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Currency investment insights Alternative risk premia benchmarks July 6 2020 Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Currency investment insights
Alternative risk premia benchmarks
For decades, the currency markets have been the first port of call for discretionary Kartik Ghia, PhD investors looking to express macroeconomic views. In more recent years, this has +1 212 617 5649 been accompanied by a plethora of quantitative investment strategies aimed at [email protected] providing access to alternative risk premia factors in the currency markets. Despite increasingly widespread use within systematic investment portfolios, the lack of Michael K. Donat, CFA established benchmarks even for the most popular investment styles—carry, value +1 212 617 5509 and trend—has made performance comparisons difficult. This has often led to a wide [email protected] range of conclusions about long run performance. Zarvan Khambatta, CFA, CAIA In this publication, we establish tradable benchmarks for three alternative factors— +1 212 617 5418 carry, value, and trend. We highlight the properties of the individual styles, the [email protected] implications for portfolio construction and propose rules-based, transparent implementations. The resulting benchmarks can either be used to replicate factor returns in the currency markets or as a tool to measure the performance of existing portfolios. The intention is to provide a common frame of reference around which asset owners and managers can base performance expectations. Our discussion includes: Examining the performance characteristics of the carry trade Investigating the value factor and propose a transparent, rules-based strategy using a measure of purchasing power parity (PPP) Assessing the role of signals and portfolio construction in developing a transparent trend following benchmark Performance attribution and case studies for three investment styles Proposing a multi-factor currency benchmark using a risk-based approach
Figure 1: Diversified profiles: carry, value and trend
Source: Bloomberg
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Introduction Systematic investment strategies have long been a staple of currency investors. Viewed as a relatively inexpensive way to express macroeconomic views, the most popular styles are carry, value, and trend following. Each provides exposure to an identifiable risk factor in exchange for a long-run positive expected return. Differences in the drivers of returns translate to distinct returns profiles—which can potentially be combined to develop robust return-seeking portfolios. Alternative beta strategies have four important elements: (1) universe selection and the choice of instrument, (2) signal and ranking methodology, (3) constituent weighting and (4) rebalancing frequency. As we see in later sections, currency strategies have one additional feature—exposure to the US dollar. Despite the existence of multiple parameters that promote a plethora of implementations, the relative similarity in signals across each implementation within a style, permits the identification of broad benchmarks for carry, value, and trend following in currencies. There is a significant amount of academic and practitioner literature discussing these styles—both the drivers of returns and the various considerations surrounding implementation. Our prior publications on the subject are listed in the bibliography. In later sections, we simply refer to these findings where appropriate. In the initial sections, we describe an example of a transparent, robust implementation for each investment style. The implementations discussed are intended to be accessible to a wide audience. To promote the flexible use of the benchmarks the construction process is modular and facilitates customization to specific requirements. The four main considerations are: 1. Transparency of methodology and data 2. Understanding whether a common consensus exists (using published literature and invested funds) 3. Implementation feasibility 4. Ability to isolate exposure to the style (‘purity’) After the individual index style construction and design has been laid out, the latter sections of this publication will discuss a multi-style benchmark. There are two main use cases for these style benchmarks; the first in a primary role as a return generator and the second in an ancillary capacity as a return enhancer/risk reducer. These can be summarized as (1) a source of investment return which is typically part of a macro portfolio and (2) an excess return overlay in conjunction with an international asset portfolio/cash flows. This is typically used by asset managers and/or the treasury department of a corporation to enhance risk-adjusted returns by systematically managing underlying currency exposures. The style benchmarks discussed here are intended to highlight longer term performance characteristics and illustrates portfolio use-cases. The aim is to construct an investible factor-based framework to provide investors with the ability to customize these benchmarks and address individual requirements. Data The currency universe for the style benchmarks is comprised of 24 indices within the Bloomberg FX Forward Index Family. The indices span developed and emerging markets Bloomberg Systematic Strategies A Bloomberg Professional Service offering
