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www.icbcstandard.com White Paper Frontier FX Portfolio and Return Characteristics July 2018

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Page 1: Frontier FX - Industrial and Commercial Bank of China · ICBC Standard Bank | Frontier FX: Portfolio and Return Characteristics 4 1. Executive Summary From a historical perspective,

www.icbcstandard.com

White Paper

Frontier FX Portfolio and Return Characteristics

July 2018

Page 2: Frontier FX - Industrial and Commercial Bank of China · ICBC Standard Bank | Frontier FX: Portfolio and Return Characteristics 4 1. Executive Summary From a historical perspective,

ICBC Standard Bank | Frontier FX: Portfolio and Return Characteristics

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Gary Licht

[email protected] +44 203 145 6704 www.icbcstandard.com

This is a marketing communication which has been prepared by a trader, sales person or analyst of ICBC Standard Bank Plc or its affiliates and is provided for informational purposes only. It is not an offer or solicitation to buy or sell any security, commodity or other financial instrument, or to participate in any particular trading strategy. ICBC Standard Bank or its affiliates may from time to time have long or short positions in financial instruments referred to in this material. The material does not constitute, nor should it be regarded as, investment research.

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Contents

1. Executive Summary 4 2. Introduction 5

Chapter Layout 5 3. A False Taxonomy 6

3.1 Back to EM Basics 6 3.2 EMF as a Single Asset Class 6

4. Sustainable Alpha: More Alpha, More of the Time 7 4.1 Risk Equalised (CRE) Portfolios 7 4.2 Comparison of Available Alpha 8 4.3 Sources of Sustainable Alpha 8

5. Frontier Returns: High Carry and Low Beta 10 5.1 Carry matters more for Frontier 10 5.2 Low Beta 11 5.3 Classical Distribution Measures 11

6. Frontier Portfolios: Low Number Diversification 13 6.1 From Samples to Portfolios 14 6.2 Low Number Diversification 15

7. Other Topics 16 7.1 Illiquidity 16 7.2 Blended Portfolios 16

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1. Executive Summary

From a historical perspective, there is little to separate the current state of many Frontier economies and markets from those that defined EM in its early days, and before it lost many of its valuable attributes in the process of deeper integration into global markets. It has always been something of a surprise to us just how many professional investors fail to take a nuanced approach to understanding Frontier markets.

Frontier FX offers a unique and compelling proposition: high carry, low median volatility and extremely low correlations allow one to construct concentrated, high Alpha portfolios without sacrificing the benefits of diversification.

Frontier has a strong track record in generating available Alpha that is both far greater and more sustainable than traditional EM. This is, perhaps, not surprising given the small and correlated universe of traditional EM. For example, Frontier’s median own-sample cross-correlation over 2005-20171 was a negligible 5% compared to 45% for EM. On a risk equalised basis, Frontier’s annual available Alpha of 18.0% was three times that of EM with a minimum equal to that of EM’s average of 6.1%.

Frontier not only has higher absolute yield than EM but, more importantly, has higher yield relative to FX movement (Beta). Annual median carry to FX movement was 1.29 for Frontier, 0.56 for EM and a meagre 0.21 for DM. Counterintuitively, the primary driver of this ratio was differences in FX movement and not yield; Frontier’s annual median FX movement of 3.9% was almost half that of EM’s 6.9% and DM’s 7.2%. More formally, Frontier’s median Beta over this period measured 18% versus EM’s 98%.

While devaluation risk is a concern for individual Frontier currencies, Frontier portfolios are surprisingly resilient. For the same sample size of 22, Frontier’s maximum monthly loss moved from -52.0% as a sample to -5.7% as a portfolio while EM’s equivalent move was from -17.4% to -7.8%. Moreover, the Frontier portfolio’s 99th percentile monthly loss was -3.4% versus -6.5% for EM. In fact, the Frontier portfolio downside even outperforms EM at N=10.

However, it is not just the limits of diversification that are higher in Frontier but convergence is faster and material diversification benefits are achievable at extremely low numbers. While EM approached its terminal volatility of 6.5% after 10 currencies, Frontier achieved the same volatility after 3 currencies and, at 2.6%, reaches a terminal volatility less than half that of EM. This same pattern was evident for tail risk as measured by rolling 10 day and 60 day maximum drawdowns.

