EXECUTIVE SUMMARY:
Exchange rate forecasting remains an ever-evolving domain with immense practical
implications that novel techniques are constantly proposed, from realm of purely technical
analysis to the recent prevalence of machine learning. Nonetheless, a comprehension of the
fundamental economic factors that directly drive a currency pair movement is crucial in adding
an extra layer of conviction to forecasts generated by various quantitative methods
Our report thoroughly examine the economic variables that potentially moderate our assigned
currency pair of AUD/USD by processing quarterly time series over the period 2000Q12021Q1 under the theoretical frameworks of purchasing power parity (PPP), a customized
macro-based model, and a composite model with the previous two models. After literaturebased justifications about the inclusion of different variables in our models are made, data
manipulation techniques are performed under the hood to reduce statistical bias and enhance
our models’ overall predictive capacity before the application of multiple regression.
Observed from the regression outputs, considering the overall statistical insignificance of both
the PPP model and the macro-based model that renders the forecasts of AUD/USD from
2021Q2 to 2021Q4 relatively inaccurate, the composite model also fails to deliver a superior
forecast result despite a sound theoretical weighting scheme. Emerging from our report, the
only relevant variable for forecasting the movement of AUD/USD is gold, reasonably with
Australia as a heavy commodity-reliant economy.
In extension, our report negated the profitability of covered interest arbitrage strategy in the
pair AUD/USD given a realistic context in alignment with the previous study. Additionally,
while having rejected the validity of PPP theory in the short run between Australian and
American economies, our report emphasizes that PPP indeed holds in the longer timeframe and
as such, practitioners might have increased confidence in long-run forecasts of foreign
exchange.
1
QUESTION 1
To estimate foreign investment opportunities as an Australian firm, this paper would evaluate
AUD/USD spot rate by analyzing its determinants before predicting its value from 2021-Q2 to
2021-Q4. Firstly, we will perform back-testing on two economic models: Purchasing Power
Parity (PPP) and macroeconomic model, by using historical quarterly data of relevant countries
from 2000-Q1 to 2021-Q1, including AUD/USD spot rate, the inflation rate in the PPP model,
short- and long-term interest rate, real GDP growth rate, money supply (M3), current account
balance (CAB), gold, crude oil, and stock price in a macroeconomic model. Then, a composite
model is finalized by systematically weighting the previous two models.
1.1. PURCHASING POWER PARITY (PPP) MODEL
The PPP theory predicates the exchange rate between two currencies should be equal to their
countries’ price levels ratio (Eun & Resnick 2020). This inevitably requires that when the home
country confronts higher inflation, its currency must be depreciated against the foreign
currency. Reasonably, with prohibitive domestic prices, demand for domestic goods declines,
and demand for foreign products increases because of their relative cheaper prices. Hence, the
demand for foreign currency to buy foreign products is higher, leading to an appreciation for
foreign currency and depreciation of home currency (Grewal & Sheehan 2005).
Overall, the first model will examine whether the differences in the inflation rate of the US and
Australia influence the changes in AUD/USD spot rate (Appendix 1). Afterwards, projections
of the exchange rate of AUD/USD for the next three periods (from 2021-Q2 to 2021-Q4) will
be included.
1-period (t-1) lagged variable is used as an independent variable because the current level of
the exchange rate is strongly determined by its historical level of inflation rate, which is
discussed previously. Besides, the lagged independent variable is applied since one quarter is
an ideal time for the impact of any alters in inflation rate on exchange rate to be detected
(Cheung et al. 2017).
The regression formula for the PPP model which is reproduced from the Economics journal of
Abdurehman and Hacilar 2016:
𝐸𝑓 = 𝛽0 +𝛽1 ×
(
1 + 𝐼𝐴𝑈𝑆
)
1 + 𝐼𝑈𝑆 ― 1
+𝜇
(1)
(𝑡 ― 1)
2
Table 1.