and represent the excess return of holding and rolling a 1-month currency forward contract against the U.S. dollar. The pricing source for the spot and forward rates used to construct these indices is Bloomberg FX Fixing (BFIX). For liquidity purposes, trades are via the US dollar pairs—even for (non-US dollar) crosses. By region, the currency indices cover: Asia Pacific: AUD*, IDR, INR, JPY*, KRW, NZD*, PHP, SGD, and TWD EMEA: CHF*, CZK, EUR*, GBP*, HUF, ILS, NOK*, PLN, RUB, SEK*, TRY, and ZAR Americas: BRL, CAD*, and MXN The asterisk (*) denotes a currency classified as a developed market (or G10) currency. For more information about this family of indices please refer to the Bloomberg FX Forward Index Family, July 2019. The inception date is January 1999 with the exception of selected emerging market (EM) currencies which start at later dates depending on data availability. For the carry strategy, we need a measure for the US funding rate. In-line with general industry practice, we use the US 1-month LIBOR. OIS rates can be used instead of LIBOR with minimal change in stated results. Given the transparency in methodology and public availability, we use PPP data from the Organization for Economic Cooperation and Development (OECD) to calculate the signal for the value strategy. The data is published with periodic revisions. Due to circumstances, our data collected prior to 2018 includes revisions. The data following this date are point-in-time. We address the impact of this in the value investing section. The carry trade The currency carry trade is defined as investing in a high yielding currency and funding in a (relatively) lower yielding currency. The profile of the risk premium is steady, incremental gains during periods of low-to-medium market volatility interspersed with sudden, large drawdowns when investor sentiment turns bearish. The compensation for this ‘crash risk’—manifested by a strong negative skewness in returns—is a positive long run expected return. The returns profile can be compared to an insurance underwriter who earns a steady income through premium but must pay-out sporadically when specified events occur. For an extended discussion on the drivers of returns and the different variations in the strategy, refer to The G10 FX carry premium, November 2010, Bloomberg LP and Deconstructing currency carry, January 2019, Bloomberg LP. The G10-EM currency classification is both a manifestation of structural differences between economic regimes and an artifact of tradition. The broad economic classification is based on aspects such as whether the currency is actively managed, the existence of capital controls, the state of economic development, and the independence of fiscal and monetary institutions. (See The EM FX Carry Premium, September 2010, Bloomberg LP). At the same time, the steady institutional developments within emerging markets has muddied the traditional distinctions between the two groups—leaving some room for discretion. For the purposes of our carry benchmark, we maintain the distinction between the two groups since many institutions still segment portfolios by traditional definitions. The currency carry benchmark comprises of a weighted average of the G10 and EM carry benchmarks. The standalone G10 and EM-only carry portfolios are combined to create Bloomberg Systematic Strategies A Bloomberg Professional Service offering
the final (composite) portfolio; which contains an equal number of offsetting G10 and EM currencies in the funding and investment legs. Over extended periods, relative to this carry portfolio (which enforces separation between G10 and EM currencies), a portfolio using a combined universe will tend to overweight the number of G10 and EM currencies in the funding and investment legs respectively.1 In turn, this translates into higher sensitivity (for the combined universe portfolio) to global ‘risk-on/off’ sentiment. Signal Currency carry strategies have signals with varying degrees of complexity. These range from the magnitude of pairwise interest rate differentials to incorporating risk measures such as volatility to include a notion of quality. A more complex approach involves using portfolio characteristics such as pairwise correlations. We examined a few of these alternatives in prior research. A summary of our findings were as follows: Interest rate differentials are slow moving Incorporating risk measures to account for quality does little for long term Sharpe ratios or truncating drawdown/volatility Correlations between currencies in the funding/investment legs move towards 1 during crisis periods. Simple representations of covariance matrices do not enhance downside risk characteristics but significantly reduce the magnitude of long run returns A meaningful enhancement of risk adjusted returns and downside characteristics