While Frontier FX is less liquid than EM, fixating on its illiquidity in the context of short-term maturities is somewhat misguided. It is difficult to find any other “illiquid asset class” that can be constructed to return full principal on a monthly or quarterly basis. Moreover, just like these other asset classes, the Alpha generating benefits of low correlation, low average volatility and premia inducing market inefficiencies are inseparable from its relative illiquidity.

1 Period 2005-2017 applies for all analysis referenced in the Executive Summary

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2. Introduction

The primary purpose of this paper is to delve deep into the data in order to explore the portfolio and return characteristics of Frontier FX.

Chapter Layout

The paper is structured across the following themes

3. A False Taxonomy: EM and Frontier as a Single Asset Class

4. Sustainable Alpha: More Alpha, More of the Time

5. Frontier Returns: High Carry and Low Beta

6. Frontier Portfolios: Low Number Diversification

7. Other Topics: Illiquidity and Blended Portfolios

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3. A False Taxonomy

This section sets the background for treating Emerging Markets and Frontier as equals within a single asset class (EMF)

3.1 Back to EM Basics

A central tenet of this paper is that the distinction between EM and Frontier as separate asset classes is false. While there may be practical reasons for treating them separately, these constraints are largely artificial and come at a cost. There is little to separate the current state of many Frontier economies and markets from those that defined EM in its early days, and before it lost many of its valuable attributes in the process of deeper integration into global markets. In this view, EM and Frontier sit on a spectrum of a single asset class that, for convenience, we will refer to as EMF.

3.2 EMF as a Single Asset Class

The definition of EM has evolved over the years and there is little consensus as to its exact meaning. Part of the difficulty is that the concept has evolved from a purely economic classification based on wealth levels to one that has also come to encompass the characteristics and behaviours of financial markets. Market liquidity, openness, sophistication and volatility are now the metrics with which financial practitioners are inclined to classify EM. The vulnerability of EM markets to political volatility and interference amid weak institutional checks and balances is another concept that is never far from discussion. The political and market definitions explain why countries as diverse as wealthy Saudi Arabia and open and liquid as Mexico both find themselves on the EM list. In fact, jut to re-enforce the fuzziness, many classify Saudi Arabia as Frontier.

The key point is that from the asset price behaviour viewpoint, all these EM and Frontier markets share common features and common risks with similar methods deployed to evaluate and manage these risks. In the same way that equity and debt each have their own set of broad risks and investor toolboxes, so is the case for EMF. It is in this sense that it is only natural to consider EM and Frontier as a single asset class.

Figure 1: EMF Universe

Core Frontier (40) Angola Ghana Nigeria UAE Argentina Jamaica Oman Uganda Armenia Kazakhstan Pakistan Ukraine Azerbaijan Kenya Paraguay Uruguay Bahrain Kuwait Qatar Vietnam Bangladesh Malawi Rwanda WAEMU Belarus Mauritius Saudi Zambia CEMAC Moldova Serbia Dom Rep Mongolia Sri Lanka Egypt Morocco Tanzania Georgia Mozambique Tunisia

EM (22) Brazil Mexico Chile Malaysia China Peru Colombia Philippines Czech Rep Poland Hungary Romania Indonesia Russia Israel Thailand India Turkey Iceland Taiwan S Korea South Africa

Source: ICBC Standard

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4. Sustainable Alpha: More Alpha, More of the Time

The available Alpha in Frontier or EMF portfolios is both far greater and more sustainable than that of portfolios that are limited to the small and correlated universe of traditional EM.

4.1 Risk Equalised (CRE) Portfolios

Of all the ratios used to measure portfolio performance, the most well-known and important is the Sharpe ratio.