Description of variables in model (1)
Variable
Description
Ef
Percentage change in AUD/USD at time t from (t–1)
β0
Intercept
β1
Slope coefficient
IAUS and IUS
1 + 𝐼𝐴𝑈𝑆
―1
1 + 𝐼𝑈𝑆
Inflation rate of Australia and the US, respectively
𝜇
Error term
(
)
The lagged inflation differential between Australia and the US at time (t– 1)
Hypothesis testing:
H0: β0=0 (There is no relationship between the percentage change in AUD/USD and the lagged
inflation differential)
H1: β0≠0 (There is a relationship between the percentage change in AUD/USD and the lagged
inflation differential)
Table 2.
Estimate results of model (1)
Indep. Var
Constant
1 + 𝐼𝐴𝑈𝑆
(
1 + 𝐼𝑈𝑆
Dep. Var: Ef
)
―1
Coefficient
Standard Error
t statistic
P value
0.0044
0.0067
0.6576
0.5127
-0.0029
0.0033
-0.8787
0.3821
Observations
84
R-squared
0.00933
Adjusted R-squared
-0.00275
Standard Error
0.06117
Significance F
0.77213
Sample regression of model (1):
𝐸𝑓 = 0.0044 ― 0.0029 ×
(
1 + 𝐼𝐴𝑈𝑆
1 + 𝐼𝑈𝑆
)
―1
(𝑡 ― 1)
Firstly, β0=0.0044≠0 and β1=-0.0029≠1 so the PPP does not hold, indicating an arbitrage
opportunity of inflation rate differential.
3
Secondly, P-value=0.3821>α=0.05 so we do not reject H0 at a 95% confidence level. Therefore,
there is no relationship between the percentage change in AUD/USD and the lagged inflation
differential.
Next, β1=-0.0029 is valueless in this model since we would interpret if the lagged inflation rate
differential increases by 1%, the exchange rate decreases by 0.0029%, ceteris paribus, and
AUD would be depreciated against USD if we rejected H0.
Finally, a low R2 and adjusted R2 prove this PPP model has a weak prediction since only
0.933% of sample variation in the changes in AUD/USD is explained by variation in the lagged
inflation differential, whereas remaining 99.067% is because of unobserved factors.
1.2. EXCHANGE RATE FORECAST BY PPP MODEL
Forecasts of the next three periods shall be based on the equation: 𝐸 = 𝐸𝑡 ― 1 × (1 + 𝑃𝑡) (2)
Table 3.
Description of variables in equation (2)
Variable
Description
𝐸
The predicted AUD/USD at time t
𝐸𝑡 ― 1
The actual AUD/USD at time (𝑡 ― 1)
𝑃𝑡
The predicted percentage of change in AUD/USD at time (𝑡 ― 1)
Table 4.
Exchange rate forecast by PPP model
Time
Period
Predict % Change
in AUD/USD
Predicted Spot Rate
Actual Spot Rate
Predicted - Actual
2021-Q2
0.5188%
0.7641
0.7518
0.0123
2021-Q3
0.4897%
0.7555
0.7206
0.0349
2021-Q4
0.5472%
0.7245
0.7256
-0.0011
The PPP model forecasts AUD/USD would appreciate in 2021-Q2, Q3, and Q4 (Table 4). Since
the changes in AUD/USD are primarily caused by other factors (mentioned previously), the
actual exchange rate is up to 3.4% different from the predicted one which is operated from the
PPP model (Table 4). In June 2021, the USD rapidly strengthens after Fed’s mid-month FOMC
meeting that amazed the market a by demonstrating a majority of 18 FOMC members predicted
50bps of rate hike in 2023 and 7 of them forecast a hike in 2022; hence, AUD/USD significantly
4
depreciate in 2021-Q2 (Attrill 2021, Figure 1). Furthermore, due to the spread of COVID-19
Delta variant and extended renewed lockdown, Australia confronts a significant decline in
activity in 2021-Q3, leading to a huge fall in GDP and depreciation of AUD (CBS 2021).
Consequently, a big difference between predicted and actual exchange rates is created.
Figure 1.
The movement of AUD/USD in Jun 2021
1.3. MACROECONOMIC MODEL
This model incorporates the macroeconomic variables as follows:
Interest Rate Differential: Dekle et al (2002) argued that the interest rate increase could
directly trigger the currency appreciation, whereas, in the revisionist view, a higher interest rate
would make currency depreciate due to a weaker financial position, hence a higher exchange
rate risk premium. According to Auten (1963), exchange rate and investment inflows
established a negative relationship based off the direction which the differentials favor.
