fx edge: introducing the danske g10 meva model

Important disclosures and certifications are contained from page 17 of this report. www.danskeresearch.com

Investment Research — General Market Conditions

We introduce the Danske G10 Medium-Term Valuation (MEVA) model which

is a quantitative framework for bridging the short and long term of FX

economics by providing ‘valuation anchors’ for major currency pairs.

MEVA models real bilateral exchange rates based on a selected set of

macroeconomic factors but results are converted to nominal ‘fundamental’

values for relevant currency pairs at a given point in time. We argue that these

are as relevant as directional indicators on a 12-36M horizon.

The G10 MEVA currently points to EUR/USD being undervalued with a model

estimate of around 1.28, hinting that a correction higher in the cross is looming.

In contrast, the moves higher in EUR/SEK and EUR/NOK alike in recent years

are largely warranted by model fundamentals, suggesting that no large

corrections are due.

Given that the MEVA has a half life of around two years, we suggest using the

model signals as a key input for 1-3Y forecasts and associated hedging

recommendations. Specifically, on the basis of, among other things, current

MEVA signals, we recommend EUR-based clients lock in USD income/assets

beyond the 6M horizon. We will publish updates of MEVA on a quarterly basis.

Bridging the gap between the short and the long term

Economic theory provides a range of suggestions as to what determines exchange rates on

different horizons, but with no over-arching theory in place, it remains an empirical

question of what drives FX markets in the short, medium and long term, respectively.

Bridging the gap: FX economics and Danske FX models

Source: Danske Bank Markets

1 October 2015

Senior Analyst, PhD Christin Tuxen +45 45 13 78 67 [email protected]

FX Edge

Introducing the Danske G10 MEVA model

Danske Bank FX models compared

Source: Reviews, DBM. Note: STFM=Danske

Short-Term Financial Model, MEVA= Danske

Medium-Term Valuation Model, PPP=Danske PPP

calculation.

STFM MEVA PPP

EUR/USD -2% -14% -10%

EUR/NOK 2% 9% 13%

EUR/SEK 0% 3% 15%

USD/JPY 2% 13% 31%

GBP/USD -2% -14% -5%

EUR/CHF 0% -15% -21%

AUD/USD 1% -7% 0%

NZD/USD 0% -9% 3%

USD/CAD 3% 13% 9%

Model deviation (% rel to spot)

mailto:[email protected]

2 | 1 October 2015 www.danskeresearch.com

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Until now, the Danske FX directional model framework has primarily consisted of two

types of indicators.

First, the Danske Purchasing Power Parity (PPP) calculation offers a long-term fix point

for the currency market. This set-up is based on effective exchange rates which ensures

internally consistent PPP levels among the G10 currencies. Empirically it is well known

that ‘the law of one price holds only in the very long term with half-lives generally found

to at least three-five years.

Second, the Danske FX Short-Term Financial Models (STFM) provide hints of short-run

mispricing. These take into account a range of financial factors including relative interest

rates and are based on 200D rolling estimations. The STFM may be used to identify

misalignments that should be corrected in the near term, say within weeks, see Danske

Bank's FX Quant Strategy 8 April 2015, for the latest update.

Danske PPP calculation points to 1.24

as long-term fix point Danske STFM suggests 1.12 is ‘fair’

Source: Macrobond Financial, DBM Source: Macrobond Financial, DBM

However, the gap between the horizons at which these two types of models may be

employed is potentially rather wide, and neither takes account of real (macroeconomic)

factors which we believe should be key drivers of real exchange rates in the medium

term, i.e. explain deviations from PPP. In this context, the Danske FX Medium-Term

Model (MEVA) framework is meant to be a key input, but not a forecast, for where a

currency pair is likely to be headed on a 12-36M horizon before PPP forces would be

expected to set in. Indeed, at times it does not make sense to incorporate a firm view on

central bank policy divergence (capturing nominal trends) beyond the 12M horizon,

which in turn highlights the need for a fundamental-based anchor (capturing real trends)

in the FX market.

In some cases we may have a clear call on where fundamental factors are headed, for

example, terms of trade, which may imply that we expect the ‘fundamental’ value to

move in a certain direction; this would of course have an impact on our medium-term

view on a cross. But in the absence of such calls and external shocks, the MEVA

framework provides a disciplined way of seeing through the impetus of short-term

factors. We stress that models should be regarded as only one input for forming views and

forecasts, and should not mechanically be employed for making projections.