The rationale of the Sharpe ratio, and its related mean optimisation framework, is to find the best return per unit risk and then use leverage to achieve the absolute risk level in line with one’s chosen risk appetite. Practitioners typically implement this through the process of volatility targeting. However, one drawback of the Sharpe ratio is that many strategies with very low absolute returns and even lower volatility perform incredibly well on paper but are vulnerable in practice. Such low returns leave little room for unforeseen friction costs and there may be practical limitations to the amount of leverage that can be deployed in reality. Resultantly, it is sensible to be aware that volatility targeting portfolios will have a capped maximum leverage multiple that depends on the underlying asset mix.

For comparative purposes, it is useful to set the volatility target equal to that of the benchmark. In this way, the new, leveraged portfolio can now be compared to the benchmark on a risk / volatility equalised basis. In order to avoid unrealistic portfolio leverage, we also set a cap to the leverage multiple. In this case the capped risk equalised (CRE) portfolio will have volatility less than or equal to the benchmark. For the remainder of the paper, we set the cap to 3 and denote this by C3RE.

4.2 Comparison of Available Alpha

One of the basic tenets of David Swenson’s Yale Investment Model is that manager selection only matters in samples of wide enough dispersion. If the difference between the best and worst performing managers is greater than the cost of due diligence then there is no point in focusing resources to choose between the two. In his context, this means allocating resources to choosing between alternative fund managers (wide dispersion in performance) instead of those in public markets (low dispersion in performance).

Drawing inspiration from this, we now evaluate the available C3RE Alpha in EM and Frontier. The idea is that funds selling Alpha exposure to investors would be wise to concentrate their efforts in categories that offer the highest available Alpha, under the proviso that such differences are material.

We do this by considering an evenly weighted portfolio of the annual top quartile – by returns – for each category over the period 2005-2017. The benchmark portfolio is defined as the evenly weighted portfolio of the 22 currencies in our EM universe. The difference is material with Frontier offering three times the available Alpha than that for EM.

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Not only is Frontier Alpha larger than EM but it is also more sustainable. Frontier’s annual Alphas never fall below EM’s average of 6.1%. In contrast, EM’s Alpha falls below this value in four out of thirteen years and has the dubious distinction of recording a negative Alpha of -0.3% in one of these instances.

Frontier’s superior performance is far from superficial. On an unleveraged basis, Frontier’s absolute returns2 are similar to that of the top quartile EM portfolios and do so at far higher Sharpe ratios and with significantly lower drawdowns. On the C3RE (leveraged) basis, Frontier’s total return is twice that of EM.

4.3 Sources of Sustainable Alpha

While empirical evidence of higher historical Alphas and superior Sharpe ratios is comforting, it would be even more convincing to identify reasons to expect this to persist into the future. After all, investment history is replete with examples of high Alpha strategies that have experienced long periods of time without significant opportunities. We investigate this across two dimensions: (i) structural features that suggest Alpha persistence over time and (ii) statistical features that increase the likelihood of such opportunities at any given time.

2 (Total) Returns throughout the paper are calculated as FX movement + Carry. Resultantly volatility excludes the contribution of interest rate movement. This is a reasonable assumption for short dated maturities. All calculations include estimated taxes and friction costs.

Figure 4: Top Quartile Return Statistics (unleveraged / annual average 2005-2017)

EM Frontier BM EM221

Number 6 15 22 Return 13.96% 11.84% 6.80% Volatility 6.93% 2.72% 5.94% Max 60d DD2 5.63% 1.94% 7.82% Sharpe3 1.58 4.07 0.95 Alpha 6.21% 9.93% 0.00% CRE Return 10.51% 21.37% 6.80% CRE Alpha 6.10% 18.00% 0.00%

Source: ICBC Standard, Bloomberg, EIU. 1Benchmark is an evenly weighted portfolio of our defined liquid EM universe consisting of 22 currencies 2It should be noted that the max DD for each category is 8.95% for EM, 7.77% for EMF, 4.87% for Frontier and 20.1% for BM EM22 3Sharpe ratio is calculated as excess returns over FX volatility

Figure 2: Available C3RE Alpha1

(annual average 2005 - 2017)

Figure 3: Available C3RE Alpha

(annual 2005-2017)

Source: ICBC Standard, Bloomberg, EIU. 1Includes estimated taxes and friction costs (average Frontier friction costs = 2%)

