/AUD. This helps in detecting that the accurate
level of current projection for USD is mainly negative, where the values can decline. Hence,
the short trend of USD/AUD is negative, where the investors can initiate a Sell Side trade for
getting benefits from the declining values of USD/AUD (Carfi and Musolino 2014). The
statistical confirmation is provided from the standard error, which is at the levels of
0.0075279.
f.1) Identifying the efficiency of FX market in context with the Autoregressive of order 1
model and Random Walk Model:
The forex market is considered to be volatile, while it does not provide higher
returns, where the calculation such as hypothesis and return can be used for detecting the
efficiency of the market. Moreover, investors to forecast the future price movement of
USD/AUD. can use the calculation of ‘Autoregressive of order 1 model’ and ‘Random Walk
Model’ The FX market does not have any kind of huge fluctuations without any kind of big
impact such as recession that came during 2008.
f.2) Analysing the best model that could be used for forecasting:
Evaluating Autoregressive of order 1 model
RMSE MAE MSE
0.0651 -0.0647 0.0042
A F SQ error Abs Error(A-F)
3/21/201
9 1.3996 1.4778 0.0061 0.0782 -0.0782
3/22/201
9 1.4073 1.4633 0.0031 0.0560 -0.0560
3/25/201 1.4126 1.4713 0.0034 0.0587 -0.0587

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9
3/26/201
9 1.4041 1.4769 0.0053 0.0728 -0.0728
3/27/201
9 1.4083 1.4680 0.0036 0.0598 -0.0598
3/28/201
9 1.4090 1.4723 0.0040 0.0633 -0.0633
3/29/201
9 1.4110 1.4732 0.0039 0.0621 -0.0621
4/1/2019 1.4043 1.4753 0.0050 0.0710 -0.0710
4/2/2019 1.4124 1.4682 0.0031 0.0558 -0.0558
4/3/2019 1.4073 1.4767 0.0048 0.0695 -0.0695
Evaluating Random Walk Model
MSE RMSE MAE
0.0001 0.0090 (0.0082)
Date A F Abs SQ error Error(A-F)
3/21/2019 1.3996 1.4139 0.0143 0.0002 (0.0143)
3/22/2019 1.4073 1.4143 0.0070 0.0000 (0.0070)
3/25/2019 1.4126 1.4147 0.0021 0.0000 (0.0021)
3/26/2019 1.4041 1.4151 0.0110 0.0001 (0.0110)
3/27/2019 1.4083 1.4156 0.0073 0.0001 (0.0073)
3/28/2019 1.4090 1.4160 0.0070 0.0000 (0.0070)
3/29/2019 1.4110 1.4164 0.0054 0.0000 (0.0054)
4/1/2019 1.4043 1.4169 0.0126 0.0002 (0.0126)
4/2/2019 1.4124 1.4173 0.0049 0.0000 (0.0049)
4/3/2019 1.4073 1.4177 0.0105 0.0001 (0.0105)
The calculations conducted in the above table regarding Autoregressive of order 1
model and Random Walk Model can be used for detecting the current valuation of
USD/AUD. The calculation conducted in the above table indicates that the MAE of both the

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model have provided negative values. In addition, RWM can be used for predicting the closet
value of USD/AUD, which can help in generating high level of return from investment.
g) Identifying the risk implications of the investment on a daily basis:
Parameters
Portfolio Value $ 1,000,000.00
Average Return 0.030073862
Standard Deviation 0.000262395
Confidence Level 0.99
Calculations
Min Return with 99%
prob 0.029463441
Value of Portfolio $ 970,536.56
Value at Risk $ 29,463.44
Risk percentage 2.9%
From the relevant evaluation, it can be detected that the risk percentage of investment
is anticipated to be at the levels of 2.9%, where the value amounts to $29,463.44. The risk
calculation is based on the min return with 99% probability with 0.99 confidence level.

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-7.156171473
-6.446991671
-5.737811869
-5.028632067
-4.319452265
-3.610272463
-2.901092661
-2.191912859
-1.482733057
-0.773553255
-0.064373453
0.644806349
1.353986151
2.063165953
2.772345755
3.481525557
4.190705359
4.899885161
5.609064963
6.318244765
7.027424567
More
0
100
200
300
400
500
600
700
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
Histogram
Frequency Cumulative %Bin
Frequency
The above histogram indicates that the return of USD/AUD falls under -7.156171473
to 7.02424567, which indicates about the maximum and minimum returns of USD/AUD.
Moreover, the investment of 1 million can generate a return in accordance with the above
mentioned maximum and minimum limits (Bruno and Shin 2015).
Conclusion:
The different types of investment strategies are mainly evaluated in the above
assessment, which can help investors to forecast the values of currency and make adequate
investment decisions. The trend of USD/AUD is evaluated, which indicates a downtrend is in
progress. Thus, investors can use sell strategies to improve their returns from the currency
trades.

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References and Bibliography:
Barroso, P. and Santa-Clara, P., 2015. Beyond the carry trade: Optimal currency
portfolios. Journal of Financial and Quantitative Analysis, 50(5), pp.1037-1056.
Beckmann, E. and Stix, H., 2015. Foreign currency borrowing and knowledge about
exchange rate risk. Journal of Economic Behavior & Organization, 112, pp.1-16.
Bruno, V. and Shin, H.S., 2015. Capital flows and the risk-taking channel of monetary
policy. Journal of Monetary Economics, 71, pp.119-132.
Carfi, D. and Musolino, F., 2014. Dynamical stabilization of currency market with fractal-
like trajectories. Scientific Bulletin of the Politehnica University of Bucharest, Series A-
Applied Mathematics and Physics, 76(4), pp.115-126.
Chernov, M., Graveline, J. and Zviadadze, I., 2018. Crash risk in currency returns. Journal of
Financial and Quantitative Analysis, 53(1), pp.137-170.
Colacito, R., Croce, M.M., Gavazzoni, F. and Ready, R., 2018. Currency risk factors in a
recursive multicountry economy. The Journal of Finance, 73(6), pp.2719-2756.
Corte, P.D., Riddiough, S.J. and Sarno, L., 2016. Currency premia and global
imbalances. The Review of Financial Studies, 29(8), pp.2161-2193.
Du, W. and Schreger, J., 2016. Local currency sovereign risk. The Journal of Finance, 71(3),
pp.1027-1070.
Glick, R. and Rose, A.K., 2016. Currency unions and trade: A post-EMU
reassessment. European Economic Review, 87, pp.78-91.

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Guesmi, K., Moisseron, J.Y. and Teulon, F., 2014. Integration versus segmentation in Middle
East North Africa equity market: Time variations and currency risk. Journal of International
Financial Markets, Institutions and Money, 28, pp.204-212.
Jensen, J.B., Quinn, D.P. and Weymouth, S., 2015. The influence of firm global supply
chains and foreign currency undervaluations on US trade disputes. International
Organization, 69(4), pp.913-947.
Lai, E.L.C. and Yu, X., 2015. Invoicing currency in international trade: An empirical
investigation and some implications for the renminbi. The world economy, 38(1), pp.193-229.
Lettau, M., Maggiori, M. and Weber, M., 2014. Conditional risk premia in currency markets
and other asset classes. Journal of Financial Economics, 114(2), pp.197-225.
Rba.gov.au. 2019. Historical Data. [online] Available at:
https://www.rba.gov.au/statistics/historical-data.html#exchange-rates [Accessed 3 Apr.
2019].