The paper is organised as follows: we first discuss the MEVA set-up in short followed by

an overview of current model signals and, finally, discuss how to use the model signals.

Details on the estimation procedure and a presentation of the data are left for Appendix A

and B, respectively.

http://danskeanalyse.danskebank.dk/abo/FXQuantTrades080415/$file/FXQuantTrades_080415.pdf

http://danskeanalyse.danskebank.dk/abo/FXQuantTrades080415/$file/FXQuantTrades_080415.pdf


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Introducing the MEVA - what we do and why in short

The MEVA framework falls within the class of Behavioural Equilibrium Exchange Rate

(BEER) models which estimate equilibrium exchange rates from a set of explanatory

variables. This contrasts with Fundamental Equilibrium Exchange Rate (FEER) models

which calculate equilibrium exchange rates based on assumptions regarding how to

obtain external balance. Our estimation procedure is inspired by the BEER models

developed by the IMF, see inter alia IMF WP/98/67, WP/07/296, WP/08/13, but we make

a crucial amendment to the prevailing IMF set-up:

We following the existing literature in modelling real (rather than nominal) exchange

rates because these are what economic theory has something to say about in the medium

term. But we crucially consider bilateral rather than effective exchange rates as these are

what clients (and we) are ultimately interested in. This also allows for more clarity in

understanding what is driving the ‘fundamental’ model estimates.

Importantly, we take a panel (rather than a single-equation) approach to estimation, see

Appendix A for details: this effectively entails that we assume that the dynamics of G10

currencies can be described by one model, i.e. that the relationship between the real

exchange rate and medium-term fundamentals is the same for, say, EUR/USD as it is for,

say, USD/JPY. This assumption may of course be questioned but if (at least

approximately) valid allows for an highly improved model fit.

We build upon IMF work for generating a list of candidate variables to enter the medium-

term equilibrium relationship with the real exchange rate. The G10 MEVA is however

based upon a mere two medium-term drivers: a gauge of the Balassa-Samuelson effect

(see box), and terms of trade. While both of these may be measured by a range of

different series, we use the CPI-PPI differential for the former and the export-import

deflator differential for the latter. When estimated within our model framework, both

factors are found to have the economically right signs (both positive) and be statistically

significant.

EUR/USD as an example: Balassa

Samuelson effect (CPI-PPI)

EUR/USD as an example: terms of

trade

Source: Macrobond Financial, Danske Bank

Markets

Source: Macrobond Financial, Danske Bank

Markets

A few factors not included in the model also deserve mentioning. First, we do not include

a direct measure of external wealth/debt: while this is usually considered a candidate

medium-term factor, our preferred measure for this, the international investment position

(IIP) to GDP, turns out not to be both economically and statistically identified. This may

either be because IIP is an inappropriate measure of external wealth/debt and/or because

another factor such as terms of trade already captures the key dynamics. Second, we do

not include the real interest-rate differential: the rationale is that this captures trends in

relative monetary policy that we arguably want to see through on a medium-term horizon.

Balassa-Samuelson effect explained

Source: Danske Bank Markets


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For guidance on what relative interest rates and other short-term factors suggest as fair for

the FX market at a given point in time we refer to our STFM framework.

We use monthly data (interpolated if required) from 1985 and onwards albeit for many

countries data series do not start until 1996. The panel consists of AUD/USD, NZD/USD,

USD/CAD, USD/JPY, GBP/USD, EUR/USD, EUR/SEK, EUR/NOK and EUR/CHF.

Note that EUR/DKK is excluded from the estimation due to the Danish peg but that

EUR/CHF is included despite the SNB floor 2011-15. In choosing candidate factors we

emphasise timeliness of data releases, and for data sources we emphasise consistency

across countries/regions. The data is discussed in greater details in Appendix B.

Estimation of the MEVA model broadly takes place in two steps – details are outlined in

Appendix A:

1. We first estimate a static medium-term equilibrium relation between the level of the

real exchange rate and a set of relevant determinants; this provides us with

‘fundamentally justified’ levels for each currency pair which is then converted from

real to nominal terms using the relevant CPI differential.

2. Next, we estimate a dynamic short-term adjustment relation for the change in the

real exchange rate to validate that the real exchange rate adjusts as appropriate in

order to warrant the use of the model as a directional indicator in the medium term.

Separately we estimate the half-life of model deviations.