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

2005 2010 2015

Average EM (6.1%) Frontier

6.1%

EM Frontier

18.0%

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Closed economies, isolated markets and behavioural biases are all structural drivers of Alpha in Frontier markets. Closed economies indicate high barriers to entry and generally lower vulnerability to global economic trends. The resilience of Frontier GDP growth since the global financial crisis is a good example of this. Regulations, illiquidity and political interference are all examples of persistent sources of market isolation and inefficiencies. Finally, behavioural biases are deeply embedded in these markets with inconsistent policy making and reliably defensive market psychology from foreigners and locals alike. Foreigners frequently suffer from reputational and other non-economic sources of risk aversion while locals lack the luxury of diversification. It is difficult to see these issues materially evaporate from all of Frontier’s 40+ countries over the next couple of decades.

Large sample size and low within-sample cross-correlation are primary drivers of statistical features that ensure a high rate of frequent Alpha opportunities. While large sample size does this in a relatively straight forward way, low correlation is more subtle. A large and highly correlated sample will generate a high number of simultaneous Alpha opportunities, but may still leave long periods of drought. Low correlation ensures that such Alpha opportunities are more evenly spread out over time.

In addition to its superior structural drivers of Alpha, Frontier also displays significant statistical advantages over that of traditional EM. While EM is capped at the presently liquid 22 currencies, Frontier consists of a 40+ universe. Moreover, cross-correlation within the EM sample is far greater than that of Frontier. Even at the 75th percentile, Frontier own-sample cross-correlation is 13% versus 52% for EM. Staggeringly, Frontier’s 95th percentile own-sample cross-correlation is only 33%.

Aside from its relevance in the current context, this extremely low own-sample cross-correlation is the primary driver of the enormous diversification benefits of Frontier portfolios. We will consider this in later sections that address how the statistics of Frontier currency samples change when incorporated into a portfolio. In particular, we see how both average volatility and downside tail risk reach impressively low levels.

Figure 5: Own-Sample Cross-Correlation Percentiles ( monthly 2005-2017)

EM (22) Frontier (40+)

0% -4% -26% 25% 37% 0% 50% 45% 5% 75% 52% 13% 95% 62% 33% 100% 87% 99%

Source: ICBC Standard, Bloomberg, EIU

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5. Frontier Returns: High Carry and Low Beta

The pre-diversification characteristics of Frontier returns are dominated by the powerful combination of high carry and low median FX movement. On the negative side, devaluation risk is strongly evident in the downside tail.

5.1 Carry matters more for Frontier

Carry is the primary form of remuneration in Frontier markets. On an annual and median basis, carry’s contribution to total return (TR+)3 is 56% for Frontier, 36% for EM and a mere 17% for DM. As one might expect, the share of TR increases with yield, which itself is a function of illiquidity.

A more intuitive ratio to convey the relative importance of yield versus FX in Frontier markets is Carry versus the magnitude of FX movements (ΔFX+). On an annual and median basis, carry is 1.29x that of ΔFX+ for Frontier, 0.56x for EM and 0.21x for DM. Not surprisingly, this ratio decreases with the time period. However, even at one month, the Frontier terminal value remains high at 0.72.

Carry matters more for Frontier not only because the absolute yields are higher but, more importantly, because yields relative to FX movements are even higher. Keep in mind this is true even before looking at diversification benefits.

3 Contribution to Total Return uses TR+= | | | |=| | ΔFX as the denominator

Figure 6: Carry / (Total Return)+

(annual median 2005 - 2017)

Figure 7: 1Yield

(annual median 2005-2017)

Source: ICBC Standard, Bloomberg, EIU. 1Includes estimated taxes and friction costs (average Frontier friction costs = 2%)

56%

36%

17%

0%

10%

20%

30%

40%

50%

60%

Frontier EM DM

4.6%

3.7%

1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

Frontier EM DM

Figure 8: Carry / (FX Movement)+ (median 2005-2017)

Source: ICBC Standard, Bloomberg, EIU

129%

56%

21%

97%

33%10%

72%

20%6%

0%25%50%75%

100%125%150%

Frontier EM DM

Annual

Quarterly

Monthly

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5.2 Low Beta

An interesting corollary from the above analysis is that the primary driver of the differences in the Carry to ΔFX+ ratio seen across market categories is not the yield differential but rather the differences in ΔFX+. The annual median for Frontier is 3.9% compared to 6.9% for EM and 7.2% for DM. This difference is even more pronounced with shorter time periods. The monthly median is 0.5% for Frontier, 1.55% for EM and 1.84% for DM.