require more complex and potentially bespoke solutions. (For example, see improving the risk-return profile of the FX carry trade, January 2011.) Relevance to a wide audience requires an investible benchmark to be transparent, parameterize parsimoniously and to deliver positive long-term returns. With this in mind, we select currencies based on interest rate differentials. For the G10 portfolio, the US dollar (interest) rate is defined as the US 1-month LIBOR rate. For all other currencies, it is the implied 1-month rate inferred from the relevant spot and forward quotes for the particular currency versus the US dollar. To moderate signal noise, especially currencies for which NDFs2 are used (BRL, IDR, INR, KRW, PHP, and TWD), the daily-calculated interest rates are averaged over the past month. Portfolio considerations The currencies are ranked in descending order by their corresponding average interest rate. Long/short exposures are assigned based on the currencies displaying the highest/lowest interest rates. The EM portfolio is, by construction, US dollar neutral. The G10 portfolio may be long or short or neutral the US dollar depending on the rank ordering of the US interest rate. Currency pairs are allocated by pairing-off currencies at the extremes of the ranking and working towards the middle. Given each successive pair added to the portfolio has a monotonically decreasing (ex-ante) interest rate differential, the number of pairs within the carry portfolio is a trade-off between declining (ex-ante) portfolio carry and increasing diversification (of idiosyncratic risks). These portfolio tradeoffs were found in Deconstructing Currency Carry (January 2019) which studied a history spanning 2003-2019. A very brief summary of our findings on portfolio size and
1 Deconstructing Currency Carry, January 2019 2 Non deliverable forward Bloomberg Systematic Strategies A Bloomberg Professional Service offering
weights are given below: Portfolio size In the G10 portfolio, 2-5 pair portfolios display similar risk-adjusted returns with monotonically declining volatility. Downside risk (as measured by drawdown-to- volatility) is approximately equal for these portfolios. For the EM portfolio, 1-3 pair portfolios display similar risk adjusted returns with monotonically declining volatility. The one pair portfolio has significantly worse downside characteristics. A portfolio comprising of 4+ pairs provide greater diversification against idiosyncratic risk but trades-off risk adjusted returns over the full sample. Portfolio weights The use of risk-based weights instead of equal notional exposures does not improve risk adjusted returns. Constructing the benchmark Differences in currency start dates impacts the size of the investible universe at a given point-in-time; therefore specifying a set number of currency pairs in the portfolio can be problematic. Furthermore, we seek to harmonize methodologies across the G10 and EM portfolios. To address these points, we select currencies based on quantiles. Each period, the available currencies are ranked by interest rate, in descending order. Dividing the currencies into terciles, the top 1/3rd are selected as part of the investment leg while the bottom 1/3rd are part of the funding leg. Since interest rates are driven by economic cycles and monetary policy, the rankings between currencies tend to change only gradually. Taking into consideration the slow moving signal, we use a monthly rebalance frequency. G10 and EM benchmarks The available (ex-ante) carry varies considerably over time (Figure 2). It is clear that while the pattern of the carry available is very similar, the magnitude for the EM and G10 portfolios is very different. The EM portfolio delivers a persistently higher amount of carry than the G10 portfolio. A closer inspection reveals this difference is attributable to the fact interest rates associated with investment currencies in the EM benchmark are significantly higher (different economic regimes) than their counterparts in the G10 benchmark. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Figure 2: Mapping the available carry (1999 – 2020)
Source: Bloomberg
The result is two carry strategies displaying similar characteristics to that found in the general literature. While the returns display a fairly high degree of correlation (0.45), the EM carry index produces higher raw and risk adjusted returns over the full sample (Figure 9). This (risk-adjusted) outperformance can be isolated to the behavior during the Credit Crisis when the G10 strategy suffered a drawdown of approximately 35% while the EM strategy declined 17% (Figures 3 and 4). The Credit Crisis highlights two related aspects of carry strategy design—the impact of drawdowns on long term returns and the importance of reducing exposure to macroeconomic shocks. The asymmetric speed of drawdowns and return accruals in a carry strategy mean investors are especially incentivized to manage downside risk. Truncating drawdowns and hence preserving capital results in less time underwater and higher longer term returns due to the benefits of compounding.