We note that significant equilibrium correction takes place, thus validating the use of

MEVA estimates as directional indicators. Moreover, we find the half-life of model

deviations to be around two years, suggesting the model is relevant as an anchor on a 12-

36M horizon.

In presenting the model results we report a so-called ‘confidence region’ alongside the

actual spot and MEVA estimates: this is based on +/- 2 standard deviations of the

historical model deviations but should be interpreted with care as the distribution of the

panel model estimator is not known.

Broad overview of the MEVA idea

Source: DBM


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G10 model results – what we find and what it means

In the following we present an overview of the current model signals for each currency

pair. An overview of model deviations in real versus nominal terms in percentage terms is

given below: in nominal terms, EUR/USD, GBPY/USD and EUR/CHF stand out as the

most undervalued crosses whereas USD/JPY and USD/CAD are markedly overvalued

according to the MEVA framework.

MEVA model deviations in real vs nominal terms

Source: Eviews, DBM

Correction higher is looming for EUR/USD

The model currently suggests that EUR/USD is substantially undervalued with the panel

model suggesting 1.28 as ‘fundamentally justified’. In fact, the ‘fundamental’ value for

EUR/USD has been held up over the past decade by the Balassa-Samuelson effect being

USD-negative (relative to EUR). As oil prices rose in the 2000s, terms of trade moved in

favour of EUR relative to USD but the recent oil-price drop and reduced reliance on

energy imports has made this factor a USD positive over the past year. While the panel

(preferred) and single-equation model for EUR/USD differ slightly (the latter suggests a

‘fundamental’ value of 1.22; not shown here), both hint that a correction higher in the

cross is looming.

-20%-15%-10%

-5%0%5%

10%15%20%

MEVA model deviation in real terms (% rel to spot) in nominal terms

overvalued

undervalued

EUR/USD fundamentally undervalued on MEVA estimates

Source: Eviews, Macrobond Financial, DBM


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Currently, we expect EUR/USD to head lower short term on monetary-policy divergence

with the Fed set to hike at a time (likely in December) when the ECB may deliver an

extension of the QE scheme. However, history shows that a first Fed hike is not in itself

enough to steer a clear direction for the USD. In this respect, the MEVA estimates

provide a key input to our longer-term forecasts: this is one reason we look for a move

higher in the cross on a 12M horizon, see FX Forecast Update: It ain't over till the Fed

sings.

Fed-ECB divergence set to drag

EUR/USD lower...

... but first Fed hike in itself does not set

a clear direction for USD


Comparing the signals for EUR/USD from our three different types of models we note

that the MEVA suggests an even higher value for the cross (1.28) than our PPP estimate

(1.25). That is, the MEVA hints that a move above the current PPP level may in fact be

warranted by medium-term factors. The STFM naturally tracks the actual spot rate better

than the other models as we allow for a range of financial factors and a rolling estimation

window, but the caveat is that using financial factors as explanatory variables imply that

we will not be able to spot dislocations in the FX market before a correction takes place

in e.g. the fixed-income market. As such, the STFM, MEVA and PPP estimates should

complement each other well across time horizons.

Scandies undervalued – but not much!

The Scandies have seen model fundamentals move in favour of a higher EUR/NOK and

EUR/SEK alike in recent years. This implies that we can largely explain the up-trends in

the crosses on the basis of a negative Balassa-Samuelson effect and deteriorating terms of

trade. That said, some potential for a move lower in both crosses in the medium term is

nevertheless supported by the MEVA with the ‘fundamental’ value for EUR/NOK and

EUR/SEK at 8.65 and 9.15, respectively.

Fundamentals dragging EUR/NOK

higher in recent years

EUR/SEK uptrend supported by BS

effect and terms of trade since 2013

Source: Eviews, Macrobond Financial, DBM Source: Eviews, Macrobond Financial, DBM

EUR/USD: model inputs at various

horizons

Source: Macrobond Financial, DBM

http://danskeanalyse.danskebank.dk/abo/FXForecastUpdate160915/$file/FX_Forecast_Update_160915.pdf

http://danskeanalyse.danskebank.dk/abo/FXForecastUpdate160915/$file/FX_Forecast_Update_160915.pdf


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Striking misalignments in other majors

GBP/USD is markedly undervalued currently. Notably the level shift in GBP/USD

following the 2008 financial crisis was not warranted by panel-model drivers with the

Balassa-Samuelson effect in fact GBP-positive in recent years. This suggests that

GBP/USD should move substantially higher towards the 1.70 level in the medium term.