When we look a little more formally at the data, the low Beta of Frontier FX is borne out. Without getting too technical, we know that Beta depends on both the correlation between the asset and the benchmark as well as the ratio of the asset standard deviation to that of the benchmark4. Frontier low Beta is supported by both terms: Frontier volatility is typically lower than EM volatility and correlation to the benchmark is also low.

5.3 Classical Distribution Measures

While the median is a useful descriptor for the average representative of the sample, performance is usually determined at the margin. This section examines the details of the full return distribution with particular attention paid to the tails.

We start by considering the classical distribution measures. In the context of the low magnitude of Frontier FX movements discussed in the previous section, it is not surprising that Frontier’s standard deviation is lower than that of EM and DM. At a glance, it is also not surprising that Frontier’s skew is horribly negative and its Kurtosis is many orders of magnitude higher than that of the other samples.

4 , where denotes correlation, standard deviation, i asseti and bm benchmark

Figure 9: FX Movement Magnitude (median 2005-2017)

Source: ICBC Standard, Bloomberg

3.9%

1.2% 0.51%

6.9%

2.8%

1.6%

7.2%

3.4%

1.8%

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%

Annual Quarterly Monthly

Frontier

EM

DM

Figure 10: Benchmark Correlation and Beta (monthly returns 2005-2017)

Correlation Beta Frontier EM Frontier EM 0% -1% 31% -1% 9% 25% 11% 62% 4% 66% 50% 16% 69% 18% 98% 75% 37% 75% 45% 125% 95% 74% 79% 95% 151% 100% 75% 82% 122% 154%

Source: ICBC Standard, Bloomberg, EIU. EM22 is used as the benchmark.

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While Skew and Kurtosis provide information about the relative size of the tails for each distribution, they have limited information for comparing tails between distributions. In order to do so, we need to know how the tails compare with regard to actual returns.

A simple starting point is to see how the percentiles of the total return distributions “line-up”. The first observation is that the difference in returns across percentile pillars is relatively modest between the 1st and 99th percentile with an extreme difference evident in the 1% tails on either side. This reflects the large devaluation (and far less common revaluation) risk evident across the managed currencies in Frontier. It is this 1% tail risk that is responsible for explaining the negative skew and extremely high kurtosis. Frontier downside returns are remarkably well behaved, actually performing better than EM up until the 99th percentile and even beating DM until the 95th percentile.

Devaluation risk of Frontier currencies is a primary concern for investors and, as the next section addresses, the low correlations of Frontier means diversification is a surprisingly effective tool in ameliorating these concerns. While not considered in scope for the current paper, it is also worth noting that large devaluations of managed currencies seldom occur without economic or political warning.

Figure 12: Return Percentiles (monthly 2005-2017)

Frontier EM DM

0% -52.0% -17.4% -14.7% 1% -8.5% -10.0% -8.0% 5% -3.5% -5.1% -5.1%

25% -0.3% -1.2% -1.7 50% 0.2% 0.4% 0.2% 75% 0.9% 1.9% 2.0% 95% 3.3% 5.3% 5.0% 99% 7.0% 8.3% 8.1%

100% 46.0% 17.7% 13.4%

Source: ICBC Standard, Bloomberg, EIU

Figure 11: Classical Distribution Metrics (monthly returns 2005-2017)

Frontier EM DM

Std Dev 2.9% 3.3% 3.1% Skew -3.3 -0.6 -0.04 Kurtosis 65.9 4.0 1.5

Source: ICBC Standard, Bloomberg, EIU

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6. Frontier Portfolios: Low Number Diversification

Frontier markets’ greater and faster limits of diversification achieve material benefits at extremely low numbers. This is evident both for average volatility and in taming the devaluation risk of individual Frontier currencies.