Besides, the empirical results from Hsing (2015) also indicate interest rates play a significant
role in AUD/USD exchange rate movements, altogether justifying for interest rate differential
as a relevant variable
5
Change in Current Account Balance (CAB): CAB essentially measures the sum of a
country’s trade balance and its net foreign income, exerting a crucial role in orchestrating the
capital flow among countries (Song 1997). Such components are in turn largely conditional
upon exchange rate, which fundamentally moderates the production levels and consumer
demands (Bullock et al. 1993). Resultantly, inclusion of CAB as a relevant variable would have
favorable impacts for our model when an inverse relationship between a country’s exchange
rate and its CAB is entirely justifiable (Altayligil & Çetrez 2020).
Real GDP Growth Rate: The real GDP growth rate has both a positive and inverse
relationship with the exchange rate, depending on the changes in the financial or trading market
(Karahan 2020). For financial market, a higher GDP growth rate indicates a higher income
which accelerates demand for consumption (Callen 2020). In modern developed economies,
such as Australia and the US, stronger economic growth causes demand-driven inflation, which
is the most important determinant of inflation, leading to a depreciation of domestic currency,
or exchange rate appreciation (Girdzijauskas et al 2022)
Change in Money Supply (M3): The central bank conducts monetary policy by controlling
the money supply to achieve the financial and output stability of the country (Kim 2016). When
implementing expansionary monetary policy, central bank promotes money supply and lowers
domestic interest rate, motivating local investors to spend capital overseas to earn a higher
profit (OCR 2015). Thus, demand for domestic currency decrease, leading to depreciation.
Therefore, money supply has a positive relationship with exchange rate.
Change in Oil Price: Numerous research have revealed a strong relationship between oil price,
a crucial commodity that so far still powers the major proportion of economic activities (Rapier
2020), and exchange rates in the long-term horizon (Beckmann 2020. With an increase in oil
price, profound theories of Buetzer (2012) imply the strength of USD is expected to be
weakened against AUD, hiking the AUD/USD rate. It would therefore be highly beneficial to
our model with oil price as a variable.
Change in Gold Price: Australian economy is heavily reliant on exportations of mining and
commodities, justifying the label of its exchange rate AUD/USD as a commodity currency
(Zou et al. 2017). As gold has long been considered instrumental in partially hedging market
risks along with other commodities (Manuj 2021), it is reasonable to extrapolate a positive
relationship between gold’s price and the movement of other commodity currencies as AUD.
Empirically, the USD-based gold price and AUD/USD have been proven to yield remarkable
6
impacts on the direction of Australian minerals sector, and there indeed established a
significantly positive relationship between the gold price and AUD/USD (Haque et al. 2015).
Change in Stock Price: The stock prices movement could moderate exchange rates as the
wealth and money demand of investors depend on the performance of the stock market (Gavin
1989). For instance, the investors’ herding behavior and loss of confidence due to economic
and political instability in a financial crisis may disrupt demand for assets, causing stock prices
movements that affect the exchange rates (Cakan & Ejara 2013). In contrast, stock prices
expectably increase because of economic growth prospects would attract foreign investors in
terms of capital; thus, increasing demand for domestic currency and vice versa, causing the
exchange rate to change (Cakan & Ejara 2013). In this model, a huge financial flow from a
close and cooperative economic relationship between the US and Australia is ideal to intensify
the impact of the stock price in AUD/USD (USTR 2021), expecting a significant correlation in
accordance with Imam et al. (2012).