We note however that the panel and single-equation models (not reported here) for cable

differ markedly with the latter suggesting the cross being close to fundamentals.

The actual value of USD/JPY remains within the reported ‘confidence region’ but is

actually at present outside the single-equation model bands (not reported here). Looking

at model fundamentals terms of trade has been a JPY negative for an extended period of

time but the Balassa-Samuelson effect has largely countered this, being a clear positive

since 2008. On balance this leaves USD/JPY markedly overvalued by some 13%

according to MEVA.

The EUR/CHF model results clearly points to the Swissie being overvalued at present,

but we note that results should be interpreted with caution given the change in policy

regime in the period under consideration with the floor on the cross in place 2011-15.

Thus the MEVA hints that EUR/CHF will eventually have to correct higher still with a

move to the mid 1.20s not unreasonable in medium term when/if the deflation issue fades.

GBP/USD undervalued on panel

model... ... while USD/JPY is overvalued

EUR/CHF headed for higher levels -

eventually

Source: Eviews, Macrobond Financial, DBM Source: Eviews, Macrobond Financial, DBM Source: Eviews, Macrobond Financial, DBM


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Weaker commodity currencies warranted – but CAD looks undervalued

For the major commodity currencies, both the Balassa-Samuelson effect and terms of

trade have been clear positives during the 2000s; this has obviously reversed in recent

years. For both AUD/USD and NZD/USD, the depreciation witnessed over the past few

years is thus largely warranted by fundamentals. However, despite the oil price drop,

USD/CAD is now starting to look somewhat overvalued with MEVA suggesting the

cross is 13% overvalued.

AUD/USD closer to ‘fundamentals’... ... and same goes for NZD/USD USD/CAD model value has risen – but

not as much as spot

Source: Eviews, Macrobond Financial, DBM Source: Eviews, Macrobond Financial, DBM Source: Eviews, Macrobond Financial, DBM


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FX Strategy: using the MEVA signals – how to

While the MEVA is meant as a tool for bridging short- and the long-term factors, we

stress that the results should be viewed as just one of many inputs in forming longer-term

views on the FX market. With an estimated half-life of around two years, we see the

MEVA framework as a key input for forming directional views on a 12-36M

horizon. Double-digit deviations of current spot values from MEVA estimates are found

in EUR/CHF (15% undervalued), EUR/USD (14% undervalued), GBP/USD (14%

undervalued) as well as in USD/JPY (13% overvalued), USD/CAD (13% overvalued) and

EUR/NOK (10% overvalued).

While CHF will likely remain overvalued for some time still given the largely empty

SNB toolbox, we stress that EUR/USD should be in for a rebound on a 6-12M horizon

provided ECB does not resort to more drastic measures than QE (i.e. stay away from

cutting the deposit rate further). Thus, a key message from the MEVA model at

present for EUR-based clients looking to hedge USD income/assets beyond the 6M

horizon should consider locking in EUR/USD on any drop in relation to a first Fed

hike (likely to arrive in December) as these (low) levels are unlikely to persist longer

term.

Similarly, we stress that while exposed to the pricing of further Norges Bank easing near

term, EUR/NOK is now substantially overvalued on MEVA despite the oil sell-off, and

we maintain that the cross should move lower again further out, see Norges Bank Review:

A 25bp rate cut and an easing bias. Thus while risks are still primarily on the downside

for oil and thus on the upside for EUR/NOK, the pair should not be expected to hover at

these levels for an extended period of time. NOK-based clients with EUR income/assets

should therefore consider locking in current levels of EUR/NOK.

Finally, we note that while USD/JPY is clearly overvalued according to MEVA, we do

not look for an imminent correction of this model deviation as we see reasons why

markets may price continued monetary-policy divergence in favour of USD/JPY for some

time still. Thus, medium-term dynamics as captured by MEVA could take longer to play

out in this case.

Going forward, we shall use the MEVA signals as a medium-term anchor hinting at

where the FX market should be headed in the absence of new shocks to the model

drivers, CPI, or short-term factors such as monetary policy. As such, the MEVA

signals could be used as a guide for longer-term (1-3Y) forecasts and hedging

decisions for corporates and institutions alike. We will publish updates of the MEVA

on a quarterly basis.

