Covid-19, Economy and FX: Past, Present, and Future
Analyzing pandemic economic effects and foreign exchange rate movements
This paper offers an extensive analysis of the economic impact of COVID-19 and the effects of the pandemic on several currencies. We then explain a predictive model that utilizes COVID-19 indicators to generate FX trading signals.
Over a portfolio of 28 currency pairs, the model applies a simple linear regression to the number of daily new COVID-19 cases in each country to, mainly, short the currencies against the USD. After optimizing the model’s hyper-parameters over the validation data, this model results in a +3.50 return, with 0.84 standard deviation, resulting in 4.01 return-to-risk (information ratio), over the out-of-sample period.
March 2022
| Mesirow Currency Management
COVID-19, Economy
and FX: Past, Present
and Future
The information contained herein is intended for institutional clients, Qualified Eligible Persons, Eligible Contract Participants, or the equivalent classification in the recipient’s jurisdiction, and is for informational
purposes only. Nothing contained herein constitutes an offer to sell an interest in any Mesirow Financial investment vehicle. It should not be assumed that any trading strategy incorporated herein will be profitable
or will equal past performance. Please see the disclaimer at the end of the materials for important additional information.
2
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Analyzing pandemic economic
effects and foreign exchange
rate movements
Abstract
This paper offers an extensive analysis of the economic impact
of the COVID-19 pandemic and the effects of the pandemic
on several currencies. We then explain a predictive model that
utilizes COVID-19 indicators to generate FX trading signals.
Over a portfolio of 28 currency pairs, the model applies a
simple linear regression to the number of daily new COVID-19
cases in each country to, mainly, short the currencies against
the USD. After optimizing the model’s hyper-parameters
over the validation data, this model results in a +3.50 return,
with 0.84 standard deviation, resulting in 4.01 return-to-risk
(information ratio), over the out-of-sample period.
Mehryar Emambakhsh
Vice President,
Senior Research Scientist
Anthony Deras
Investment Management Analyst
Richard Turner
Managing Director,
Head of Research
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
1
. Introduction
The COVID-19 pandemic created a non-homogenous effect on global economies and
is causing a divergence in economic recovery rates between developed and emerging
markets. This effect is evident even between advanced economies as each country
has unique lockdown policies, different underlying economies, different population
distributions, and varying levels of vaccination rates.
It is expected that this divergence among advanced economies (and between advanced
and emerging economies) will feed through to movements in exchange rates. The goals
of this paper are to 1) understand the effect of the COVID-19 pandemic on the different
types of economies 2) understand the correlation between COVID-19 statistics and FX
markets and 3) create a predictive model based on a COVID-19 infection rate signal to
opportunistically predict currency depreciations.
The rest of this paper is broken down as follows:
•
Section 2
– Outlines the background on pandemic economies and the economic
effects of COVID-19 on the selected countries of this study
•
Section 3
– Describes COVID-19 status of our selected countries (as of 12.31.21)
•
Section 4
– Considers the relationship between COVID-19 and FX rates
•
Section 5
– Describes our predictive model
•
Section 6
– Details the backtest and experimental results of our predictive model
•
Section 7
– Conclusion
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
2
. Economic effects of COVID–19
Background (“Money, Machines and Mayhem – The Economist”
1
)
The current pandemic has been unique in the measures taken to shut down parts of the
economy in order to limit the strain on healthcare systems. Past global pandemics can provide
valuable information towards the economic effects that result from containment measures and
the rebound in activity post-pandemic.
Relevant historic global pandemics include the smallpox outbreak of the 1870s and the Spanish
Flu of 1919. While the smallpox epidemic was relatively contained to Europe, the Spanish Flu
was more global and fatal. It is estimated that over 500 million individuals were infected by the
Spanish Flu and 50 million people died as a result.
The economies of WWI and WWII present similarities to the current pandemic economy
given the level of disruption to everyday life and activity. WWI led to the deaths of 20 million
people globally
2
while WWII was associated with the deaths of over 75 million people.
3
While the current pandemic has not reached these levels of lethality (as of early March 2022,
approximately 6 million people have sadly died from COVID-19), the uncertainty and reduced
economic activity and mobility parallels the effects on wartime economies of the 20th century.
In both war and pandemic economies, there is a clear effect on household savings which
has implications for demand once these events come to pass. During the smallpox epidemic,
household savings in England nearly doubled. During the Spanish Flu, U.S. household savings hit
their highest levels until WWII, in which they were equivalent to around 40% of GDP.
On the supply side of the economy, research studies have shown that technology and
automation tend to rise following these types of disruptions. This will be an important factor
in the post-COVID economy as the current pandemic has been unique in that both supply and
demand were intentionally disrupted in order to curb the spread of the virus. This dynamic is
evident in the economic data.
1. https://www.economist.com
2. http://www.centre-robert-schuman.org
3. https://courses.lumenlearning.com
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Data | Country selection
The countries selected for this portion of the study
are as follows: United Kingdom, Germany, Norway,
Philippines, India, China, Australia, New Zealand,
and Sweden. These countries were selected based
on COVID lockdown strategies and underlying
economies (services oriented vs. manufacturing
oriented, export vs. import economies, etc.).
Additionally, both Australia and New Zealand are
unique given their locations and ability to completely
close their borders to foreigners (as opposed to
countries with land borders).
Sweden was selected based on its avoidance of full-scale lockdowns, which was in contrast to the Australian and New
Zealand eradication approach. In between these two extremes lie the rest of the countries included in this study.
The countries selected also provide a broad sample of how the pandemic adversely affected various parts of the
economy. For example, services-oriented economies, such as the U.K., Australia, and New Zealand, felt the burden of the
lockdowns through the accommodation and food services industries whereas industrial-oriented export economies, such
as Germany and the Philippines, experienced adverse effects to industrial production and manufacturing. Given that the
shutdowns created negative demand and supply shocks, each country’s dependence on the above industries generally
corresponded to the magnitude in changes to real GDP.
This portion of our study leveraged the Oxford COVID-19 Government Response Trackers’ stringency index to determine
each country’s relative level of lockdown measures enforced to curb the spread of the virus. The stringency index is
derived from 23 indicators such as school closures, travel restrictions, vaccination policies, and mask mandates, that are
aggregated into a single number from 0-100 (0 being the least stringent, 100 being the most stringent).
COVID-19 specific data (infections, deaths, vaccinations) was obtained from Our World in Data. Economic data for each
country was obtained from Bloomberg and from the Organisation for Economic Co-operation and Development (OECD).
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
UK | A services oriented economy
The U.K. experienced the largest single quarterly real
GDP decline of all the countries in the study. Given that
services accounted for 79.6% of the U.K.’s GDP in 2020 and
lockdowns directly impacted services-oriented businesses,
this drop in real GDP is no surprise.
GDP
19.4% decline in 2Q20
17.6% rebound during 3Q20
Distribution, Transport, Hotel and Restaurants (“DTHR”),
the second largest component of all services industries in
the U.K., was impacted the most, falling by 31.8% in 2Q20.
Like the U.K.’s real GDP, DTHR recovered by 42.8% the
following quarter, although the recovery was short-lived
as the renewed lockdowns continued to impact this sector
throughout 2021.
The U.K.’s furlough program, a government program enacted
to prevent increased unemployment rates while providing
citizens with temporary income, also suggests that food and
tourist related industries were most sensitive to lockdown
policies. Figure 2.1 shows the number of furlough claims in
Accommodation and Food Services in comparison to those
in Construction, Administrative and Support Services, and
Wholesale and retail.
Accommodation and Food Services accounted for the
majority of total furlough claims filed in the U.K., followed by
Wholesale and Retail. These industries suffered the most as
their business models revolve around maximizing foot traffic,
which was severely limited during lockdowns.
UNEMPLOYMENT
Because of the furlough program, the U.K.’s unemployment
rate has not seen a dramatic increase throughout the
pandemic thus far (Figure 2.2).
5.2% at its apex in 4Q20, however, it dropped to
4.10% at the end of November 2021 with Accommodation
and Food Service workers feeling the greatest impact.
EXPORTS & IMPORTS
10.1% decrease in U.K. exports during 2Q20, driven, in
part by the
59.9% decrease in U.S. imports during 2Q20
Imports fell in 2Q20 and have fluctuated since, depending
on the severity of the lockdown policies.
21.1% decrease in U.K. imports, driven by a drop in
domestic consumer expenditures and the
20.1% decrease in exports from Germany in 2Q20.
CONSUMER PRICES
Turning to CPI, prices in the U.K. remained stable as the
economy experienced simultaneous supply and demand
shocks. Consumer prices have increased as the economy has
reopened, increasing by 5.4% YoY as of 4Q21. Manufacturing
prices, however, experienced slight deflation from 2Q20 –
3Q20. Prices have since increased by 9.8% YoY as of the
end of 2Q21.
Going forward, it is expected that inflationary pressures will
continue to rise as U.K. household savings rates peaked at
25% during the first wave of the pandemic and sat at 20%
at the end of 1Q21 (Figure 2.3) coupled with supply chain
disruptions faced during the pandemic that have not yet fully
recovered and may not be able to keep up with demand.
