Comprehensive Analysis of USD/AUD Exchange Rate and Market Efficiency

This report provides a detailed analysis of the USD/AUD exchange rate, market efficiency, and associated risks. It begins with a graphical representation of compounded returns and applies the 68-95-99.7 rule to assess the relationship between actual and artificially created returns. The report assesses directional moves using statistical analysis and forecasts the currency's performance over a 30-day period to convince investors. The efficiency of the FX market is evaluated using the Random Walk Model and Autoregressive models, along with MAE and RMSE. A Value at Risk (VaR) analysis is conducted to assess potential financial risks over a 100-day period, and the report concludes by examining key variables such as exchange rate behavior, productivity impacts, and overvaluation considerations. This report aims to provide a comprehensive understanding of the USD/AUD exchange rate dynamics and its implications for international finance and investment strategies.

INTERNATIONAL
FINANACE

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
A. Graphically presenting the Compounded returns of USD/AUD.......................................1
B. Determining 68-95-99.7 Normal rule of thumb with specific evidences..........................2
C. Comparing artificially created returns with actual returns................................................4
D. Assessment for directional move with the influences of valid statistic.............................5
E. Convincing team for making investments as per 30 days of forecasted trend..................6
F. Analysing the Efficiency of FX market............................................................................7
G. Risk assessment analysis for 100 days..............................................................................9
H. Implicating international finance’s key variable exchange rate for distributional
characteristics and its market efficiency...............................................................................10
CONCLUSIONS............................................................................................................................10
REFERENCES..............................................................................................................................12

INTRODUCTION
Analysing the variations in the capital or currency market on an international segmentation
on which there are various organisations and economies which has have influences. In relation
with analysing the efficiency of market and economy there has been various models and methods
which have influences in the operational gains and activities of the firm. Australia has a sound
economy and the exchange rate is comparatively on the favourable state which defines economic
stability in nation. In the present report there will be implication of various models and tests to
analyse efficiency of AUD in compared with USD. Along with this, there will be discussion
based on benefits and drawbacks of data set in the forecasted trend analysis that will bring
accurate information among investors for their investing decisions.
A. Graphically presenting the Compounded returns of USD/AUD
To analyse the compound ate of return has been payable by S(USD/AUD) for having
effective determination of market efficiency. It has been analysed by considering the 15 years of
data set on daily basis, starts from 1 January 2003 to 9 August 2018. This is usually treated as the
percentage of the outcomes which determines the gains and losses incurred on the capital
analysis over the period (Wilson, and et.al., 2014). This is the most reliable and accurate analysis
of the data set in terms of identifying the effective rate of returns in the operations. Thus, in
analysing the compounded annual rate of return the data set of exchange rate of USD/AUD has
been measured as seen below:
years Annual compound rate
2003 1.001167
2004 1.000214
2005 0.999987
2006 1.000318
2007 1.000466
2008 0.999163
2009 1.001087
2010 1.000542
2011 1.000038
2012 1.000105
2013 0.999428
2014 0.999669
2015 0.999616
2016 0.999985
2017 1.000313
1

2018 0.999703
Interpretation: Considering the outcome which determined that there have been huge
movements in the interest rate outcome of the firm. Thus, as per making effective determination
of all the operations on which it can be said that, Australia dollars are comparatively adequate as
compared with USD. However, these outcomes determine that, there will be growth in the
profitability and economic level of the country as per rise in the exchange rate. Thus, nation will
retain effective revenue and the gains from exporting resources. There will be rise in the
economy as compatibility with the US dollars will bring rise in economy, enhance per capita
income as well as monitors operational requirements of firm.
B. Determining 68-95-99.7 Normal rule of thumb with specific evidences
To analyse the differences between normal and actual returns which has have bring
variations in the outcomes (Kim and Yazdian, 2014). Thus, as per analysing the operational
requirements and wants there will be suitable analysis of the data set after implicating Rule of
thumb. The main concept behind implicating this theory is for bringing the adequate information
and analysis on the data set
68.27% 95.45% 99.73%
2