The regression formula for the Macroeconomic model:
𝐸𝑋𝑅𝑡 = 𝛽0 + 𝛽1 × 𝑆𝐼𝑁𝑇𝐷𝑡 ― 1 + 𝛽2 × 𝐿𝐼𝑁𝑇𝐷𝑡 ― 1 + 𝛽3 × 𝐼𝑁𝐹𝐷𝑡 ― 1 + 𝛽4 × 𝐺𝐷𝑃𝐺𝐷𝑡 ― 1 +
𝛽5 × 𝐶𝐴𝐵𝐴𝑈𝑆𝑡 ― 1 + 𝛽6 × 𝐶𝐴𝐵𝑈𝑆𝐴𝑡 ― 1 + 𝛽7 × 𝑀𝑆𝐴𝑈𝑆𝑡 ― 1 + 𝛽8 × 𝑀𝑆𝑈𝑆𝐴𝑡 ― 1 + 𝛽9 ×
𝐺𝑂𝐿𝐷𝑡 ― 1 + 𝛽10 × 𝑂𝐼𝐿𝑡 ― 1 + 𝛽11 × 𝑆&𝑃𝐴𝑆𝑋200𝑡 ― 1 + 𝛽12 × 𝑆&𝑃500𝑡 ― 1 +𝜇 (3)
Table 4.
Description of variables and the use of lagged model in model (3)
Variable
Description
Lagged Model
Short-term interest rate differential between
Australia and United States, which is
SINTD
LINTD
calculated by subtracting United States’
short-term interest rate from Australia’s
Lagged interest rate directly moderates the
short-term interest rate, based on Golit et al.
impacts of monetary policy due to the
(2019).
continually adjusting expectations of market
Long-term interest rate differential between
participants, and monetary policy in turn alters
Australia and United States, which is
exchange rate in drastic measures (Sack &
calculated by subtracting United States’
Wieland 2000).
long-term interest rate from Australia’s
long-term interest rate, based on Golit et al.
(2019).
INFD
Inflation rate differential between Australia
Use of lagged inflation is empirically
and United States, which is calculated by
informative in anticipation of future inflation
7
subtracting United States’ inflation rate
and could potentially predict exchange rates
from Australia’s inflation rate, based on
with increased precision (Sarno & Schmeling
Golit et al. (2019).
2014).
“Order
flow
is
strongly
related
to
fundamentals, with GDP components as key
data of analysis, can profitably forecast
GDP growth rate differential between
Australia and United States, which is
GDPGD
calculated by subtracting United States’
GDP growth rate from Australia’s GDP
growth rate.
exchange rate movements. We take this to be
robust evidence that can bridge the micromacro divide, in the sense that current and
future exchange rates are not random walks
but are indirectly determined by economic
fundamentals” (Rime et al. 2007). Since the
availability/ publication of GDP data usually
lags market developments, the use of lagged
GDP makes logical sense.
CABAUS
CABUSA
Change in Current Account Balance of
Australia
Change in Current Account Balance of
United States
Transformation of data into percentage terms
enhances
MSAUS
MSUSA
the
(statistical)
normality
of
Change in Money Supply (M3) of Australia
variables, which reduces probabilities of type
Change in Money Supply (M3) of United
I & II errors and simultaneously enhances the
States
model’ predictive capacities (Osborne 2002).
Used of those lagged variables are reasonable
GOLD
Change in gold price
OIL
Change in oil price
S&PASX200
Change in S&P/ASX 200 index
S&P500
Change in S&P 500 index
since macro-economic factors are leading by
nature and necessitates time for gradual
adjustment based on market participants’
changing interpretations
8
Table 6.