Additional sources: commonslibrary.parliament.uk, www.gov.uk
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
FIGURE 2.1:
UK FURLOUGHS – ACCOMMODATION AND FOOD SERVICES VS. CONSTRUCTION VS. ADMIN AND
SUPPORT SERVICES (MARCH 2020 – JUNE 2021)
200,000
400,000
600,000
2,000,000
0
1,200,000
800,000
1,000,000
1,400,000
1,800,000
1,600,000
Mar-20
Jun-20
Sep-20
Dec-20
Mar-21
Jun-21
Accommodation and food services
UK Furloughs
Construction
Administrative and support services
Wholesale and retail
Source: www.gov.uk
FIGURE 2.2:
UK UNEMPLOYMENT RATE (NOVEMBER 2018 – NOVEMBER 2021)
3.5%
4.0%
4.5%
6.0%
3.0%
5.0%
5.5%
Nov-18
May-19
Nov-19
Nov-20
May-20
May-21
Nov-21
Unemployment Rate
Source: www.gov.uk
FIGURE 2.3:
UK HOUSEHOLD SAVINGS (MARCH 2005 – SEPTEMBER 2021)
0%
5%
10%
25%
15%
20%
Mar-05
Sep-05
Mar-06
Sep-06
Mar-07
Sep-07
Mar-08
Sep-08
Mar-09
Sep-09
Mar-10
Sep-10
Mar-11
Sep-11
Mar-12
Sep-12
Mar-13
Sep-13
Mar-14
Sep-14
Mar-15
Sep-15
Mar-16
Sep-16
Mar-17
Sep-17
Mar-18
Sep-18
Mar-19
Sep-19
Mar-20
Sep-20
Mar-21
Sep-31
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Germany | A manufacturing and export
oriented economy
GDP
10.0% decline in 2Q20
9.0% rebound in 3Q20 (Figure 2.4)
Given the sensitivity to the COVID-19 cycles and lockdown
policies, Germany’s quarterly GDP has not recovered to its
pre-pandemic levels. Similarly, Germany’s unemployment
rate reached a peak of 6.4% in 2Q20 and has been steadily
declining since.
The largest components of Germany's real GDP are industrial
production (excluding construction), manufacturing, public
administration and defense, and wholesale/retail trade and
food services. Not surprisingly, these sectors were impacted
the most from lockdown policies. In 2Q20:
17.4% decrease in industrial production
18.9% decrease in manufacturing output
15.1% decrease in wholesale/retail trade and food services
8.5% decrease in public admin and defense
Industrial production and manufacturing promptly recovered
after the first COVID-19 infection cycle but continued to fall
as new lockdown measures were implemented.
The lockdowns also had adverse effects on household
consumption and gross capital formation. Household
consumption fell by 11.6% in 2Q20. Similarly, gross capital
formation fell by 8.4% in 2Q20, with machinery and
equipment taking the largest decrease of 14.79% during the
quarter.
EXPORTS & IMPORTS
Germany’s exports decreased by 20.2%% in 2Q20 as a result
of the shutdown of manufacturing facilities, supply chain
disruptions that impacted the export of vehicles (such as
semiconductors), and a decrease in imports from its top
export recipients, such as the U.S. and the U.K.
20.6% decrease in export of goods in 2Q20
18.1% decrease in export of services in 2Q20
While not at pre-pandemic levels, exports have been steadily
recovering, but imports were also negatively impacted by the
lockdowns, falling by 16.9% in 2Q20.
12.6% decrease in imports of goods in 2Q20
30.9% decrease in imports of services in 2Q20
CONSUMER & PRODUCER PRICES
Consumer prices remained stable throughout the early
stages of the pandemic, increasing by 0.8% YoY in 2Q20.
Consumer prices have since risen by 5.7% YoY as of 4Q21
and is likely to persist based on increased demand.
1.8% decrease in producer prices (YoY in 2Q20)
24.2% increase (YoY at the end of 4Q21)
While a portion of this increase may be due to the base
effect, it remains to be seen whether producer prices will
continue to rise as suppliers try to keep up with demand
fueled by the growth in household savings. Household savings
doubled to 20.3% in 2Q20 and stands at 12.2% as of 3Q21.
FIGURE 2.4:
GERMANY’S REAL GDP
(MARCH 2005 – SEPTEMBER 2021)
0%
-6%
12%
-12%
6%
Mar-05
Dec-05
Sep-06
Jun-07
Dec-08
Sep-09
Jun-10
Mar-08
Dec-11
Sep-12
Jun-13
Mar-11
Dec-14
Sep-15
Jun-16
Mar-14
Dec-17
Sep-18
Jun-19
Mar-17
Dec-20
Sep-21
Mar-20
GDP (QoQ%)
80
90
100
130
70
110
120
GDP Index
GDP Index
GDP (QoQ%)
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Norway | An oil-based economy
Norway presents an interesting case study of how an oil-
oriented economy performed during the pandemic.
GDP
4.7% fall in 2Q20, but has been recovering since
Industrial production, public admin/defense and wholesale
and retail trade/accommodation and food services are the
three largest components of Norway’s real GDP (excluding
exports). Industrial production (excluding construction) was
stable throughout the pandemic, only falling by 2.4% in
4Q20. Wholesale and retail trade fell by 10.0% in 2Q20 and
has struggled to recover given its sensitivity to lockdowns.
Public admin/defense fell by 4.5% but has recovered to its
pre-pandemic levels.
EXPORTS & IMPORTS
4
7.5% decrease in exports of goods
7.5% decrease in crude oil/natural gas exports
49.3% decrease in exports of ships and oil platforms
9.2% decrease in exports of services
Except for crude oil/natural gas, exports have returned to
pre-pandemic levels.
Imports were notably more sensitive to the lockdown
measures. Imports fell by 13.6% in 2Q20 and was impacted
by subsequent waves of COVID-19 infections. During the
second wave (March 2021), imports fell by 9.7% and still
remain below pre-pandemic levels (as of 4Q21).
CONSUMER & PRODUCER PRICES
Consumer prices in Norway remained stable throughout
2020, rising by 1.4% YoY as of December 2020. As housing
and utility costs have increased throughout the country,
consumer prices in 2021 have increased 5.3% YoY as of
4Q21, a high not seen since 2008.
Producer prices have been much more volatile when
compared to consumer prices.
14.4% decrease YoY through June 2020
5.7% decrease YoY as of December 2020, but since then,
prices have sharply increased by
68.7% increase YoY as of December 31, 2021
The increase in prices is mostly likely the result of increased
demand from export partners. The largest increases have
come from the extraction of oil and natural gas (+179%
YoY) and electricity, gas and steam supply (+112% YoY; both
figures not seasonally adjusted).
FIGURE 2.5:
NORWAY EXPORTS & IMPORTS
(DECEMBER 2019 – DECEMBER 2021)
Sep-20
Dec-20
Mar-20
Jun-20
Mar-21
Dec-21
Dec-19
Jun-21
Sep-21
-15%
-10%
-5%
0%
5%
10%
Exports
Imports
Source: Bloomberg
4. These figures are not seasonally adjusted
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Philippines | An emerging economy
An example of a manufacturing and export oriented
emerging economy, the Philippines was more sensitive to
lockdowns relative to the other countries in this study.
GDP
The Philippines’ economy is seasonal such that its major
industries typically decrease during the first quarter of every
year. Driving the 15.6% real GDP decrease in 2Q20 was the
following:
11.9% decrease in household consumption
36.7% decrease in fixed capital formation
8.2% decrease in construction (after contracting by 31.5%
in the first quarter)
17.8% decrease in manufacturing
Additionally, around 10.0% of the Philippines’ real GDP is
from agriculture, which decreased by 17.5% in 1Q20 and
further decreased by an additional 5.4% in 2Q20.
Unemployment increased from 5.3% in January 2020 to
17.7% in April 2020 but has since recovered to 6.5% as of
4Q21.
EXPORTS &IMPORTS
Exports fell by 24.4% in 2Q20, driven by the lockdowns and
decreases in imports from its top trade partners, such as the
U.S., Japan and China. Specifically, during the second quarter
of 2020 there was a
21.3% decrease in exports of goods
27.7% decrease in exports of services
Goods imported also fell by 25.1% in 2Q20 but have nearly
recovered to pre-pandemic levels. Services imported fell
by 43.1% in 2Q20 and have struggled to recover since
(Figure 2.6).
Consumer inflation remained on its prior trajectory of around
+3% YoY. Producer prices, however, have experienced
prolonged deflation throughout the pandemic. During the
onset of the initial global lockdowns, producer prices fell by
around 6% YoY in March 2020. Through February 2021, the
YoY decrease in PPI hovered around -5%, but prices started
to rise in March 2021 and as of November 30, 2021, PPI
was up 0.9%.
FIGURE 2.6:
PHILIPPINE PRODUCER PRICES
(JANUARY 2019 – NOVEMBER 2021)
3%
2%
1%
0%
-1%
-2%
-3%
-4%
-5%
-6%
-7%
Jan-19
Mar-19
May-19
Jul-19
Sep-19
Nov-19
Jan-20
Mar-20
May-20
Jul-20
Sep-20
Nov-20
Jan-21
Mar-21
May-21
Jul-21
Sep-21
Nov-21
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
India | An emerging economy
India’s emerging economy has been sensitive to the lockdown measures enforced to contain the spread of the virus.