Year μ σ Average
one
standard
deviation Average
2
standard
deviation Average
3
standard
deviation
2003 0.001167196 0.006677 -0.00551 0.007844 -0.01219 0.014522 -0.01886 0.021199
2004 0.000213993 0.008573 -0.00836 0.008787 -0.01693 0.017361 -0.02551 0.025934
2005
-
0.000236629 0.005209 -0.00545 0.004972 -0.01065 0.010182 -0.01586 0.015391
2006 0.000317518 0.005518 -0.0052 0.005836 -0.01072 0.011354 -0.01624 0.016872
2007 0.000466383 0.008245 -0.00778 0.008712 -0.01602 0.016957 -0.02427 0.025202
2008
-
0.000836962 0.015445 -0.01628 0.014608 -0.03173 0.030054 -0.04717 0.045499
2009 0.001086724 0.011112 -0.01003 0.012199 -0.02114 0.023311 -0.03225 0.034424
2010 0.000536836 0.008593 -0.00806 0.00913 -0.01665 0.017722 -0.02524 0.026315
2011 3.77502E-05 0.008995 -0.00896 0.009033 -0.01795 0.018028 -0.02695 0.027024
2012 0.000104948 0.005754 -0.00565 0.005859 -0.0114 0.011614 -0.01716 0.017368
2013
-
0.000572172 0.006427 -0.007 0.005855 -0.01343 0.012282 -0.01985 0.018709
2014
-
0.000330758 0.005667 -0.006 0.005337 -0.01167 0.011004 -0.01733 0.016671
2015
-
0.000383775 0.007547 -0.00793 0.007163 -0.01548 0.014711 -0.02303 0.022258
2016 -1.4807E-05 0.006875 -0.00689 0.00686 -0.01377 0.013736 -0.02064 0.020611
2017 0.000313241 0.005107 -0.00479 0.005421 -0.0099 0.010528 -0.01501 0.015636
2018
-
0.000297176 0.005067 -0.00536 0.00477 -0.01043 0.009837 -0.0155 0.014904
Histogram:
3

C. Comparing artificially created returns with actual returns
To identify the Guage differences in the two data set for analysing the efficiency in such
markets. As per analysing the outcomes there have been adequate differences and determination
of all the operational needs (Barnhart and van Es, 2015). Using this technique will be helpful in
demonstrating the performance made in a year of both portfolios. Below listed is the Foreign
Exchange annual rate of USD/AUD which has been compared with the artificial data set.
Year
Average annual
rate of return artificial
2003 0.0011672 0.00212
2004 0.000214 0.000345
2005 -0.000237 -0.00126
2006 0.0003175 0.00157
2007 0.0004664 0.000256
2008 -0.000837 -0.00094
2009 0.0010867 0.001024
2010 0.0005368 0.000635
2011 3.775E-05 3.78E-05
2012 0.0001049 0.000145
2013 -0.000572 -0.00064
2014 -0.000331 -0.00023
2015 -0.000384 -0.00032
2016 -1.48E-05 -0.00015
2017 0.0003132 0.000325
4

2018 -0.000297 -0.0002
Interpretation: By considering the above listed analysis on which the comparison of
Actaul return and artificially created portfolios. Thus, as per analysing the outcomes the FX rate
of country is comparatively near to the estimated rates. Moreover, in analysing the performance
of Actual returns on which it can be said that there is need to have strategic plans which will be
helpful for controlling economic condition and it will bring a stability in the performance.
D. Assessment for directional move with the influences of valid statistic.
It has been estimated that AUD will have economic growth in the coming period as this
currency will have effective growth in the future (Jaeger and Adair, 2014). Similarly, considering
the regular growth and rise in the currency level there will be effective rise in various operations.
it will bring the rise in GDP, reduces inflation, unemployment in country. Along with this, there
will be rise in the capital value in the international stock market and which will result in effective
growth of the nation.
TREND analysis:
5

As per analysing the efficiency of the market on which it can be said that the trend
determines that, there will be upward movement in the TREND line. Thus, on which there will
be increment in data set as per considering the 30 days of forecast. Direction of this graph and
Trend line is on upward movement which defined that there will be favourable rise in returns.
E. Convincing team for making investments as per 30 days of forecasted trend
To estimate the growth of currency in the next 30 days the below listed trend analysis will
be helpful as per having effective outcomes and determination of all the facts (Behizadeh, 2014).
Thus, this equation has been made as per considering the 15 year of data set of FX rate
USD/AUD with consideration of fruitful determination of all the operations. However, it will
have based on analysing the outcomes based on two hypothesis such as:
Ho: μ =0 (mean return to be zero)
H1: μ > 0, (mean return to be positive)
3922 0.7441 0.001885 equation forecast
3923 0.89296 0.8829
3924 0.89299 0.8829
3925 0.89302 0.8830
3926 0.89305 0.8830
3927 0.89308 0.8830
3928 0.89311 0.8830
3929 0.89314 0.8831
3930 0.89317 0.8831
6

3931 0.8932 0.8831
3932 0.89323 0.8831
3933 0.89326 0.8832
3934 0.89329 0.8832
3935 0.89332 0.8832
3936 0.89335 0.8832
3937 0.89338 0.8833
3938 0.89341 0.8833
3939 0.89344 0.8833
3940 0.89347 0.8834
3941 0.8935 0.8834
3942 0.89353 0.8834
3943 0.89356 0.8834
3944 0.89359 0.8835
3945 0.89362 0.8835
3946 0.89365 0.8835
3947 0.89368 0.8835
3948 0.89371 0.8836
3949 0.89374 0.8836
3950 0.89377 0.8836
3951 0.8938 0.8836
3952 0.89383 0.8837
Interpretation: Considering the above listed graph and trend analysis of the data set on
which growth of FX rate has been analysed. Thus, there will be growth in the rates of USD/AUD
in the future. Therefore, the estimation has been made as per analysing and ascertaining the
impacts of such data which brings positive outcomes. However, as per considering the outcomes,
here alternative hypothesis has been considered which states that mean value will be positive
(H1: μ > 0, (mean return to be positive)).
F. Analysing the Efficiency of FX market
In relation with analysing the FX market with appropriate rate of return and the
determination of the outcomes which will be based on implicating two models such as:
Random Walk Model:
This is the method and model which helps in analysing a path of succession of data set
and rise in the level of outcomes (Christensen and Knezek, 2017). There has been estimation
based on considering the true (+1) value and False (-1) on the data set.
7