6.1 From Samples to Portfolios

The previous section studied the return profile of Frontier currencies as a sample; this section looks at the diversification benefits of Frontier by studying portfolios of Frontier currencies. We start by taking a look at how the basic statistics change when moving from the sample to an evenly weighted portfolio of the full sample.

The change in statistics is the largest for Frontier where all metrics have improved markedly. Interestingly, volatility is lower across all three categories but skew is actually worse for EM and DM while kurtosis is now higher for DM.

The transformation in the return percentiles is even more revealing. Focusing on the downside, the Frontier portfolio not only extends its over-performance to EM and DM up until the 95th percentile but is now also better through to the 100th percentile. In fact, its max loss is now almost half that of EM and DM. The other side of this is that Frontier significantly underperforms on the upside.

An interesting feature of this is that, far from common perception, the negative skew and high kurtosis of Frontier is primarily driven by its limited upside and not its downside tail risk.5

5 It should be noted that this observation does not invalidate the central thesis of Frontier’s higher excess returns. As section 4 demonstrates, significant upside and diversification benefits can both be maintained by the skilled selection of a concentrated portfolio amongst Frontier’s 40+ currencies

Figure 13: Classical Distribution Metrics (monthly returns 2005-2017)

Frontier EM DM Sample Portfolio Sample Portfolio Sample Portfolio Std Dev 2.9% 0.9% 3.3% 2.2% 3.1% 1.8% Skew -3.3 -1.2 -0.6 -0.7 -0.04 -0.5 Kurtosis 65.9 2.8 4.07 1.6 1.5 2.0

Source: ICBC Standard, Bloomberg, EIU

Figure 14: Return Percentiles: Full Samples (monthly 2005-2017)

Frontier EM DM Sample Portfolio Sample Portfolio Sample Portfolio 0% -52.0% -4.0% -17.4% -7.8% -14.7% -7.5% 1% -8.5% -2.5% -10.0% -6.5% -8.0% -4.9% 5% -3.5% -1.7% -5.1% -3.3% -5.1% -2.5% 25% -0.3% -0.3% -1.2% -0.7% -1.7% -0.8% 50% 0.2% 0.3% 0.4% 0.5% 0.2% 0.1% 75% 0.9% 0.7% 1.9% 1.7% 2.0% 1.0% 95% 3.3% 1.4% 5.3% 3.8% 5.0% 3.4% 99% 7.0% 1.8% 8.3% 4.9% 8.1% 4.2% 100% 46.0% 2.1% 17.7% 5.9% 13.4% 4.5%

Source: ICBC Standard, Bloomberg, EIU

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6.2 Low Number Diversification

The Frontier universe of 40 is significantly larger than that of EM at 22 and a fair question to ask is how they compare at equal numbers and across numbers. This is particularly relevant given the large devaluation risk of individual Frontier currencies. We know that the limits of diversification eventually peter out for some number N and it is, perhaps, not surprising that the Frontier 22 displays very similar benefits of diversification to the portfolio consisting of the full sample. However, it might surprise some that even with N=10, Frontier’s downside still performs better than that of the full EM universe.

It is not just the limits of diversification that are higher in Frontier but the convergence to this limit is also faster6. With regard to volatility, EM approaches its terminal value of 6.5% after 10 currencies while Frontier reaches this same volatility after 3 currencies. At 10 currencies Frontier records a volatility of 3.5%. Moreover, its terminal volatility of 2.6% is less than half that of EM.

6 Analysis for the remainder of the section looks at daily (not monthly) FX (not total) returns over 2005-2017. The former allows us to better capture the maximum drawdowns on a more accurate daily basis and the latter also provides for a more conservative calculation of drawdown without materially affecting volatility results.