Danske FX model estimates compared

Source: Eviews, DBM. Note: STFM=Danske Short-Term Financial Model, MEVA= Danske Medium-Term

Valuation Model, PPP=Danske PPP calculation.

Spot STFM MEVA PPP STFM MEVA PPP

EUR/USD 1.12 1.14 1.28 1.24 -2% -14% -10%

EUR/NOK 9.50 9.27 8.65 8.22 2% 9% 13%

EUR/SEK 9.41 9.42 9.15 7.97 0% 3% 15%

USD/JPY 120.31 118.24 105.00 82.60 2% 13% 31%

GBP/USD 1.52 1.54 1.73 1.60 -2% -14% -5%

EUR/CHF 1.09 1.09 1.26 1.32 0% -15% -21%

AUD/USD 0.70 0.70 0.75 0.70 1% -7% 0%

NZD/USD 0.64 0.64 0.70 0.62 0% -9% 3%

USD/CAD 1.34 1.30 1.16 1.21 3% 13% 9%

Danske model estimates Model deviation (% rel to spot)

http://danskeanalyse.danskebank.dk/abo/NorgesBankSeptemberReview240915/$file/NorgesBank_SeptemberReview_240915.pdf

http://danskeanalyse.danskebank.dk/abo/NorgesBankSeptemberReview240915/$file/NorgesBank_SeptemberReview_240915.pdf


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Appendix A: MEVA estimation procedure – how we do it in detail

In the following we discuss some general modelling considerations and present the

MEVA estimation procedure in details.

First note that we explicitly factor in that PPP does not hold as an equilibrium concept for

the time horizon under consideration, i.e. in the ‘medium term’, which in a statistical

sense imply that the real exchange rate is treated as non-stationary. As a result, we use

cointegration techniques to ensure valid inference.

Regime shifts and data availability limit the historical information on the dynamics

governing each currency pair. Thus, while we may estimate an equilibrium relation for

each currency pair in isolation, we argue that pooling information across G10 currency

pairs while assuming a homogenous equilibrium relation for these, i.e. we take a panel

approach to estimation, arguing that this provides a more informed modelling procedure.

We shall thus use the G10 panel model as the main reference for judging how far off

“fundamental” value a currency pair is at any given point in time, but individual (single-

equation) models are used as a cross-check and as a guide to model uncertainty. Note that

confidence bans are not readily available for the panel cointegration model as the

distribution of the panel estimator is not known.

Also, note that while we estimate the model in real terms we are interested in the nominal

exchange rate. In conducting this real-to-nominal transformation of the model estimates

we use the relevant CPI differential at any given point in time. In doing this, we

essentially impose medium-term homogeneity between the nominal exchange rate and the

CPI differential. Furthermore, when arguing that the MEVA ‘equilibrium error’ tells us

how the nominal exchange rate should correct over time, we are inherently assuming that

adjustment takes place solely via the exchange rate, which may not necessarily be the

case. That said, CPI differentials tend to be rather persistent compared with nominal

exchange rates (i.e. movement in the real exchange rate is primarily caused by the

nominal exchange rate), suggesting this is a reasonable assumption in the medium term.

Finally, the panel model structure makes it possible to calculate a model-implied value for

currency pairs not directly included in estimation of the model. While USD/SEK, for

example, should not be included in estimating the model due to multi-collinearity

(EUR/USD and EUR/SEK included already), the panel structure of the model suggests

that the estimated relations should be more generic in nature, and thus that is may be

justifiable to use these for calculating ‘fundamental’ levels for other crosses. Thus, we

may calculate a model-implied value for USD/SEK based on US versus Swedish

fundamental factors (conditional on certain assumptions about the individual-specific

intercept) or we could simply calculate it from the model-implied values for EUR/USD

and EUR/SEK.

Below, we outline the econometric details of the MEVA estimation procedure.

Real vs nominal EUR/USD



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1. Single-equation estimation: a first glance at relations (cross check)

Estimate a single cointegrating relation for each currency pair individually (equation-

by-equation) by Fully Modified OLS (FMOLS with a restricted constant); notably

this provides confidence bands (note that panel estimation does not).

Test for cointegration and equilibrium correction using standard OLS.

2. Panel model estimation: Danske MEVA framework

Test for the number of cointegrating relations and check at most one cointegrating

relation is present (Johansen-Fisher test).

Estimate the panel cointegration model by weighted FMOLS (restricted constant).