Estimate results of model (3)
Indep. Var
Dep. Var: EXR
Coefficient
Standard Error
t statistic
P value
Constant
-0.0196
0.0166
-1.1825
0.2410
SINTD
0.0088
0.0106
0.8268
0.4111
LINTD
-0.0253
0.0243
-1.0427
0.3007
INFD
0.0062
0.0071
0.8652
0.3899
GDPGD
0.6134
0.8977
0.6834
0.4966
CABAUS
0.0076
0.0085
0.8941
0.3743
CABUSA
-0.0248
0.0376
-0.6608
0.5109
MSAUS
0.9287
0.6318
1.4700
0.1460
MSUSA
0.1048
0.5595
0.1873
0.8520
GOLD
0.2691
0.1033
2.6044
0.0112
OIL
0.0096
0.0447
0.2153
0.8302
S&PASX200
0.0220
0.1612
0.1367
0.8916
S&P500
-0.0580
0.1444
-0.4016
0.6892
Observations
83
R-squared
0.18613
Adjusted R-squared
0.04661
Standard Error
0.05999
Significance F
1.33408
Sample regression of model (3):
𝐸
= ―0.0196 + 0.0088𝑆𝐼𝑁𝑇𝐷𝑡 ― 1 ― 0.0253𝐿𝐼𝑁𝑇𝐷𝑡 ― 1 + 0.0062𝐼𝑁𝐹𝐷𝑡 ― 1 + 0.6134
𝐺𝐷𝑃𝐺𝐷𝑡 ― 1 + 0.0076𝐶𝐴𝐵𝐴𝑈𝑆𝑡 ― 1 ― 0.0248𝐶𝐴𝐵𝑈𝑆𝐴𝑡 ― 1 + 0.9287𝑀𝑆𝐴𝑈𝑆𝑡 ― 1
+ 0.1048𝑀𝑆𝑈𝑆𝐴𝑡 ― 1 + 0.2691𝐺𝑂𝐿𝐷𝑡 ― 1 + 0.0096𝑂𝐼𝐿𝑡 ― 1 + 0.0220𝑆&𝑃200
𝑡 ― 1 ― 0.0580𝑆&𝑃500𝑡 ― 1
Since only P-valueGOLD=0.0112<α=0.05, we reject H0 at 95% confidence level. Therefore,
there is a relationship between the change in gold price and the change in AUD/USD. Next,
β9=0.2691 indicates if the change in gold price increases by 1%, the exchange rate increases
by 0.2691%, ceteris paribus, and AUD would be appreciated against USD.
Whereas P-value of other independent variables greater than α=0.05, so we do not reject H0 at
95% confidence level. Thus, there is no relationship between the change in AUD/USD and
other lagged explanatory variables (short- and long-term interest rate differentials, inflation
9
rate differentials, GDP growth rate differentials, change in CAB of the US and Australia,
change in money supply of the US and Australia, change in oil, and stock price)
Regarding model fitness, a low R2 and adjusted R2 prove this macroeconomic model has a
weak prediction since only 18.613% of sample variation in the changes in AUD/USD is
explained by those independent variables, whereas remaining 81.387% is because of
unobserved factors.
Nonetheless, those statistics of the macroeconomic model are higher than those of the PPP
model (R2Macro=0.1861>R2PPP=0.0391; adjusted R2Macro=0.0466>adjusted R2PPP=0.0273)
indicating the model would improve the fitness level when adding more significant
macroeconomic variables.
1.4. EXCHANGE RATE FORECAST BY MACROECONOMIC MODEL
Table 7.
Exchange rate forecast by macroeconomic model
Time
Period
Predicted Change
in AUD/USD
Predicted Spot Rate
Actual Spot Rate
Actual - Predicted
2021-Q2
-0.0206
0.7446
0.7518
0.0072
2021-Q3
-0.0031
0.7494
0.7206
-0.0288
2021-Q4
-0.0323
0.6973
0.7256
0.0283
Contrast to the PPP model, the Macroeconomic model forecasts AUD/USD would depreciate
in 2021-Q2, Q3, and Q4, which has the same trend with the actual data, except 2021-Q4 (Table
7). In October 2021, the AUD/USD gains 4% compared to the previous month because of the
dramatically rise of commodity prices, especially thermal coal prices, and a diminishing in VIX
index from 25 to 15, which undermined the USD (Attrill 2021, Figure 2). Additionally, because
the changes in AUD/USD are mainly caused by other elements (discussed previously), the
actual exchange rate is up to 2.9% different from the forecasted one which is produced from
the Macroeconomic model (Table 7).
10
Figure 2.
The AUD/USD in October 2021
1.5. COMPOSITE MODEL
Bates and Granger (1969) advocated the use of a composite forecast model that potentially
offset the demerits of individual models in FX forecasting. By applying a weighting scheme as
in Stock and Watson (2001) where mean squared forecast errors (MSE) are accounted for,
forecasts with increased accuracy could be feasibly obtained.
Specifically, a low MSE value represents a better-performing model that should be more
substantially weighted and contrariwise:
Firstly, evaluation of input models, namely PPP model and Macro model for our combined
forecast is necessary.
11
Table 8.