Figure 3: Variation of G10 FX Carry with implied Figure 4: Similar pattern for EM FX Carry volatility
Source: Bloomberg Source: Bloomberg
Currency carry strategies tend to suffer losses during periods of elevated market uncertainty. High yielding currencies depreciate when investors unwind their carry Bloomberg Systematic Strategies A Bloomberg Professional Service offering
trades as part of the process of moving from a ‘yield-seeking’ mode to one where capital preservation is the aim. This ‘flight-to-quality’ tendency is amplified in the emerging markets as investors repatriate capital via the US dollar. Defining the EM currency carry portfolio to long/short positions based solely on EM currencies (i.e. no G10 currency exposure) is one way to manage this macroeconomic risk. As is typical of currency carry strategies, returns are strongly negatively correlated with change in implied volatility. Based on monthly returns, the G10 and EM correlations with their respective implied volatility indices are -0.6 and -0.5. Figure 5: Mapping correlations with the US dollar Figure 6: EM returns display greater variation
Source: Bloomberg Source: Bloomberg
By construction, exposure to the US dollar is limited to—at most—the funding or investment leg in a single pair. The full sample correlation reflects this; the correlation with changes in the US dollar index (DXY) is -0.3 and zero for the G10 and EM benchmarks respectively. However, as seen from Figure 5, a rolling correlation highlights just how time varying the relationship is. The EM benchmark returns tend to both exceed those of the G10 benchmark with the exception of the period around 2012 and display greater variability (Figure 6).
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Figure 7: Conditional returns: FX Carry benchmark Figure 8: Index performance: Carry benchmarks
Source: Bloomberg Source: Bloomberg
The composite carry benchmark is constructed by combining the G10 and EM sub- benchmarks based on inverse volatility. The portfolio is rebalanced on a quarterly frequency using a 60-month lookback window to calculate volatility. Risk-adjusted returns over the full sample are attractive (0.6) and the portfolio delivers an annualized return of 4.5% (Figure 9). Keeping with the traditional carry profile, the returns display a strong negative skew (-1) and subsample performance varies significantly. The time varying correlation between the US dollar and G10 and EM strategies (Figure 5) supports the lack of a consistent relationship between strategy returns and quintile-based returns in the US dollar, over the full sample (Figure 7). This is in sharp contrast to changes in the VIX—which is a proxy for investor sentiment.
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Figure 9: Performance statistics
Universe Performance G10 FX Carry EM FX Carry FX Carry Period: January 2000 - May 2020 Annualized return 3.1% 7.0% 4.5% Annualized volatility 8.4% 9.9% 7.4% Sharpe ratio 0.37 0.70 0.61 Max drawdown -33.9% -18.3% -25.3% Max drawdown/volatility -4.0 -1.8 -3.4 Skewness (monthly) -0.88 -0.12 -0.96
Sub period: January 2000 - December 2009 Annualized return 5.7% 13.6% 8.6% Annualized volatility 8.8% 11.6% 8.0% Sharpe ratio 0.65 1.17 1.07 Max drawdown -33.9% -17.8% -25.3%
Sub period: December 2009 - May 2020 Annualized return 0.6% 1.0% 0.7% Annualized volatility 7.9% 7.6% 6.6% Sharpe ratio 0.08 0.13 0.11 Max drawdown -16.6% -18.3% -15.1%
Source: Bloomberg
The value premium The ‘law of one price’ states that in the absence of transaction/trading costs, identical goods in different countries must have the same price. The value strategy in currency markets is based on a related idea—purchasing power parity (‘PPP’). The modern concept of PPP can be dated to the early 20th century and measures prices of a basket of goods in different countries. It can be used to infer the appropriate exchange rate such that purchasing power for the given basket is equalized. As defined by the OECD, “Purchasing power parities (PPPs) are the rates of currency conversion that try to equalize the purchasing power of different currencies, by eliminating the differences in price levels between countries.” The implicit assumption is that a rise (fall) in purchasing power in a country leads to a corresponding strengthening (weakening) in the associated currency. In its weak form, PPP is assumed to hold in the long run. In practice, non frictionless trading, tariffs and the Harrod-Balassa-Samuelson (HBS) effect—which shows the tendency of high income and productivity countries to have a persistently higher price level than lower productivity and income countries—raise the possibility that the realized exchange rate may not convergence to the estimated PPP value. Value investing in currencies has a long tradition with systematic investors given its underpinnings in economic theory. At the same time, the extended (and uncertain) time horizons over which exchange rates are expected to reach an equilibrium requires a high degree of conviction in the concept. There are three main versions of value-based models Bloomberg Systematic Strategies A Bloomberg Professional Service offering