Quarterly GDP fell by around 30% (not seasonally adjusted) in 2Q20 (Figure 2.7) followed by a recovery of 22% in
3Q20 and continued growth of 10% in 4Q20.
UNEMPLOYMENT
India’s unemployment rate (trailing 30-day unemployment) has also been sensitive to the COVID-19 infection cycles,
reaching 23% in April and May of 2020. India’s unemployment has stayed around the 7-9% range since this spike,
with May 2021 being the exception where it hit close to 12% as a result of the delta variant’s spread.
GDP
Driving the decrease in quarterly GDP and rise in the unemployment rate was the impact COVID-19 had on India’s
largest components of GDP; finance/Insurance/business services, India’s largest industry as a percentage of GDP, has
increased on average by around 26.0% during the second quarter of every year since 2012 (industry is cyclical), but
only increased by 20.7% during 2Q20, returning to its 2018 levels.
Trade/Transport/Hotels/Communications:
53.0% decrease in 2Q20
55.1% recovery/increase in 3Q20
35.3% decrease in 2Q21 (Delta variant)
Also in 2Q20, Manufacturing and Construction decreased by 38.5% and 52.6%, respectively, but have been
recovering since.
EXPORTS & IMPORTS
India’s exports and imports were also negatively impacted by the COVID-19 infection cycles. Exports fell by 21% in
3Q20 and have yet to recover to pre-pandemic levels. Imports were more sensitive to lockdown measures, falling by
25% in 2Q20. Like exports, imports have also not recovered to pre-pandemic levels and continue to be impacted by
successive waves of COVID-19 infections.
CONSUMER & PRODUCER PRICES
Consumer prices were elevated throughout 2020 relative to 2019 and increased by 4.6% YoY at year end 2020.
As of December 2021, consumer prices have increased by 5.6% YoY. The increases in consumer prices were driven
by increases in fuel and light, clothing, and housing costs. Wholesale prices decreased by 1.8% during the first half of
2020. Prices recovered during the second half of the year, increasing by 2.0% at year-end 2020.
Producer prices have increased dramatically throughout 2021, increasing by 13.5% YoY as of December 2021.
This increase was driven by the 32.3% increase in fuel power light prices and 10.6% increase in manufacturing prices.
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
FIGURE 2.7:
INDIA’S REAL GDP (JUNE 2011 – SEPTEMBER 2021)
-10%
0%
-20%
35%
30%
-35%
-30%
10%
20%
Dec-11
Jun-12
Dec-13
Jun-11
Dec-14
Jun-15
Dec-15
Jun-14
Dec-16
Jun-17
Dec-17
Jun-16
Dec-18
Jun-19
Dec-19
Jun-20
Jun-21
Dec-20
Jun-18
GDP (QoQ%)
40
80
200
0
120
160
GDP Index
GDP Index
GDP (QoQ%)
Source: Bloomberg
FIGURE 2.8:
INDIA’S UNEMPLOYMENT RATE (DECEMBER 2018 – DECEMBER 2021)
10%
15%
25%
5%
20%
Mar-19
Jun-19
Sep-19
Dec-18
Mar-20
Jun-20
Sep-20
Dec-19
Mar-21
Jun-21
Sep-21
Dec-20
Dec-21
Unemployment Rate
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
China | Earliest economic effects
With the pandemic originating in Wuhan, the effects of
the COVID-19 lockdowns affected the Chinese economy
earlier relative to the rest of the world. Quarterly GDP fell
by 10.5% in 1Q20 but recovered to its pre-pandemic levels
the following quarter (Figure 2.9). Growth has been slower,
however, throughout 2021.
GDP
Largely driving the decrease in GDP the first quarter of
2020 was China’s primary and secondary industries. China’s
primary industries (cultivation and acquisition of raw
materials) fell by 3.2% YoY in 1Q20 but have recovered to
pre-pandemic levels. More importantly, China’s secondary
industries (manufacturing/assembly processes) fell by 9.6%
YoY in 1Q20. This makes sense given the direct impact
lockdowns had on manufacturing facilities.
As expected, manufacturing fell by 10.2% YoY in 1Q20.
Construction, Wholesale/Retail Trade, Transportation/
Storage/Postal Services and Hotels/Catering Services all
were the most impacted sectors of the economy. In 1Q20
there was a:
17.5% decrease in Construction (YoY)
17.8% decrease in Wholesale/Retail Trade (YoY)
14.0% decrease in Transportation/Storage/Postal Services
(YoY)
35.3% decrease in Hotels/Catering Services (YoY)
CONSUMER & PRODUCER PRICES
While the current pandemic was not as deflationary to
consumer prices in China relative to the Global Financial
Crisis of 2007-2008 (“GFC”), consumer prices decreased
slightly throughout 2020. Consumer prices have increased by
1.5% YoY as of December 2021. Similarly, producer prices in
China did not decrease as much as they did during the GFC.
Industrial, Raw Materials and Wholesale PPIs all decreased
YoY through 1H20, decreasing by 3.0%, 4.4% and 2.3%,
respectively. Prices recovered slightly and all three PPIs
stayed flat YoY through year-end 2020. However, like many
of the economies across the globe, prices have increased
throughout 2021 as demand has picked up.
FIGURE 2.9:
CHINA’S REAL GDP
(JUNE 2011 – DECEMBER 2021)
-5%
0%
-10%
5%
20%
-15%
10%
15%
Jun-11
Dec-11
Jun-12
Dec-12
Dec-13
Jun-14
Dec-14
Jun-13
Dec-15
Jun-16
Jun-15
Jun-17
Dec-17
Jun-18
Dec-16
Jun-19
Dec-19
Jun-20
Dec-18
Jun-21
Dec-21
Dec-20
GDP (QoQ%)
40
80
120
240
0
160
200
GDP Index
GDP Index
GDP (QoQ%)
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Australia and New Zealand | Strict quarantine
and closure policy
Both Australia and New Zealand have similar services-
oriented economies, with services driving 75% of Australia’s
GDP and 74% of New Zealand’s GDP.
Both countries diverge from others in our current study
because of their extensive approach to curb COVID-19
infections through border closures, strict quarantine policies,
contact tracing, and lockdowns. New Zealand implemented a
comprehensive genetic sequence testing program to pinpoint
the source of any COVID-19 strains that has infected
citizens and, as a result, has maintained control of most local
COVID outbreaks.
GDP
In 2Q20, New Zealand’s quarterly real GDP fell by 10.3%
while Australia’s real GDP fell by 6.8%, but both countries’
quarterly real GDP has returned to pre-pandemic levels. New
Zealand and Australia experienced a notable decrease in
household consumption in 2020
8
with New Zealand's falling
by 10.6% and Australia's by 12.1% in 2Q20, but they have
since recovered to pre-pandemic levels due, in part, to the
increase in household savings in both countries during the
onset of the lockdowns.
Both Australia and New Zealand experienced sharp
decreases to their exports and imports in 2020 and have
struggled to recover to pre-pandemic levels (Figure 2.10).
17.1% decrease in New Zealand’s exports (2Q20)
6.7% decrease in Australia’s exports (2Q20)
24.4% decrease in New Zealand’s imports (2Q20)
12.7% decrease in Australia’s imports (2Q20)
CONSUMER & PRODUCER PRICES
Consumer prices in New Zealand went up on average by
around 1.5% YoY from 2Q20 – 1Q21. As of 3Q21, consumer
prices increased 4.9% YoY. In Australia, consumer prices
remained stable through the onset of the pandemic in 2Q20
(dropping only by 0.3% YoY) and has stayed around +1.0%
YoY through 1Q21. As of 3Q21, consumer prices increased
3.0% YoY.
Producer input prices in New Zealand were stable from
2Q20 – 1Q21 but increased by 6.9% YoY as of 4Q21.
9
Driving this increase has been the rise in electricity and
gas, petrol and coal manufacturing and meat/meat product
manufacturing. Australia’s producer prices also remained
stable throughout 2020, decreasing by 0.1% YoY as of year-
end
8
2020. Australian producer prices increased by 2.9% YoY
as of December 2021 due to increases in the costs of labor
in construction and petrol refining/manufacturing.
FIGURE 2.10:
AUSTRALIA AND NEW ZEALAND IMPORTS
& EXPORTS (SEPTEMBER 2019 – SEPTEMBER 2021)
Sep-20
Nov-20
Jan-21
Mar-20
May-20
Jul-20
Mar-21
May-21
Sep-21
Sep-19
Nov-19
Jan-20
Jul-21
-30%
-10%
-20%
0%
10%
20%
NZ Exports
NZ Imports
AUS Exports
AUS Imports
Source: Bloomberg
Additional sources: www.stats.govt.nz, tradingeconomics.com, www.abs.gov.au
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Sweden | Lockdown resistance
Sweden presents an interesting case study of a country
that resisted lockdowns in response to the initial waves of
COVID-19. Sweden’s quarterly GDP fell by 8 % in 2Q20 and
has been recovering since.