Comparison between the PPP model and the Macroeconomic model
PPP model
Macroeconomic Model
Adjusted R2
0.0093
0.0466
MSE
0.0039
0.0030
Significance F
0.3821
0.2196
Considering adjusted R square, Macro model substantially outperformed PPP, since more
sample variations in the changes of AUD/USD could be attributed to the variations of its
variables relative to that of PPP (4.66% > 0.0093%). Despite by a small margin only, better
performance could also be seen in the lower MSE of macro model (0.3%<0.39%).
However, a predominant outperformance of macro model to PPP model was negated since their
significance F metrics was surpassed by that of PPP, with the majority of the macro model’s
variables being statistically insignificant, with the exception of gold variable.
Reasonably, macro model has been derived as of heavier weights.
Table 9.
Steps to derive the weight
PPP model
Macroeconomic Model
MSE
0.0039
0.0030
1/MSE
256.4103
333.3333
=Weight
0.4994
0.5006
Table 10.
Composite model output
Forecasted values
of AUD/USD
PPP Model
Macroeconomic Model
2021-Q2
0.7641
0.7446
2021-Q3
0.7555
0.7494
2021-Q4
0.7245
0.6973
Composite Model
= 0.4994*0.7641 + 0.5006*0.7446
0.7543
= 0.4994*0.7555 + 0.5006*0.7494
0.7524
= 0.4994*0.7245 + 0.5006*0.6973
0.7109
Based on the output of composite model as above, the composite model has failed to
consistently outperform the individual input models in projection of AUD/USD movement. In
12
2021-Q2, while PPP model prescribed an appreciation of AUD by 0.51% and Macro model
contradicted with a depreciation of 2.05%, composite model managed to deliver the closest
estimate to reality (-0.77% against –1.1%). However, in upcoming periods of 2021-Q3&Q4,
Macro model and PPP models have respectively outperformed the composite one, since their
forecast outputs matches the actual changes more closely as shown below.
Table 11.
The actual and predicted % change in exchange rate from three models.
Predicted % Change
from 2021-Q1
Actual % Change
from 2021-Q1
PPP Model
Macroeconomic Model
Composite Model
2021-Q2
0.51%
-2.05%
-0.77%
2021-Q3
-0.62%
-1.42%
-1.02%
2021-Q4
-4.70%
-8.27%
-6.49%
2021-Q2
-1.10%
2021-Q3
-5.21%
2021-Q4
-4.55%
With the overall statistical insignificance observed in both input models (Table 11), it is
reasonable that weighting scheme of any methods could hardly produce a drastically better
performing model, as is with our composite model.
1.6. LIMITATION
Due to the limited data frequency, the quarterly data is selected for all variables in PPP and
macroeconomic models, which inevitably limit the our sample size. Moreover, the data is
collected from 2000 which is relatively old to increase the models’ number of obervations
because larger sample size allows a more precise estimate results, facilitates the assessment of
the sample representativeness and genralizes the results (Biau et al. 2008). However, the old
data series might not improve the generalizability of the outcome and the understanding of
causal relationship, especially between floating exchange rate and its determinants.
13
QUESTION 2
Proof of relevant data could be found in appendix section.
Table 12.
Calculations without taking transaction costs into consideration.
Spot Rate: AUD/USD
0.74599
Forward Rates:
AUD/USD
1. 90-day period
Bid
Ask
Mid
0.74993
0.74596
2. 180-day period
0.75106
0.742
0.7430
9
AUD Interest (Annual)
USD Interest (Annual)
Assumption: 360 days
per compounding period:
AUD Interest (Daily)
USD Interest (Daily)
0.10%
0.50%
Since the interest in the
US is more lucrative than
that in Australia:
Assumption: initial
balance of
Conversion into USD for
offshore investing:
0.0003%
0.0014%
1000000
Conversion back to AUD
by use of forward
contract:
Profit (AUD) from such
an attempt at CIA
Profit (AUD) from simply
investing onshore
AUD
= 1000000 * 0.74599
745990
If the investment period
is:
The ($) balance becomes:
0.74708
USD
90 (days)
180 (days)
= 745990 * ( 1 + 0.0014% * 90)
= 745990 * ( 1 + 0.0014% * 180)
746929.9474
747869.8948
= 746929.9474 / 0.74596
= 746929.9474 / 0.74708
1001300.267
999799.1479
= 1001300.267 - 1000000
= 999799.1479 - 1000000
1300.267
-200.8521
= 1000000 * (0.0003% * 90)
= 1000000 * (0.0003% * 180)
270
540
The assumption of zero transaction costs results in a highly profitable trade of 1300.267 AUD
for covered interest arbitrage over the 90-day period. Reasonably, the forward rates are
14
sufficiently discounted in this case for investors to convert their USD back to AUD after having
compounded their profits in the American higher-yielding environment. However, for 180-day
period, a loss has incurred due to the unfavorably high forward rates that renders the conversion
back to AUD prohibitively costly and thus unprofitable.