and range from shorter to long run convergence bets and from simple to more complex: Version 1: Based on changes in inflation rates (measured as CPI) with forecast horizons typically ranging from monthly to quarterly Version 2: Uses a PPP measure and calculates the deviation from the nominal exchange rate. In the more recent literature, this model has been adapted to incorporate real exchange rates and account for structural factors such as the HBS effect (see Menkhoff and Sarno, 2016) Version 3: The behavioral equilibrium exchange rate (BEER) framework is based on an econometric model. It models the long run behavior of exchange rates based on macroeconomic variables such as interest rate differentials, inflation and terms of trade Value investors face exposure to three main risks: uncertainty regarding the time to convergence (especially with versions 2 and 3), estimation error, and the potential for PPP to be a ‘moving target’ (i.e. the target exchange rate is dynamic). In return for taking on these risks, they expect—in the long run—to receive a positive return. An investible value benchmark needs to incorporate the key concepts associated with an equilibrium/convergence based approach as discussed in the literature, with a high level of transparency. Accordingly, we base our benchmark on a form of PPP convergence (version 2). Currencies are classified as over/undervalued based on the deviation of the spot exchange rate from the PPP value. As is common with most rules-based currency value strategies, the investible universe is restricted to the G10 currencies. This is primarily due to the availability and quality of historical data and the impact of the HBS effect. Signal The PPP data is produced by the OECD. The release cycle is annual; with an initial PPP estimate per country/region followed by periodic, revisions during the course of the year. There is a lag between the release dates and the data collection period. We use the initial release value of the PPP estimate. Prior to 2018 the initial estimates were unavailable. For the purposes of the back test, we use the revised estimates prior to 2018. Over the full sample, we acknowledge this increases the annualized return of the historical backtest by 0.6-0.7%. PPP values are measured as the national currency per US dollar. PPP and spot rate data are available for the nine currencies versus the US dollar. The US dollar is assigned a value of 1 for each metric. Since we are making a cross sectional comparison of PPP values, we use percentage deviations to ensure scale invariance. The measure for currency i at time t is calculated as: 푡 푡 푖 퐹푋푖 − 푃푃푃푖 퐷푒푣푖푎푡푖표푛푖 = 푡 푃푃푃푖 This implies the deviation value for the US dollar is set to 0.
Constructing the benchmark Currencies are ranked in descending order by the deviation score. Currencies with high relative scores are undervalued while currencies with low relative scores are overvalued. Note that since this is a relative ranking, the absolute value of the signal is unimportant. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
The investment and funding legs of the strategy comprise of the relatively undervalued and overvalued currencies. The number of pairs selected is based on three aspects—diversification of returns, dilution of the signal, and consistency with the other strategy benchmarks. Given the uncertainty in the time to convergence, it appears prudent to invest in multiple pairs to mitigate the risk of large drawdowns. At the same time, each additional pair added to the portfolio—by construction—has a weaker signal (and by implication dilutes the strategy). With regard to consistency, we note the carry benchmark is constructed by taking long/short positions based on terciles. An examination of correlations and conditional returns provides additional support in deciding on portfolio composition.