GDP
Driving the loss in GDP included losses in wholesale retail
and trade/accommodation and food services, industrial
production, manufacturing and professional/scientific and
support services. In 2Q20:
15.2% decrease in wholesale retail and trade/
accommodation and food services
17.9% decrease inindustrial production excluding
construction
21.8% decrease in manufacturing
10.9% decrease in professional/scientific and support
services
EXPORTS & IMPORTS
Exports fell by 17.2% in 2Q20. More specifically,
goods exported fell by 16.9%
while services exported fell by 17.8%.
While the level of goods exported has recovered to its pre-
pandemic levels, services exported has struggled to recover.
Similarly, imports also decreased by 12.3% in 2Q20. Goods
imported decreased by 10.6% while services imported
decreased by 15.7%. Like exports, imports of services
have not been able to recover while imports of goods have
exceeded their pre-pandemic level.
CONSUMER & PRODUCER PRICES
Consumer prices in Sweden remained stable throughout
the onset of the pandemic, staying flat from March 2020
to December 2020 YoY. YYoY consumer price growth rates
returned to their pre-pandemic levels before accelerating
to 3.9% as of December 2021. Producer prices decreased
throughout all of 2020. Producer prices have since risen by
18.1% YoY as of November 2021.
UNEMPLOYMENT
Sweden’s unemployment rate increased from 7% (Sweden’s
unemployment is seasonal and typically varies by quarter) up
to 10% in 2Q21 (Figure 2.12). Sweden’s unemployment rate
is slowly approaching its pre-pandemic but remained in the
7-8% range July – November 2021.
FIGURE 2.11:
SWEDEN’S REAL GDP
(JUNE 2011 – SEPTEMBER 2021)
-4%
0%
8%
-8%
4%
Dec-11
Jun-12
Dec-12
Jun-11
Dec-13
Jun-14
Dec-14
Jun-13
Dec-15
Jun-16
Dec-16
Jun-15
Dec-17
Jun-18
Dec-18
Jun-19
Jun-21
Dec-20
Jun-20
Dec19
Jun-17
GDP (QoQ%)
75
150
50
100
125
Index
Index
GDP (QoQ%)
Source: Bloomberg
FIGURE 2.12:
SWEDEN’S UNEMPLOYMENT RATE
(NOVEMBER 2018 – NOVEMBER 2021)
7%
6%
11%
5%
9%
8%
10%
Feb-19
May-19
Aug-19
Nov-18
Feb-20
May-20
Aug-20
Nov-19
Feb-21
May-21
Aug-21
Nov-20
Nov-21
Unemployment Rate
Source: Bloomberg
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
3
. State of the world
TABLE 3.1:
COVID-19 STATUS IN SEVERAL COUNTRIES AROUND THE
WORLD (AS OF DECEMBER 31, 2021)
Country
Total cases
(per million)
Total deaths
(per million)
%
population
fully
vaccinated*
%
population
boosted*
New cases 21
day moving
average
As of date
Australia
16,500
87
77%
9%
8,360
2021-12-31
Brazil
104,171
2,894
67%
12%
4,888
2021-12-31
Canada
57,339
796
77%
20%
17,401
2021-12-31
Chile
94,028
2,036
86%
57%
1,339
2021-12-31
China
71
3
84%
0%
129
2021-12-31
Colombia
100,602
2,535
55%
6%
3,226
2021-12-31
Czechia
230,847
3,369
62%
22%
7,762
2021-12-31
Hungary
130,412
4,067
62%
33%
3,500
2021-12-31
India
25,019
346
43%
0%
8,521
2021-12-31
Indonesia
15,424
521
45%
*
189
2021-12-31
Israel
148,954
887
64%
46%
1,645
2021-12-31
Japan
13,743
146
78%
0%
214
2021-12-31
Mexico
30,552
2,299
56%
0%
3,239
2021-12-31
New Zealand
2,754
10
75%
7%
64
2021-12-31
Norway
72,134
239
72%
29%
3,975
2021-12-31
Philippines
25,611
464
45%
0%
370
2021-12-31
Poland
108,692
2,568
56%
18%
15,389
2021-12-31
Romania
94,569
3,072
41%
0%
828
2021-12-31
Russia
70,730
2,074
46%
5%
25,604
2021-12-31
Singapore
51,233
152
86%
40%
332
2021-12-31
South Africa
57,598
1,518
26%
0%
16,467
2021-12-31
South Korea
12,382
110
83%
36%
5,937
2021-12-31
Sweden
129,406
1,507
73%
0%
4,075
2021-12-31
Switzerland
152,902
1,402
67%
25%
10,509
2021-12-31
Taiwan
714
36
68%
1%
15
2021-12-31
Thailand
31,786
310
66%
10%
2,984
2021-12-31
Turkey
111,503
968
61%
27%
22,837
2021-12-31
United Kingdom
190,089
2,181
70%
50%
105,995
2021-12-31
United States
164,584
2,486
62%
22%
233,858
2021-12-31
Source: Our World in Data. Data pulled January 31, 2022 | *indicates data is unavailable
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Australia
As previously discussed, Australia’s
comparatively low level of deaths
(87 per million, Figure 3.1), the fourth
lowest in the study, could be due to the
strict containment measures enacted to
eradicate COVID-19 infections from its
borders.
Brazil
Given the hesitancy towards lockdown
measures at the beginning of the
pandemic, the high rate of fatalities from
COVID-19 in Brazil are no surprise.
Canada
Canada has the sixth highest
vaccination rate within our study, with
77% of its population fully vaccinated
as of December 31, 2021 (Figure 3.2).
TABLE 3.2:
% OF POPULATION
FULLY VACCINATED
(AS OF DECEMBER 31, 2021)
Country
% population fully
vaccinated*
Singapore
86%
Chile
86%
China
84%
South Korea
83%
Japan
78%
Canada
77%
Australia
77%
New Zealand
75%
Sweden
73%
Norway
72%
United Kingdom
70%
FIGURE 3.1:
TOTAL COVID-19 DEATHS
(PER MILLION, AS OF DECEMBER 31, 2021)
1,000
2,000
5,000
0
3,000
4,000
United Kingdom
United States
Thailand
Turkey
Switzerland
Taiwan
South Korea
Sweden
Singapore
South Africa
Romania
Russia
Philippines
Poland
New Zealand
Norway
Mexico
Israel
Japan
India
Indonesia
Czechia
Hungary
China
Colombia
Canada
Chile
Australia
Brazil
Average
Total deaths per million
Source: Our World in Data. Data pulled January 31, 2022
FIGURE 3.2:
% OF POPULATION FULLY VACCINATED
(AS OF DECEMBER 31, 2021)
10%
20%
100%
0%
30%
90%
80%
70%
60%
50%
40%
United Kingdom
United States
Thailand
Turkey
Switzerland
Taiwan
South Korea
Sweden
Singapore
South Africa
Romania
Russia
Philippines
Poland
New Zealand
Norway
Mexico
Israel
Japan
India
Indonesia
Czechia
Hungary
China
Colombia
Canada
Chile
Australia
Brazil
Average
% population fully vaccinated
Source: Our World in Data. Data pulled January 31, 2022
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Chile
Chile has the highest % of population boosted (57%) as of
December 31, 2021.
China
China reports that COVID-19 has caused a total of
three deaths per million while only infecting 0.01% of its
population, the lowest levels of all countries in the study, and
reports that 84% of its population is fully vaccinated. As of
December 31 2021, China reports a 21-day moving average
of 129 daily new cases, well below the median moving
average of 3.975 new cases.
Czech Republic
COVID-19 has caused a total of 3,369 deaths per million
in the Czech Republic, the highest level in our study. 23%
of its population has been infected by the virus, the highest
infection rate of all countries in our study. 62% of its
population is fully vaccinated and 22% is boosted.
TABLE 3.3:
COVID-19 CASES (AS OF DECEMBER 31, 2021)
Country
Total cases per million
Czech Republic
230,847
United Kingdom
190,089
United States
164,584
Switzerland
152,902
Israel
148,954
TABLE 3.4:
COVID-19 DEATHS (AS OF DECEMBER 31, 2021)
Country
Total cases per million
Hungary
4,067
Czech Republic
3,369
Romania
3,072
Brazil
2,894
Poland
2,568
Source: Our World in Data. Data pulled January 31, 2022
Hungary
As of December 2021, COVID-19 has caused a total of
4,067 deaths per million, which is the highest level of deaths
per million in our study. There are several potential reasons
that explain Hungary’s high death rate. The first being
that Hungary was infected by a more aggressive variant
of COVID-19 that originated in the U.K. in early 2021. In
conjunction with this more aggressive variant, Hungary’s
government was slower to respond to the surge in cases
as the country did not experience a high infection or death
rate during the first wave that impacted most of the world.
Influencing the slower government response was also the
quality of domestic COVID-19 data and testing protocols.
These factors, along with idiosyncratic demographic
conditions, seemed to lead to higher deaths per million.
13.0% of Hungary’s population has been infected by the
virus, which is 5.7 percentage points higher than the average
of 7.7%. 62% of Hungary’s population is fully vaccinated.
India
India reports a total of 346 deaths per million in India, well
below the average of 1,348 deaths per million. This low
death rate could be due to India only including confirmed
COVID-19 deaths in hospitals in the official tally. India’s
official statistics report that only 2.5% of its population has
been infected.