Table 13.
Calculations when transaction costs are taken into consideration.
Spot Rate: AUD/USD
Forward Rates:
AUD/USD
1. 90-day period
2. 180-day period
AUD Interest (Annual)
USD Interest (Annual)
Assumption: 360 days
per compounding period:
AUD Interest (Daily)
USD Interest (Daily)
Since the interest in the
US is more lucrative than
that in Australia:
Assumption: initial
balance of
Conversion into USD for
offshore investing:
0.74599
Bid
Ask
Mid
0.74993
0.75106
0.742
0.74309
0.74596
0.74708
0.10%
0.50%
0.0003%
0.0014%
1000000
= 1000000 * 0.74599
745990
If the investment period
is:
the ($) balance becomes:
Conversion back to AUD
by use of forward
contract:
Profit (AUD) from such
an attempt at CIA
Profit (AUD) from
simply investing onshore
AUD
USD
90 (days)
180 (days)
= 745990 * (1 + 0.0014% * 90)
= 745990 * (1 + 0.0014% * 180)
746929.9474
747869.8948
= 746929.9474 / 0.74993
= 746929.9474 / 0.75106
995999.5565
994501.0351
= 995999.5565 - 1000000
= 994501.0351 - 1000000
-4000.4435
-5498.9649
= 1000000 * (0.0003% * 90)
= 1000000 * (0.0003% * 180)
270
540
15
With transaction costs, both strategies across time horizon have failed to yield a profit
compared to onshore investing. Essentially, the transactions costs are too significant to be fully
compensated by the gains exploitable in the higher-yielding environment.
Conclusively, there are practically no opportunities for covered interest arbitrage considering
the current quotations. A more critical setting that involves political risk factors and tax
differentials would add further convictions to the unprofitability of such a strategy (Eun &
Resnick 2020).
QUESTION 3
DOES PURCHASING POWER PARITY HOLD IN THE MARKET?
As introduced question 1, the PPP regulates that: a unit of currency in one country can purchase
the same quantity of goods as a foreign currency in another country at the concurrent exchange
rate and therefore, similar goods from differing countries can be used in as a base comparison
to determine the purchasing power of their respective currencies (Taylor & Taylor 2004). This
generally enables prompt comparisons of the current exchange rate and purchasing power.
As one of its fundamental flaws, PPP accepts “Ceteris Paribus” and “The Law of One Price”
(Vo & Vo 2022), which assumes that external costs such as transportation or taxes do not occur
between the 2 trading markets, hence cost of goods cannot be altered by restrictions on trade
of any kind. Realistically, costs of goods can fluctuate for the buyer or seller based on what
restriction is being placed on the traded goods. To illustrate, implementing tariffs will increase
the cost of goods for the import country in comparison to the exporting country. The law of
one price also does not include the costs of non-tradable inputs such as rent of distribution
points or minor differences in the production process, all of which contribute to different costs
of goods (Schmitz 2012). As the PPP does downplay these variables, it is highly unlikely to
hold across markets.
To better capture the theoretical implications of PPP, economists have reinterpreted PPP into
a long-run theory where PPP is not required to hold in a single point in time, instead PPP is reevaluated over an extended period to determine estimates of the movement of currency values
in exchange (Schmitz 2012).
Before discussing the validity of PPP between the AUD/USD it would be useful to note that
PPP’s volatility is more nuanced when being applied to 2 countries with different developing
16
characteristics. The macroeconomic factors of developing countries such as inflation, currency
stability, growth rates and more are too volatile compared to developed countries thus only
exacerbating PPP’s flaws as mentioned above (Joseph 2005).