Figure 10: Constituent correlations (2000 –May 2020) Figure 11: Conditional returns: Performance during the 10th decile of monthly returns
Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 1 100% Pair 2 13% 100% Pair 3 19% 1% 100% Pair 4 -4% 0% -13% 100% Pair 5 8% -10% -5% -20% 100%
Source: Bloomberg Source: Bloomberg
Low correlations over the full sample indicate the potential diversification benefits of multiple pairs (Figure 10). As important is the relationship between the pairs during periods of stress. A good way to illustrate this is to examine the median monthly return for each ranked pair during the 10th decile of returns for a given pair. In Figure 11, each row contains the median value for the 10th decile of the specified pair. The columns contain the corresponding performance for all pairs. The difference in returns between the diagonal and off-diagonals provides additional support for investing in multiple pairs. Putting together the results from the pairwise relationships and the parameters of the carry portfolio, we use three currency pairs to represent the value portfolio. Since the signals are relatively slow moving and the strategy premised on medium term convergence, we rebalance the portfolio on a monthly frequency. Each month, signals and rankings are computed following which the portfolio is rebalanced to equal notional weights. Results For comparison purposes, we show results for portfolios comprising 3-5 pairs. The risk adjusted returns for 3-4 pairs are attractive (0.6) and approximately the same as the carry benchmark. Expanding the number of pairs lowers both the portfolio volatility (as per the correlation analysis above) and return. Adding the 5th pair appears to impair performance—primarily through a significant reduction in return. This can perhaps be attributed to signal degradation since the full universe is being selected. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Figure 12: Performance statistics
FX Value Pairs Performance 3 Pairs 4 Pairs 5 Pairs Period: January 2000 - May 2020 Annualized return 3.8% 3.2% 1.5% Annualized volatility 6.9% 5.6% 4.7% Sharpe ratio 0.55 0.57 0.32 Max drawdown -16.1% -14.0% -13.6% Max drawdown/volatility -2.3 -2.5 -2.9 Skewness (monthly) -0.01 0.03 0.04
Sub period: January 2000 - December 2009 Annualized return 5.8% 4.3% 2.2% Annualized volatility 8.0% 6.7% 5.4% Sharpe ratio 0.73 0.64 0.40 Max drawdown -11.9% -9.3% -9.2%
Sub period: December 2009 - May 2020 Annualized return 2.0% 2.2% 0.8% Annualized volatility 5.7% 4.3% 3.8% Sharpe ratio 0.34 0.51 0.21 Max drawdown -16.1% -13.4% -12.1%
Source: Bloomberg
The return characteristics of the value benchmark are in sharp contrast to the carry benchmark. This can be seen through the skewness and the drawdown/volatility measure (the low correlation between pairs truncates the left tail). Another feature is the relatively equal distribution of returns across the two sub-periods. In comparison to a correlation of -0.5 between the carry benchmark returns and changes in currency implied volatility, the value benchmark displays a correlation of zero. An examination of rolling correlations based on 36 monthly returns reveals the correlation was negative prior to 2007 and positive thereafter. Since 2019, the correlations have been approximately zero. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Figure 13: Index performance
Source: Bloomberg
Trend-following Trend-following strategies have a long history in the currency markets, with performance characteristics well documented in the financial literature. Part of its popularity stems from the long established and varied drivers of returns including well documented phenomena related to investors’ behavior, institutional rigidities, lagged expectations, and the differing—and at times non-economic—objectives among market participants. See our publication Diversified Trend Following for a fuller discourse. Trend-following strategies have developed a reputation—especially after the Credit Crisis—as a source of uncorrelated return. Specifically, this style of investing is referred to in the popular literature as ‘crisis alpha’ or an empirical tail-hedge given its (theoretical) ability to accrue returns in both rising and falling markets. It is helpful to break-up the strategy construction into four parts: 1. Detection of trend 2. Entry/exit rules: Decide when to take a position on a particular underlying and determine stop loss rules 3. Positioning/sizing: Decide how to size a position and maximum position sizing/leverage constraints 4. Portfolio construction: Combining the individual positions We use a combination of these to construct a trend-following benchmark using the 24 US dollar currency pairs. Trend signal There are many different ways to construct trend following signals—ranging from simple moving average crossovers to more black box-style machine learning approaches. In essence, all signals attempt to identify the systematic component of price movements. Price trends can occur over multiple frequencies—ranging from intraday to multi-year. For the purposes of this analysis we rebalance on a weekly frequency and seek to capture short-to-medium term price trends. Since transparency is of paramount importance, we use a signal based on changes in price levels to measure trend. Using a single lookback window produces a binary signal (±1). For robustness, we use an aggregate signal comprising of 12 lookback windows Bloomberg Systematic Strategies A Bloomberg Professional Service offering
ranging from 1-12 months. To allow for minimal parameterization, the final signal is a simple average over the individual signals and ranges from -1 to +1. The multiple windows can also be interpreted as a measure of conviction or consistency. To try and replicate tradability, we maintain a 2-business day lag between the signal determination and rebalancing date. The weekly rebalancing frequency provides a reasonable trade-off between minimizing transaction costs and the flexibility to react to changing market conditions. The table below outlines the calculation of the individual and composite signals, where Sk refers to the signal value for a lookback window of k months.