Check for economic and statistical identification by judging the validity of the

parameter signs and their significance, respectively.

3. Testing validity of MEVA

Test for cointegration using panel unit root test (Im-Pesaran-Shin test).

Test for equilibrium correction (fixed-effects OLS).

Calculate half-life of equilibrium error (fixed-effects OLS).

4. Using MEVA

Transform model estimates from real to nominal using relevant CPI differentials.

Compare model ('fundamental') values to actual ones; note that confidence bands are

not readily available as the distribution of the estimator is non-standard and requires

simulation; instead we report a ‘confidence region’ based on the standard error of the

equilibrium error.

Use for conditional forecasting (requires forecasts of the explanatory factors).

Overview of full MEVA estimation procedure

Source: DBM

EUR/USD: single-equation vs G10

panel results

Source: Eviews, Macrobond Financial, DBM


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An overview of the modelling procedure in equations is given below.

Danske G10 MEVA procedure in equations

Source: DBM


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Appendix B: G10 data series – what are the drivers?

We briefly discuss potential medium-term drivers and then present the data series used in

the G10 MEVA.

Balassa-Samuelson effects. Theoretically, these are caused by productivity

differences between the tradable and non-tradable sector leading to disproportional

upward pressure on wages and prices in the non-tradable sector, thus explaining real

appreciation. This may be captured by the relative CPI-PPI differential (as PPI

includes mainly tradables whereas CPI captures both) or real-income differentials (cf.

Penn effect). Alternatively, differentials in labour productivity or in real income could

be used. Another factor potentially capturing (part of) this effect is the ratio of

government consumption (falling more intensively on non-tradables) to GDP. In the

G10 MEVA we use CPI-PPI differentials.

Terms of trade. Movements in the price of exports relative to imports caused by, for

example, commodity-price moves may have significant wealth effects for a country;

as such terms of trade are related to the accumulation of net foreign assets. In the G10

MEVA we use export-import deflator differentials.

One more candidate factor warrants a brief discussion, namely external

wealth/indebtedness. A country accumulating foreign assets will typically see a stronger

exchange rate and thus run larger current-account deficits, ceteris paribus. This effect may

be captured by various measures of net foreign assets but such data are usually not readily

available and/or frequently updated. The ‘net international investment position’ is an

alternative and more timely indicator available from national sources but this does not

account for valuation changes, capital transfers, debt reductions etc. Finally, an

alternative to these stock measures is the current-account balance (flow). All such

measures should be measured relative to GDP or total trade to make cross-country

comparison reasonable. In the G10 MEVA, the ‘external wealth’ factor is excluded as it is

not properly identified in both an economic and statistical sense simultaneously.

US: net foreign asset vs IIP Eurozone: net foreign asset vs IIP

Source: LMF(2006), Macrobond Financial, DBM Source: LMF(2006), Macrobond Financial, DBM

The data series are graphed below. Note that all series are log-transformed and relatively

defined, i.e. for EUR/USD we consider the euro area versus the US series. Exchange rates

are monthly averages of daily closing prices and converted from nominal to real using

CPI deflators from national sources; PPI, export and import deflators are OECD data.

EUR/USD: real exchange rate vs

relative IIP



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US vs. EU: Balassa Samuelson effect

(CPI-PPI) US vs. EU: terms of trade


Sweden vs. EU: Balassa Samuelson

effect (CPI-PPI) Sweden vs. EU: terms of trade


Norway vs EU: Balassa Samuelson

effect (CPI-PPI) Norway vs EU: terms of trade



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Swiss vs EU: Balassa Samuelson

effect (CPI-PPI) Swiss vs EU: terms of trade


UK vs US: Balassa Samuelson effect

(CPI-PPI) UK vs US: terms of trade


Japan vs US: Balassa Samuelson

effect (CPI-PPI) Japan vs US: terms of trade



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Australia vs US: Balassa Samuelson

effect (CPI-PPI) Australia vs US: terms of trade


New Zealand vs US: Balassa

Samuelson effect (CPI-PPI) New Zealand vs US: terms of trade


Canada vs US: Balassa Samuelson

effect (CPI-PPI) Canada vs US: terms of trade



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Disclosures This research report has been prepared by Danske Bank Markets, a division of Danske Bank A/S (‘Danske

Bank’). The author of the research report is Christin Tuxen, Senior Analyst.

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FX Edge

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