Indonesia
Indonesia has a relatively low infection rate, with the virus
infecting only 1.5% of its population. According to Reuters,
a seroprevalence study revealed that this number is most
likely underreported as Indonesia had low contact tracing
processes and insufficient laboratory capacity to process
COVID-19 tests. The seroprevalence study estimated that
closer to 15.0% of Indonesia’s population has been infected
(report as of June 2021).
5
5. Source: www.reuters.com
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Japan
COVID-19 has caused a total of
146 deaths per million in Japan.
COVID-19 has infected 1.3% of
Japan’s population, which is relatively
low compared to the other countries
in the study. 78% of Japan’s
population is fully vaccinated, above
the average vaccination rate of 64%.
New Zealand
New Zealand’s strict quarantine and
closure policies could be the reason
for the country’s low infection rate
of 0.28% of total population and 10
deaths per million.
Romania
Romania has the third highest level of
total deaths per million (4,067). 41%
of the population is fully vaccinated.
Singapore
86% of Singapore’s population is
fully vaccinated, which is the highest
vaccination rate in the study.
South Africa
26% of South Africa’s population is
vaccinated, the lowest vaccination
rate in the study.
FIGURE 3.3:
COVID-19 CASES AS % OF POPULATION
(AS OF DECEMBER 31, 2021)
10%
20%
100%
0%
30%
90%
80%
70%
60%
50%
40%
United Kingdom
United States
Thailand
Turkey
Switzerland
Taiwan
South Korea
Sweden
Singapore
South Africa
Romania
Russia
Philippines
Poland
New Zealand
Norway
Mexico
Israel
Japan
India
Indonesia
Czechia
Hungary
China
Colombia
Canada
Chile
Australia
Brazil
Average
Total cases as % of population
Source: Our World in Data. Data pulled January 31, 2022
Sweden
In spite of Sweden’s high vaccination rate (73%), the country’s lockdown resistance
may have led to their above average total deaths per million (1,507) and % of
population infection rate, which can be seen in Figures 3.1 and 3.3.
Switzerland
COVID-19 has infected 15.3% of Switzerland’s population, one of the highest
infection rates in the study.
United Kingdom
In spite of a 70% vaccination rate plus a 50% boosted population rate, COVID-19
has infected 19.0% of the U.K.’s population – 190,089 cases per million, the 2nd
highest infection rate in our study.
United States
The United States has the highest 21 day moving average of new cases at 233,858
as of December 31, 2021.
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
4
. COVID-19 and FX
The relationship between macroeconomic indicators and foreign exchange rates
has been researched in multiple academic studies. Bernard Njindan Iyke
6
found
that fundamental macroeconomic indicators like foreign interest rates, government
spending, terms of trade, and net assets can have a direct impact on exchange
rates. Robert J. Hodrick
7
also found that unanticipated macroeconomic events,
like exogenous swings in the conditional variances of income growth and fiscal/
monetary policy, influenced exchange rates through changes to the risk premia.
As shown in Section 2, the current pandemic has adversely affected the services
industry and constrained global supply chains. In particular, accommodation/food
services have been most impacted, while on the supply side, manufacturing and
industrial production have been the most sensitive industries to COVID-19 cycles.
Each country’s dependence on these industries dictated the effects to GDP and
recovery rates thus far.
Given COVID-19’s level of disruption to everyday economic activity, it is
anticipated that there should be a link between COVID-19 variables (infection
rates, death rates, excess mortality) and foreign exchange rates. Indeed, this has
been the topic of academic research throughout the pandemic. Iyke showed in
another study that COVID-19 outbreaks had predictive power over exchange
rate volatility. Although the sample was from December 2019 – August 2020, the
study found that the level of COVID-19 infections negatively predicted exchange
rate volatility for USDCHF, USDCNY, USDILS, USDJPY, and USDPEN over a
1-day horizon. The study also looked at a 5-day horizon and found that infections
negatively predicted USDCHF, USDEUR, USDINR, USDPLN and USKSEK returns
and positively predicted USDGBP and GBPUSD returns. For volatility over a
5-day horizon, the study found that infections positively predicted USDCAD and
USDEUR while negatively predicting USDSEK and USDGBP.
While this study showed that COVID-19 infections did, indeed, contain predictive
information, it remains to be seen how this relationship has evolved throughout
the rest of 2020 and into 2021, especially with the emergence of vaccines and
the divergence in vaccination rates between advanced economies and emerging
economies.
6. Iyke, Bernard Njindan, "Macro determinants of the real exchange rate in a small open small island economy: Evidence from mauritius via bma." Buletin Ekonomi Moneter dan Perbankan 21.1 (2018): 57-80.
7. Hodrick, Robert J., "Risk, uncertainty, and exchange rates." Journal of Monetary economics 23.3 (1989): 433-459.
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Our current study looked at the relationship between the
level of infections, deaths, and excess mortality on exchange
rates for the following currency pairs: USDSEK, USDCHF,
USDTWD, USDGBP, USDAUD, USDCAD, USDCLP,
USDCNH, USDXEU, USDIDR, USDJPY, USDNOK, USDPHP,
USDPLN, USDRON, USDRUB, USDZAR, USDKRW,
USDTHB, USDTRY, USDBRL, USDCOP, USDCZK, USDHUF,
USDINR, USDILS, USDMXN and USDNZD.
Assuming the USD as the base currency, it was anticipated
that the level of COVID-19 infections, deaths and excess
mortality would be negatively correlated to the exchange
rate. In other words, the hypothesis was that each
acceleration in COVID-19 infections within each cycle
in country
n
would correspond to a depreciation in that
country’s currency.
Plotting the COVID-19 variables against the individual spot
rates, this relationship was confirmed for the following
pairs: USDPHP, USDPLN, USDRON, USDRUB, USDZAR,
USDKRW, USDTHB, USDTRY, USDBRL, USDCOP, USDCZK,
USDHUF, USDINR, USDILS, USDMXN and USDNZD.
In particular, USDRON, USDRUB, USDZAR, USDCZK,
USDTRY and USDHUF had the strongest relationships out
of all the pairs. It is no surprise that these currency pairs
appeared to be more sensitive to COVID-19 infections as
they are all emerging economies and their vaccination rates
are not as high relative to the advanced economies
Looking at all the currency pairs that showed a negative
correlation to the level of new cases, many of these pairs
were clustered together according to both demographic and
GDP data. This is illustrated in our demographic hierarchical
clustering model (Figure 4.1). Brazil, Mexico, Colombia,
Philippines, South Africa and Turkey were all clustered
together. Similarly, Israel, Australia, New Zealand and Russia
were also clustered together. Lastly, Poland, Romania,
Thailand, and South Korea also created a cluster.
FIGURE 4.1:
HIERARCHICAL CLUSTERING USING COUNTRIES' DEMOGRAPHIC DATA
United Kingdom
United States
Thailand
Turkey
Switzerland
Taiwan
South Korea
Sweden
Singapore
South Africa
Romania
Russia
Philippines
Poland
New Zealand
Norway
Mexico
Israel
Japan
India
Indonesia
Hungary
Austria
Belgium
France
Netherlands
Germany
Portugal
Spain
Finland
Cyprus
Greece
Italy
Slovenia
Czech Republic
Latvia
Lithuania
Estonia
Slovakia
Luxembourg
Malta
Ireland
China
Colombia
Canada
Chile
Australia
Brazil
Source: Mesirow
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Looking to GDP data (Figure 4.2), Russia, Mexico and Turkey created a cluster, South Korea and the
Czech Republic formed another cluster, and Israel and New Zealand also formed a cluster.
In terms of the other countries that did not exhibit a negative correlation between COVID-19 data
and their exchange rates, one reason that the relationship may not exist is due to central bank
intervention and macroprudential policies.
FIGURE 4.2:
HIERARCHICAL CLUSTERING USING COUNTRIES' SECTORIZED GDP DATA
United Kingdom
United States
Denmark
Turkey
Switzerland
Sweden
South Africa
Russia
Poland
New Zealand
Norway
Mexico
Israel
Iceland
Japan
India
Indonesia
Hungary
Austria
Belgium
France
Netherlands
Germany
Portugal
Spain
Finland
Greece
Italy
EEA
European Union
Czech Republic
Korea
Latvia
Lithuania
Estonia
Luxembourg
Costa Rica
Ireland
China
Colombia
Canada
Chile
Australia
Brazil
Slovenia
Slovakia
Source: Mesirow
FIGURE 4.3:
HIERARCHICAL CLUSTERING USING COUNTRIES' FX DATA
USDTHB
USDILS
USDZAR
USDTWD
USDCHF
USDRON
USDTRY
USDMYR
USDCAD
USDSGD
USDBRL
USDPHP
USDCLP
USDSEK
USDJPY
USDRUB
USDPLN
USDMXN
USDCZK
AUDUSD
XEUUSD
USDKRW
USDINR
USDIDR
NZDUSD
USDCNH
GPBUSD
USDNOK
USDCOP
USDHUF
Source: Mesirow
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Handling a country’s missing COVID-19 data
One way to complete missing or unreliable COVID-19
indicators for a country can be to use the data from
other countries with similar economic or demographic
characteristics. A data-driven approach to implement
this solution is to utilize a hierarchical clustering
algorithm (see Figures 4.1 – 4.3).