Monthly signal Composite signal
12 Current Price 1 Sk=1 if − 1 ≥ 0, else -1 CS = ∑ Sk Lookback Pricek 12 k=1
Portfolio construction A key feature of trend-following strategies relates to the distribution of portfolio returns. Since each individual currency pair can be viewed as an independent trade that is based on the price movements of that particular asset, the trend signal reduces the correlation between asset returns. This tends to result in a strategy displaying a neutral or positive skewness of returns. From an asset allocation perspective, this feature is attractive for investors seeking to truncate downside risk—especially if the core portfolio is tilted towards seeking yield (for example a currency carry strategy). Since the dynamic nature of the direction signals makes it difficult to forecast asset correlations, the most common approach to portfolio construction is to equalize risk weights between assets using only a measure of volatility based on the long-only asset returns. Volatility weighting can take two forms depending on leverage constraints and whether the investor is seeking to target an overall level of volatility. The first is based on volatility targeting the individual instruments while the second approach uses inverse volatility weights to size asset exposures. For the trend benchmark, we employ the first approach where each currency instrument is volatility targeted to achieve a 10% annualized volatility. The leverage factor is calculated weekly using a relatively short lookback window of 3-months with daily returns and is subject to a cap. This helps control exposure mismatches during periods of market dislocations. Constructing the benchmark The role of the US dollar as the global reserve currency along with the impact of the US economy on global growth has meant trends in US dollar currency pairs tend to be stronger than cross currency pairs. It is most evident during periods of market stress and is often referred to as the ‘Dollar effect’. This, together with the reduction in asset correlations brought about by trend signals, suggests a single universe combining G10 and EM currencies is appropriate. The trend signal is measured on the underlying currency indices (see the ‘Data’ section) on a weekly basis. Given the use of 12-lookback windows, position sizing vary from -1 to Bloomberg Systematic Strategies A Bloomberg Professional Service offering
+1 in increments of approximately 0.16. For a given currency, once the trading signal has been determined, the corresponding exposure is taken in the volatility targeted instrument. Results The trend-following portfolio has produced attractive risk-adjusted returns over the past 20-years; delivering a Sharpe ratio of 0.6. A deeper understanding of the return dynamics can be gotten via dissecting performance by the currency universe and a closer inspection of individual trading signals.
Figure 14: EM and G10 FX Trend (January 2000 – May Figure 15: Comparing returns profiles 2020) EM FX G10 FX
Trend Trend Annualized return 3.1% 1.8%
Annualized volatility 4.5% 4.7%
Sharpe ratio 0.69 0.39
Drawdown -10.2% -10.7%
Drawdown/Volatility -2.3 -2.3
Skewness 0.19 0.77
Source: Bloomberg Source: Bloomberg
Performance over the full sample suggests EM currencies display superior trend- following characteristics as measured by risk-adjusted returns (Figure 14). However, mapping returns (Figure 15) highlights this difference stems from variations in performance over a small window (2005-2006). The rolling correlation between the G10 and EM components oscillates around 0.6. The large positive skew displayed by the G10 currencies (Figure 14) is confirmed by the annual returns profile (Figure 16)—which also highlights the greater variability of G10 returns. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Figure 16: Mapping annual returns: G10 versus EM trend
Source: Bloomberg
To assess the impact of varying the lookback window and signal aggregation, we examine the performance of four equally-spaced individual signals. The windows selected are 3, 6, 9 and 12-months; which are compared to the composite signal. The performance statistics (Figure 17) indicate the risk-adjusted returns are similar and modestly positive—ranging from 0.4-0.6. The two sub-periods suggest a slightly more consistent performance by the intermediate lookback windows (6 and 9 months). The key differentiating factor is the downside risk characteristics. As the lookback window is extended, the drawdown increases and the skewness is more negative. These are consistent with the literature—shorter windows are more effective in adapting to changes in price patterns; hence truncating losses. The composite signal displays a Sharpe ratio towards the higher end of the individual signal range (0.6) along with a skewness that is comparable to the short window (0.4). It is interesting to note that the relative differences between the single signal strategies is consistent across the full sample and two sub-samples; providing some support for model robustness.