Autoregressive of order model:
To estimate the time series on the data set which will be adequate in making proper
estimation in coming period.
MAE and RMSE:
Year
Average annual rate
of return artificial Differences
2003 0.001167196 0.00212 0.000952804
2004 0.000213993 0.000345 0.000131007
2005 -0.000236629 -0.00126 -0.001023371
2006 0.000317518 0.00157 0.001252482
2007 0.000466383 0.000256 -0.000210383
2008 -0.000836962 -0.000937 -0.000100038
2009 0.001086724 0.001024 -6.27238E-05
2010 0.000536836 0.0006345 9.76637E-05
2011 3.77502E-05 0.0000378 4.98027E-08
2012 0.000104948 0.000145 4.00519E-05
2013 -0.000572172 -0.000642 -6.98275E-05
2014 -0.000330758 -0.000225 0.000105758
2015 -0.000383775 -0.000315 6.87749E-05
2016 -1.4807E-05 -0.000145 -0.000130193
2017 0.000313241 0.000325 1.17591E-05
2018 -0.000297176 -0.000195 0.000102176
MAE 0.000272441
RMSE 0.000478191
8

G. Risk assessment analysis for 100 days
To analyse the forecasts of the data set there has been determination of Value at risk
analysis which will be helpful in bringing the adequate outcomes such as: (Calculating value at
risk, 2012)
Year μ Portfolio
var
historical
2003 0.001167 5.454371 0.008576
2004 0.000214 -0.90434 -0.00142
2005 -0.00024 -0.74525 -0.00117
2006 0.000318 0.68081 0.00107
2007 0.000466 -0.55723 -0.00088
2008 -0.00084 -0.77017 -0.00121
2009 0.001087 2.024311 0.003183
2010 0.000537 14.22076 0.022359
2011 3.78E-05 0.359704 0.000566
2012 0.000105 -0.18342 -0.00029
2013 -0.00057 1.729883 0.00272
2014 -0.00033 0.861854 0.001355
2015 -0.00038 25.91851 0.040752
2016 -1.5E-05 -0.04727 -7.4E-05
2017 0.000313 -1.05406 -0.00166
2018 -0.0003 -0.18901 -0.0003
0.001572 sum
As per analysing the daily risks and evaluating the efficiency of the market in this regard
which is required to have effective analysis which ill be based on considering various stages:
Risk Overview: There will be risks relevant with the possible returns which brings the big
differences and original expectance to the operations. in capital market these risks are generally
higher as investors estimate the returns a firm will make them in a period. However, to analyse
the risks which will based on three categories such as low risk, medium risk and high risk.
Beta: Analysing the beta value bring the volatility in comparison of the data base with
international market.
Alpha: It demonstrates the outperformance with respect to its benchmark as well as
differences in actual returns and returns on one would analyse actual efficiency.
R-Square: It ascertains the similarity between the performance of the data set with the
budgeted data base.
9

Standard deviation: To analyse fund’s return this method will be helpful in addressing
the performance in the data base.
H. Implicating international finance’s key variable exchange rate for distributional characteristics
and its market efficiency.
By considering the time frame on which determination of various outcomes will be based
on making effective rise in the rates (Ling, 2016). Thus, there has been consideration of daily
exchange rates of USD/AUD in relation with having accurate estimation of data set. Moreover,
there has been various techniques which will be helpful in addressing the risks such as:
Exchange rate behaviour:
The exchange rate is relative price in the open economy where the purchase and sale of
security can be done without considering any issues and obstacles.
Impacts of productivity changes:
There has been impacts on the share value of the entity as the product line and services
offered by them has been changes. Considering the foreign exchange rates on which impacts of
various economic factors which affects changes in valuation or returns. Therefore, applications
various models bring information relevant with the rapid growth of economy or the valuation of
returns.
Overvaluation:
Ascertaining the PPP model on which it can be determines that the currency is
overvalued or undervalues. It will be beneficial in analysing all the necessary outcomes that will
be helpful and adequate in making proper changes and bringing operational benefits.
CONCLUSIONS
On the basis of above report it can be said that, USD/AUD FE rates will have satisfactory
rise in the rates in near further as per analysing the effective outcomes. This report had analysed
all the outcomes such by implying various models like random walk model, compound interest,
autoregressive of order model etc. Thus, implication of these models has helped in estimating the
growth in rates and the positive outcomes from the data set.
10