In general, clustering algorithms categorize the input
data into various classes. As they do not rely on
labelled samples, they are also known as unsupervised
classification techniques. One of their key parameters
is the number of output clusters. Generally, this is
either explicitly given to the algorithm (by considering
some prior knowledge about the problem at hand) or is
implicitly inferred from the feature space. Hierarchical
clustering incorporates the second approach. It first
assumes each sample forms a cluster. Then, the
cross-correlations between all samples are calculated.
Depending on their adjacency, samples form new
clusters. Each new cluster is then treated as a new
sample point. This process of detecting closer samples
(to form sub-clusters) and merging them to create new
clusters repeats until we are only left with one cluster,
comprising all the input samples.
This step-by-step calculation of correlations and
merging of clusters are usually visualized as a tree
diagram known as a dendrogram. By assigning a cut-
off level over its branches, we can find the clustering
results. We can also count the number of intersections
between the cut-off line and the branches of the
dendrogram as a rough estimate of the number of
clusters. Therefore, this method finds clusters in the
input data, without setting the number of clusters as a
priori assumption. Instead, it facilitates estimating the
number of clusters from the dendrogram results.
The Federal Reserve has set up swap lines with all G7
central banks and with the Swiss National Bank. These
swap lines were set up to ensure these central banks
had enough liquidity to withstand the flight to safety
(USD) that occurred at the onset of the pandemic, which
may have impacted each currency’s behavior. Given the
severity of the pandemic, many central banks globally
deployed all tools in their toolkits to stabilize financial
markets and protect their currencies.
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
5
. Predictive model
Considering the economic data and our analysis in the previous sections, we
establish the following hypothesis, which constitutes the basis of our predictive
model design:
There is a negative correlation between a country’s COVID indicators and its
currency strength.
If this assumption is true, a predictive mechanism applied to a country’s COVID
indicators can be used to generate signals to short its currency against the USD
as soon as the COVID situation is about to worsen. One of the most obvious and
widely available data to describe a country’s COVID status is its number of daily
new cases. Our modelling starts with first performing a linear regression over
C
t-T
B
:t
(i)
,
which is the number of new cases during the last
T
B
days in the country
i
.
The regression results in a line, which makes the
θ
t-T
B
:t
(i)
angle with the horizontal
axis, as shown in Figure 5.1. This angle
θ
t-T
B
:t
(i)
is used by our predictive model to
generate trading signals (
z
-score normalization has been applied to both axes).
FIGURE 5.1:
LINEAR REGRESSION APPLIED TO A COUNTRY’S RECENT
COVID DATA
2.0
-2.0
-1.5
1.5
1.0
0.5
0.0
-0.5
-1.0
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Sample index
Regresssion result
Daily number of cases (normalized)
Source: Mesirow
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March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
The sign of
θ
t-T
B
:t
(i)
is determined by the slope of the
regression line: if the slope is positive,
θ
t-T
B
:t
(i)
has a positive
sign, otherwise its sign is negative. The absolute value
of
θ
t-T
B
:t
(i)
indicates the rise or fall rate of the new cases. If
our above assumption about the currency strength vs.
the COVID status is true,
θ
t-T
B
:t
(i)
can be quite informative
to establish a predictive model. The closer
θ
t-T
B
:t
(i)
is to 90°,
the more rapid country i’s currency could potentially fall
against the USD. Therefore, at any given time
t
, if
θ
t-T
B
:t
(i)
is
greater than a threshold
θ
T
, we calculate
θ
t-T
B
:t
(i)
/(π⁄
2
)
and
use it as a signal to generate a short position,
s
t:t+T
F
(i)
=
{
–2 x
θ
t-T
B
:t
/
π
(i)
0
if θ
t-T
B
:t
>
θ
T
(i)
if θ
t-T
B
:t
≤
θ
T
(i)
,
Equation 1
where
s
t:t+T
F
(i)
is the generated signal for currency
i
at time
t
,
traded during the next
T
F
days.
It should be mentioned that although this strategy is
mainly designed to predict the currency’s depreciation
against the USD, potentially, due to severe COVID
conditions in the corresponding country (and this is why
we have used a negative sign to indicate a short signal),
it still allows longing the currency when
θ
t-T
B
:t
(i)
is negative.
We will further explain this in the next section.
Use of other COVID-19 indicators
As explained, our proposed predictive modelling technique
uses the new daily cases as an input. This was mainly due
to its ease of availability for several currency pairs in our
portfolio. As the level of vaccinations continues to rise
in various countries, the antibody rate increases, and the
population gradually becomes more robust against the
virus, the new daily cases data may become less correlated
with the country’s currency. Therefore, other COVID-19
indicators (antibody rate, number of excess deaths or
vaccination rate, etc.) could become more useful and
used as input for the predictive model. The main difficulty,
however, is to gather this data for all countries, which is
not so straightforward for emerging economies.
26
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
6
. Backtest and experimental results
We evaluate the model over the period of July 2020 to end of August 2021,
assuming July 2020 to April 2021 as the validation, and the final four months
(May, June, July, and August 2021) as the out-of-sample period.
T
B
,
T
F
and
θ
T
are the hyper-parameters of the proposed predictive model, which are
optimized using the data from the validation period.
We incorporate a hierarchical procedure to perform the optimization. At
every stage, we assume only one parameter to be variable and the other
two as constant. Starting with
T
B
, and assuming
T
F
and
θ
T
are 4 days and 0°,
respectively, we calculate the 2021 year-to-date (YTD) information ratio (IR),
when
T
B
is varied from 5 to 60 days. The YTD IR is calculated until the end of
validation period in 2021 (end of April 2021).
As can be seen in Figure 6.1, the model performance peaks at around 25 days
and then steadily declines for larger values for
T
B
. This decline can be because
long look-back window lengths fail to detect immediate trend changes in the
COVID data. On the other hand, selecting too small values for
T
B
can result
in only a few numbers of samples, which prevents the linear regression from
accurately modeling the COVID status in the country and, therefore, hinders
the predictive performance.
In Figure 6.1 we can see the effect of increasing the look-back window size (
T
B
)
over the predictive model performance (here we have assumed
T
F
=4 days and
θ
T
=0°).
27
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
FIGURE 6.1:
MODEL OPTIMIZATION, STEP 1
2.0
-0.5
1.5
1.0
0.5
0.0
0
10
20
30
40
50
60
T
B
(days)
Information Ratio
(Jan 2021 – Apr 2021)
Source: Mesirow
FIGURE 6.2:
MODEL OPTIMIZATION, STEP 2
2.0
-0.5
1.5
1.0
0.5
0.0
-30
-20
-10
10
0
20
30
40
50
Ѳ
T
(days)
Information Ratio
(Jan 2021 – Apr 2021)
Source: Mesirow
FIGURE 6.3:
MODEL OPTIMIZATION, STEP 3
1.8
0.8
1.6
1.4
1.2
1.0
0
5
10
15
20
25
30
35
T
F
(days)
Information Ratio
(Jan 2021 – Apr 2021)
Source: Mesirow
Assuming
T
B
=25 days, we now repeat the same process
for
θ
T
. The results are shown in Figure 6.2. When
θ
T
is varied from -20 to 50 degrees, the performance
peaks around -10 degrees. The YTD Information Ratio,
however, declines for large values for
θ
T
, because at
these values, the resulting signals become too sparse,
and generates lower returns with higher risk. It is
interesting to observe that an optimal
θ
T
has had a
negative value. This shows that, given
T
B
=25, the model
also facilitates longing the currency against the USD,
by allowing
θ
t-T
B
:t
(i)
to be negative in Equation 1. This is a
sensible choice as the decline of the new cases at a very
small rate, could be a sign of the currency recovery and,
potentially, its appreciation against the USD.
Using the optimal value of 25 days for
T
B
from 6.1, here,
we evaluate the effects of varying
θ
T
on the model
performance, assuming
T
F
=4 days.
Finally, assuming
T
B
=25 and
θ
T
=
~
-10°, we perform the
same analysis for
T
F
(6.3).
T
F
indicates for how many
following days, we are going to keep the generated
positions. As illustrated in Figure 6.3, while
T
F
peaks at 4
days, keeping the position for periods longer than 8 days
(
T
F
>8 days) deteriorates the model performance. One
reason for this could be the currency’s recovery due to
the government’s intervention by providing incentives.
For smaller values for
T
F
, however, the model shows
significantly higher performance.
Using the optimal values of
T
B
=25 and
θ
T
=10° from
6.1 and Figure 6.2, we now investigate the model
performance over various values for
T
F
.
28
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Using the computed optimal values for
T
B
=25 (days), θ_
T
=-10° and
T
F
=4 (days),
12
we now can evaluate the performance
of the predictive model over the out-of-
sample period (beginning of May 2021
to the end of August 2021).
Figure 6.4 and Figure 6.5 show the per
currency and total portfolio returns over
the out-of-sample period, respectively.
GBPUSD, AUDUSD, USDTHB, USDPHP
and USDNOK have the top 5 total
out-of-sample returns, while USDTRY,
USDILS, USDINR, USDSEK and USDCHF
are the five worst performing currencies.
The total cumulative returns during the
out-of-sample period is 3.35% with 0.84
standard deviation, resulting in 4.01
information ratio. The monthly returns
are also detailed in the table below.