When discussing whether purchasing power parity holds between Australian and American
markets, the exchange rate was therefore ideally contrasted against the relative price of goods
between Australia and the USA presented as a ratio of the cost of goods, thus allowing for a
fairer comparison to the exchange rate costs for PPP analysis. As evidence against PPP in the
short-term, the figure 3-6 suggests data points that are too erratic to present reliable or accurate
representations of the PPP between both countries.
Figure 3.
Exchange rate and Relative Price of Australia and the US from Q1-2014 to Q4-2016
(OECD, 2022) (FRED, 2022)
17
Figure 4.
Exchange rate and Relative Price of Australia and the US from Q1-2016 to Q4-2018
(OECD, 2022) (FRED, 2022)
Figure 5.
Exchange rate and Relative Price of Australia and the US from Q1-2018 to Q4-2020
(OECD, 2022) (FRED, 2022)
18
In contrast, the long-term relationship, approximated over a 118-year period between the
relative price and exchange rate (Figure 6), displays a more noticeable trend where both
variables show a positive correlation and downtrends in their movements. Potentially, PPP can
be argued to be of use for the mega-trend exchange rate estimation.
On balance, the deviations existing within the co-movements of both variables are observably
significant, which could be, as previously stated, attributed to the bold assumptions regarding
the immobility of costs of goods & services across countries & currencies. In conclusion, the
limitations of the PPP outweigh its potential in practical terms, and evidence in AUD/USD
movements implies PPP’s failure to hold in the market in a short-term horizon. Still, there is
clear potential for long term uses of the PPP as an effective estimator of exchange rate (Loh
Hoong 2008).
Figure 6.
Exchange rate and Relative Price of Australia and the US from 1901 to 2019
Source: Vo & Vo (2022)
19
APPENDIX
Appendix 1.
Sources of data
Variable
Details and Sources
Exchange Rate
The AUD/USD spot rate is collected quarterly from Q1 2000 to Q4 2021.
Source: Fred Economic Data
Inflation Rate
The growth rate of CPI is reproduced quarterly for Australia and the US from Q1
2000 to Q4 2021.
Source: Organization for Economic Co-operation and Development
Short-Term Interest Rate
The rate of short-term borrowings between the financial institutions or of issued
short-term government paper is collected from Q1 2000 to Q4 2021.
Source: OECD
Long-Term Interest Rate
The rate refers to government bonds maturing in 10 years, which is collected from
Q1 2000 to Q4 2021.
Source: OECD
Current Account Balance
The quarterly current account of Australia and the US is collected from the
Balance of Payments Analytic Presentation from Q1 2000 to Q4 2021.
Source: International Monetary Fund (IMF)
Real GDP Growth Rate
The quarterly Real Gross Domestic Product for Australia and the US is collected
from Q1 2000 to Q4 2021. After that, our team calculate its percentage change.
Source: Fred Economic Data
Money Supply
The M3 of Australia and the US is collected in the last month of a quarter as the
quarterly data is not available.
Source: Fred Economic Data
Oil Price
Gold Price
Stock Price
The series of crude oil (petroleum) price in USD/barrel, which is the average spot
price of Brent, Dubai and West Texas Intermediate equally weighed, is collected
in the last month of a quarter as the quarterly data is not available.
Source: Indexmundi
The series of gold price in USD/troy ounce, which is the Gold (UK), 99.5% fine,
London afternoon fixing, average of daily rates, is collected in the last month of
a quarter as the quarterly data is not available.
Source: Indexmundi
The series of S&P/ASX 200 and S&P 500 represents the stock price index for
Australia and the US respectively, which is collected in the last month of a quarter
as the quarterly data is not available.
Source: Investing.com
20
Appendix 2.
Annual interest rate in the US
Source: Trading Economics (2022)
Appendix 3.
Annual interest rate in Australia
Source: Trading Economics (2022)
21
Appendix 4.
Forward exchange rate AUD/USD
Source: Xignite (2022)
Appendix 5.
Spot exchange rate AUD/USD
Source: Xignite (2022)
22
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