Figure 15: Return Percentiles ( monthly 2005-2017)

1Frontier 10 1Frontier 22 EM (22) Sample Portfolio Sample Portfolio Sample Portfolio 0% -52.0% -6.8% -52.0% -5.7% -17.4% -7.8% 1% -8.5% -3.9% -8.2% -3.4% -10.0% -6.5% 5% -3.5% -1.9% -3.4% -1.8% -5.1% -3.3% 25% -0.3% -0.3% -0.3% -0.3% -1.2% -0.7% 50% 0.2% 0.3% 0.2% 0.3% 0.4% 0.5% 75% 1.0% 0.8% 1.0% 0.7% 1.9% 1.7% 95% 3.4% 1.7% 3.3% 1.6% 5.3% 3.8% 99% 6.9% 2.7% 6.9% 2.1% 8.3% 4.9% 100% 46.0% 4.50% 46.0% 2.9% 17.7% 5.9%

Source: ICBC Standard, Bloomberg, EIU.1Average of historical simulations of randomly selected portfolios recalculated every 6 months

Figure 16: Standard Deviation vs Portfolio Number N (FX daily 2005-2017)

Source: ICBC Standard, Bloomberg. Average of historical simulations of randomly selected portfolios recalculated every 6 months

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Frontier EM

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This same pattern is evident for tail risk as measured by 10 day and 60 day maximum drawdowns. Frontier achieves EM’s terminal value after only a handful of currencies while Frontier’s 10 day and 60 day terminal value is roughly half that of EM.

While low number diversification was already implicit when comparing available Alpha in Section 4, the current analysis makes it explicit: Frontier FX allows for the construction of high Alpha, concentrated portfolios without sacrificing the benefits of diversification.

Figure 17 : Maximum FX Drawdowns vs Portfolio Number N (FX daily 2005-2017)

Source: ICBC Standard, Bloomberg. Average of historical simulations of randomly selected portfolios recalculated every 6 months

0.0%

2.5%

5.0%

7.5%

10.0%

12.5%

15.0%

17.5%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

10 Day

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

60 Day

Frontier EM

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7. Other Topics

Liquidity is never far from the minds of investors exploring Frontier FX and it is important to contextualise this risk appropriately and consider its benefits as well as its disadvantages. The role of Frontier FX in blended portfolios is another subject worthy of further thought.

7.1 Illiquidity

While it is certainly wise to be cognisant of Frontier FX’s relative illiquidity, it is somewhat misguided to fixate on it in the context of short term maturities. It is difficult to think of another “illiquid asset class” that can be constructed to return full principal on a monthly or quarterly basis.

The underlying difficulty with assessing the liquidity of Frontier FX is deciding on its comparison group. On the one hand, treasury bills and forwards are clearly financial assets but on the other the ecosystem of participants and inefficiencies of Frontier FX markets provide it with many real asset return characteristics. This hybrid nature is an awkward fit in an investment world so partitioned by mandates: EM FX investors are intimidated by its relative illiquidity and Alternative / Private Market investors are daunted by its macroeconomic sensitivity.

After spending most of the paper exploring the benefits of Frontier FX over EM FX, it is worth looking at how it compares to other illiquid assets. While low correlation, low average volatility and premia related to market efficiencies are shared drivers of Alpha, high carry and short term maturities are unique to Frontier FX. Private Equity and Venture Capital (PEVC) offer no income and are usually tied up for a minimum of 5 years. Unlike carry, which is always positive and collected in cash along the way, PEVC returns are binary at a single date many years into the future. Real Estate and Infrastructure offer income but holding periods are normally many years with any secondary market activity hampered by months of due diligence. Moreover, any income is either low in comparison to capital price movements or is in jurisdictions or industries of similar high risk to Frontier. This is not to say that these other illiquid investments are not well considered and justified but rather that illiquidity is accepted as inseparable from its many appealing attributes- it is singularly for Frontier FX that, despite its demonstrably superior liquidity profile, this tends to be forgotten.

7.2 Blended Portfolios

The analysis in this paper suggests an interesting role for Frontier FX within portfolios (i) blended between local and hard currency debt and (ii) blended between Frontier and EM FX. With regard to the latter, it is only natural to consider the full and expanded EMF universe and construct portfolios that exploit the discovered portfolio benefits of high carry, low beta and diversification that are possible in large and uncorrelated samples. Moreover, the addition of EM also allows for greater flexibility in managing liquidity and leverage.

While not addressed explicitly in this paper, it would not be surprising to see Frontier FX materially enhance the already popular diversification strategies of blended local and hard currency EM debt.

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