TABLE 6.1:
MONTHLY RETURNS
DURING THE OUT-OF-SAMPLE
PERIOD (MAY 2021 – AUGUST 2021)
May
2021
June
2021
July
2021
August
2021
Total out-
of-sample
returns
-2.40%
5.95%
0.41%
-0.61%
3.35%
The instances when the strategy
generates short signals over the whole
period of validation and out-of-sample are
shown in Figure 6.6-a to -h, for AUDUSD,
GBPUSD, USDHUF, USDJPY, USDKRW,
USDNOK, USDPHP and USDTHB
currency pairs: The larger the absolute
value of the generated signal, the lighter
the intensity of the gray vertical bar.
FIGURE 6.4:
CUMULATIVE RETURNS PER CURRENCY PAIR OVER THE
OUT-OF-SAMPLE PERIOD (MAY 2021 – AUGUST 2021)
1.5%
-1.0%
0.5%
1.0%
0.0%
-0.5%
2021.05.03
2021.05.17
2021.05.31
2021.06.14
2021.06.28
2021.07.12
2021.07.26
2021.08.09
2021.08.23
GBPUSD
AUDUSD
USDTHB
USDPHP
USDNOK
USDCAD
USDHUF
USDCLP
USDKRW
USDJPY
USDMXN
USDRUB
USDZAR
USDTWD
USDCNH
USDPLN
USDSGD
USDCOP
USDRON
USDBRL
USDCZK
NZDUSD
USDIDR
XEUUSD
USDCHF
USDSEK
USDINR
USDILS
USDTRY
Cumulative returns
Source: Mesirow
FIGURE 6.5:
CUMULATIVE TOTAL RETURNS OVER THE OUT-OF-SAMPLE
PERIOD (MAY 2021 – AUGUST 2021)
10%
-4%
6%
8%
4%
2%
0%
2021.05.03
2021.05.17
2021.05.31
2021.06.14
2021.06.28
2021.07.12
2021.07.26
2021.08.09
2021.08.23
Total returns
-2%
Cumulative returns
Source: Mesirow
29
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
FIGURE 6.6:
MODEL SIGNAL GENERATION
a.
1000
800
600
400
200
0
Daily new COVID-19 cases
0.80
0.78
0.76
0.74
0.72
0.70
FX rate of AUDUSD
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
b.
70000
35000
17500
52500
0
1.42
1.40
1.38
1.36
1.34
1.28
1.30
1.32
FX rate of GBPUSD
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
c.
10000
8000
6000
4000
2000
0
315
310
305
300
295
285
290
FX rate of USDHUF
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
d.
25000
20000
15000
10000
5000
0
110
108
106
102
104
FX rate of USDJPY
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
e.
2000
1500
1000
500
0
1200
1180
1160
1080
1140
1120
1100
FX rate of USDKRW
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
f.
1750
875
0
9.6
8.2
8.9
FX rate of USDNOK
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
g.
17500
8750
4375
0
51.0
47.5
48.0
48.5
49.0
49.5
50.0
50.5
FX rate of USDPHP
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
h.
25000
20000
10000
5000
0
33.5
30.0
30.5
31.0
31.5
32.0
32.5
33.0
FX rate of USDTHB
2020.07.08
2020.08.25
2020.09.22
2020.10.20
2020.11.17
2020.12.15
2021.01.12
2021.02.09
2021.03.09
2021.04.06
2021.05.04
2021.06.01
2021.06.29
2021.07.27
2021.08.24
Daily new COVID-19 cases
Source: Mesirow
30
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
7
. Conclusion
An extensive analysis of the economic impact of the COVID-19 pandemic was
provided. After describing the current state of the world and the effects of
the pandemic on the several currencies, we proposed a predictive model that
utilized COVID-19 indicators to generate FX trading signals.
Over a portfolio of 28 currency pairs, the model applied a simple linear
regression to the number of daily new COVID-19 cases in each country to,
mainly, short the currencies against the USD. After optimizing the model’s
hyper-parameters over the validation data, this model resulted in 3.35%
positive returns over the out-of-sample period.
One of the challenges of writing a paper during a pandemic is that the
situation is constantly evolving and, sure enough, during the review stage
for this paper a new variant of concern, Omicron, came to light. Omicron has
around 50 mutations, including around thirty in the area of the genome that
encodes the spike protein of the virus, a much higher number than previous
variants.
The mutations in the spike protein are particularly worrying as the spike
protein is targeted by vaccines and changes there increase the risk that the
new variant will be able to evade immune responses, either from vaccines or
natural immunity acquired after infection. Analysis of the mutations also made
clear that the Omicron variant is more transmissible than the Delta variant,
which is already more transmissible than the original Covid strain.
The website nextstrain.org provides graphical analysis of a large global
database of virus sequencing data. The sequence data for the Omicron variant
indicate that it evolved from a mid-2020 covid strain. One hypothesis is that
an immunosuppressed individual suffered a long term chronic covid infection,
allowing the virus within the individual to acquire a large number of mutations,
before escaping back into the general population. If the individual was
vaccinated, this would apply selective pressure on the virus to evade vaccine
antibodies.
31
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
After first being identified in sequencing data in Botswana,
Hong Kong, and South Africa’s Guateng province towards
the end of November 2021, the Omicron variant has been
detected all around the world. The new variant causes an
anomalous result in polymerase chain reaction (PCR) tests,
termed S-gene dropout, and this has allowed the spread of
the new variant to be tracked more easily than relying solely
on sequencing data.
The Omicron variant has led to a rapid growth in the number
of cases among individuals who have either been vaccinated
or had a prior Covid infection and December 2021 and
January 2021 saw a spike in new cases around the world
(Figure 8.1).
The emergence of Omicron has served as a timely reminder
that the pandemic will not be over, and the risk of new
variants of concern will remain, until all countries have
vaccinated their citizens and it has highlighted the issue of
vaccine inequality between rich and poor countries.
The analysis presented in this paper provides a framework
for analysis of how a new wave of infections will affect
countries. We can expect countries with higher levels of
vaccination and effective booster programs to experience
a lower proportion of severe cases and hospitalizations in
a new wave of infections, reducing the pressure on their
health services and reducing the need for lockdowns and
other mitigation measures that have a detrimental impact on
economic activity. Conversely, countries with low vaccination
levels are likely to fare worse, although it is still uncertain to
what extent immunity from prior covid infections, which are
often higher in countries with low vaccination rates, provides
protection against the omicron strain.
There is now two years of accumulated knowledge and
experience of how covid spreads, how to treat it and how to
make vaccines that work against it. While a new variant with
a high risk of reinfection is obviously concerning, countries
are in a much better state of preparedness than at the start
of 2020, and we should expect the impact on economic
activity from a new wave of infections to be less severe.
FIGURE 8.1:
NEW CASES PER MILLION (DECEMBER 1, 2021 – JANUARY 31, 2021)
20,000
160,000
0
80,000
40,000
60,000
100,000
140,000
120,000
2021.12.01
2021.12.05
2021.12.09
2021.12.13
2021.12.17
2021.12.21
2021.12.25
2021.12.29
2022.01.02
2022.01.06
2022.01.10
2022.01.14
2022.01.18
2022.01.22
2022.01.26
2022.01.30
Africa
New cases per million
Asia
Europe
North America
Oceania
South America
Source: Our World in Data
32
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Appendix
FX Rates and COVID
PHILIPPINES | PHPUSD
4
-2
2
3
1
0
-1
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
POLAND | PLNUSD
3
-3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
RUSSIA | RUBUSD
3
-3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
SOUTH AFRICA | ZARUSD
3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
Normalised
Normalised new cases 7-days moving average
Normalised new death 7-days moving average
Normalised exccess mortality monthly
33
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
SOUTH KOREA | KRWUSD
4
3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
THAILAND | THBUSD
5
3
4
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
TURKEY | TRYUSD
4
3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
BRAZIL | BRLUSD
3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
Normalised
Normalised new cases 7-days moving average
Normalised new death 7-days moving average
Normalised exccess mortality monthly
34
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
COLOMBIA | COPUSD
3
1
2
0
-1
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
CZECH REPUBLIC | CZKUSD
3
1
2
0
-1
-3
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
HUNGARY | HUFUSD
4
3
1
2
0
-1
-3
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
INDIA | INRUSD
4
3
1
2
0
-1
-3
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
Normalised
Normalised new cases 7-days moving average
Normalised new death 7-days moving average
Normalised exccess mortality monthly
35
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
ISRAEL | ILSUSD
4
3
1
2
0
-1
-3
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
MEXICO | MXNUSD
4
3
1
2
0
-1
-3
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
NEW ZEALAND | NZDUSD
10
8
4
6
2
0
-2
2020.04.14
2020.07.23
2020.10.31
2021.02.08
2021.05.19
2021.08.27
Normalised data
Source: Mesirow
Normalised
Normalised new cases 7-days moving average
Normalised new death 7-days moving average
Normalised exccess mortality monthly
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
mesirow.com
About Mesirow
Mesirow is an independent, employee-owned financial services firm founded
in 1937. Headquartered in Chicago, with locations around the world, we serve
clients through a personal, custom approach to reaching financial goals and
acting as a force for social good. With capabilities spanning Global Investment
Management, Capital Markets & Investment Banking, and Advisory Services,
we invest in what matters: our clients, our communities and our culture.
To learn more, visit
mesirow.com
or
contact Joe Hoffman at 312.595.7019
or
joseph.hoffman@mesirow.com
.
37
March 2022 | Mesirow Currency Management | COVID-19, Economy and FX: Past, Present and Future
Mesirow Financial Currency Management (“MCM”) is a division of Mesirow Financial Investment
Management, Inc. (“MFIM”) a SEC registered investment advisor. The information contained herein is
intended for institutional clients, Qualified Eligible Persons and Eligible Contract Participants and is for
informational purposes only. This information has been obtained from sources believed to be reliable
but is not necessarily complete and its accuracy cannot be guaranteed. Any opinions expressed are sub
-
ject to change without notice. It should not be assumed that any recommendations incorporated herein
will be profitable or will equal past performance. Mesirow Financial does not render tax or legal advice.
Nothing contained herein constitutes an offer to sell or a solicitation of an offer to buy an interest in any
Mesirow Financial investment vehicle(s). Any offer can only be made through the appropriate Offering
Memorandum. The Memorandum contains important information concerning risk factors and other
material aspects of the investment and should be read carefully before an investment decision is made.
Currency strategies are only suitable and appropriate for sophisticated investors that are able to lose
all of their capital investment.
This communication may contain privileged and/or confidential information. It is intended solely for
the use of the addressee. If this information was received in error, you are strictly prohibited from
disclosing, copying, distributing or using any of this information and are requested to contact the sender
immediately and destroy the material in its entirety, whether electronic or hardcopy.
Certain strategies discussed throughout the document are based on proprietary models of MCM’s or its
affiliates. No representation is being made that any account will or is likely to achieve profits or losses
similar to those referenced.
Performance pertaining to the Currency Risk Management Overlay strategies is stated gross of fees.
Performance pertaining to the Currency Alpha and Macro strategies may be stated gross of fees or
net of fees. Performance information that is provided net of fees reflects the deduction of implied
management and performance fees. Performance information that is provided gross of fees does not
reflect the deduction of advisory fees. Client returns will be reduced by such fees and other expenses
that may be incurred in the management of the account. Simulated model performance information
and results do not reflect actual trading or asset or fund advisory management and the results may not
reflect the impact that material economic and market factors may have had, and can reflect the benefit
of hindsight, on MCM’s decision-making if MCM were actually managing client’s money in the same
manner. Performance referenced herein for Currency Risk Management Overlay strategies prior to May
2004, the date that the Currency Risk Management team joined Mesirow Financial, occurred at prior
firms. Performance referenced herein for Currency Alpha and Macro strategies prior to October 1, 2018,
the date that the Currency Alpha and Macro Strategies team joined Mesirow Financial, occurred at
prior firms. Any chart, graph, or formula should not be used by itself to make any trading or investment
decision. Any currency selections referenced herein have been included to illustrate the market impact
of certain currencies over specific time frames. The inclusion of these is not designed to convey that
any past specific currency management decision by MCM would have been profitable to any person. It
should not be assumed that currency market movements in the future will repeat such patterns and/or
be profitable or reflect the currency movements illustrated above.
Comparisons to any indices referenced herein are for illustrative purposes only and are not meant to
imply that a strategy’s returns or volatility will be similar to the indices. The strategy is compared to the
indices because they are widely used performance benchmarks.
The MSCI ACWI Index is a free float-adjusted market capitalization weighted index that is designed to
measure the equity market performance of developed and emerging markets. The MSCI ACWI consists
of 46 country indices comprising 23 developed and 23 emerging market country indices.
Australian Investors: The information contained herein is intended for Wholesale Clients only and is
for informational purposes only. This document is not a prospectus or product disclosure statement
under the Corporations Act 2001 (Cth) (Corporations Act) and does not constitute a recommendation to
acquire, an invitation to apply for, an offer to apply for or buy, an offer to arrange the issue or sale of,
or an offer for issue or sale of, any securities or investment service in Australia, except as set out below.
The strategy has not authorised nor taken any action to prepare or lodge with the Australian Securities
& Investments Commission an Australian law compliant prospectus or product disclosure statement.
Accordingly, this strategy and document may not be issued or distributed in Australia other than by way
of or pursuant to an offer or invitation that does not need disclosure to investors under Part 6D.2 or
Part 7.9 of the Corporations Act, whether by reason of the investor being a ‘wholesale client’ (as defined
in section 761G of the Corporations Act and applicable regulations) or otherwise. This document does
not constitute or involve a recommendation to acquire, an offer or invitation for issue or sale, an offer
or invitation to arrange the issue or sale, or an issue or sale, of any strategy or investment service to a
‘retail client’ (as defined in section 761G of the Corporations Act and applicable regulations) in Australia.
Canadian Investors: The information contained herein is intended for Permitted Clients only and is for
informational purposes only. This confidential material pertains to the offering of the currency strategies
described herein only in those jurisdictions and to those persons where and to whom they may be
lawfully offered for sale, and only by persons permitted to sell such strategies. This material is not, and
under no circumstances is to be construed as, an advertisement or a public offering of the strategies
described herein in Canada. No securities commission or similar authority in Canada has reviewed
or in any way passed upon this document or the merits of the strategies described herein, and any
representation to the contrary is an offence.
EU Investors: The information contained herein is intended for Professional Clients as the term is de
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fined by MiFID II and is for informational purposes only. Recipients that are classified under MiFID II as
retail clients must opt up to Professional Clients before receiving any services from Mesirow Financial
Currency Management.
Japanese Investors: Mesirow Financial Currency Management provides discretionary investment
management services to managed accounts held on behalf of qualified investors only. MCM will not
act as agent or intermediary in respect of the execution of a discretionary investment management
agreement. Please note that this presentation is intended for educational purposes and solely for the
addressee and may not be distributed.
Hong Kong Investors: The contents of this document have not been reviewed by any regulatory authority
in Hong Kong. You are advised to exercise caution in relation to the contents of this document. You
should obtain independent professional advice prior to considering or making any investment. The
investment is not authorized under Section 104 of the Securities and Futures Ordinance of Hong Kong
by the Securities and Futures Commission of Hong Kong. Accordingly, the distribution of this Presen
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tation Material and discretionary management services in Hong Kong are restricted. This Presentation
Material is only for the use of the addressee and may not be distributed, circulated or issued to any
other person or entity.
South Korean Investors: Upon attaining a client, Mesirow Financial Investment Management, Inc.
(“MFIM”) will apply for the appropriate licenses and retain the services of a local licensed intermediary
(a Korean financial investment company). In the interim, MFIM will rely on and sub-delegate to Me
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sirow Advanced Strategies, Inc. (“MAS”).
Singapore Investors: Mesirow Financial Currency Management provides discretionary investment
management services to managed accounts held on behalf of qualified investors only. MCM will not
act as agent or intermediary in respect of the execution of a discretionary investment management
agreement. Please note that this presentation is intended for educational purposes and solely for the
addressee and may not be distributed.
Swiss Investors: Services are only offered to Regulated Qualified Investors, as defined in Article 10 of the
Swiss Collective Investment Scheme Act. There can be no guarantee investment advice will be profitable
or meet its investment objectives.
UAE Investors: This information does not constitute or form part of any offer to recommend, issue or
sell, or any solicitation of any offer to subscribe for or purchase, any securities or investment products
or strategies in the UAE (including the Dubai International Financial Centre and the Abu Dhabi Global
Market) and accordingly should not be construed as such. Furthermore, this information is being made
available on the basis that the recipient acknowledges and understands that the entities and securities
to which it may relate have not been approved, licensed by or registered with the UAE Central Bank,
the Dubai Financial Services Authority, the UAE Securities and Commodities Authority, the Financial
Services Regulatory Authority or any other relevant licensing authority or governmental agency in the
UAE. The content of this report has not been approved by or filed with the UAE Central Bank, the Dubai
Financial Services Authority, the UAE Securities and Commodities Authority or the Financial Services
Regulatory Authority.
United Kingdom Investors: In the United Kingdom, this communication is directed only at persons who
fall within the definition of: (i) “investment professionals” as defined in COBS 4.12 and Article 14 of the
Financial Services and Markets Act 2000 (Promotion of Collective Investment Schemes) (Exemptions)
Order 2001 (the “PCISE Order”); or (ii) “high net worth companies, unincorporated associations etc” as
defined in COBS 4.12 and Article 22(2)(a) to (d) of the PCISE Order (all such persons together being re
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ferred to as “Relevant Persons”). This communication must not be acted on or relied on by persons who
are not Relevant Persons. Any investment or investment activity to which this communication relates is
available only to Relevant Persons and will be engaged in only with Relevant Persons.
Mesirow refers to Mesirow Financial Holdings, Inc. and its divisions, subsidiaries and affiliates. The
Mesirow Financial name and logo are registered service marks of Mesirow Financial Holdings, Inc.,
© 2022, Mesirow Financial Holdings, Inc. All rights reserved. Investment management services pro
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vided through Mesirow Financial Investment Management, Inc., a SEC registered investment advisor, a
CFTC registered commodity trading advisor and member of the NFA, or Mesirow Financial International
UK, Ltd. (“MFIUK”), authorized and regulated by the FCA, depending on the jurisdiction.
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