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Figure 17: Performance statistics
Isolated signal 12 Signal Performance 3M 6M 9M 12M Composite
Period: January 2000 - May 2020 Annualized return 2.1% 2.7% 2.5% 2.1% 2.5% Annualized volatility 5.0% 4.8% 5.0% 5.6% 4.1% Sharpe ratio 0.43 0.57 0.50 0.38 0.60 Max drawdown -12.1% -12.5% -11.4% -19.6% -10.4% Max drawdown/volatility -2.4 -2.6 -2.3 -3.5 -2.5 Skewness (monthly) 0.51 0.14 -0.01 -0.15 0.39
Sub period: January 2000 - December 2009 Annualized return 4.1% 5.0% 5.0% 4.4% 4.8% Annualized volatility 5.0% 5.0% 5.0% 5.8% 4.4% Sharpe ratio 0.82 1.00 1.00 0.76 1.08 Max drawdown -6.5% -8.4% -7.7% -14.1% -5.4%
Sub period: December 2009 - May 2020 Annualized return 0.3% 0.6% 0.2% 0.0% 0.3% Annualized volatility 5.0% 4.5% 5.0% 5.3% 3.7% Sharpe ratio 0.06 0.13 0.04 0.00 0.08 Max drawdown -12.1% -12.5% -11.4% -15.9% -10.4%
Source: Bloomberg
The performance of the composite portfolio indicates the diversification benefits of measuring trend over different horizons. Correlations based on monthly returns (Figure 18) range between 0.5-0.9. As expected, the windows in closer proximity display the higher correlation. The variation in signals throughout the sample period can be seen by mapping index levels (Figure 19). The grey lines represent the individual strategies while the blue highlights the composite signal strategy.
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Figure 18: Correlation between individual lookback Figure 19: Impact of signal aggregation windows (January 2000 – May 2020)
3mo 6mo 9mo 12mo
3mo 100%
6mo 65% 100%
9mo 55% 72% 100%
12mo 48% 66% 88% 100%
Source: Bloomberg Source: Bloomberg
The trend signal displays considerable time variation. Since all the currency exposures are versus the US dollar, measuring the cumulative trend signal provides an indication of positioning versus the US dollar. Note, this is not the same as calculating exposure to the US dollar, which would require incorporating the leverage factor per currency. The cumulative trend signal is calculated as the simple average of the individual currency composite signals and is shown in Figure 20.
Figure 20: Variation in the trend signal Figure 21: Impact of changes in the US dollar
Source: Bloomberg Source: Bloomberg
The significance of US dollar movements are clearly visible from Figure 21; which plots median portfolio returns sorted by percentage changes in the US dollar index (DXY). The U-shaped profile provides support for the robustness of the trend signals given that each instrument is effectively a long or short dollar play. It also highlights when trend portfolios perform poorest—during periods when markets are range-bound.
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Multi-style benchmark Following the extended discussion on systematic approaches to carry, value, and trend- based investing, we compare the three style benchmarks to assess the potential synergies in the context of a multi-style portfolio. To do so, we do a side-by-side performance comparison along with macroeconomic indicators and construct a risk- based portfolio combining the three benchmarks. Each of these strategies provide positive long run returns in exchange for taking on identifiable risks. In the case of the carry trade, this materializes when investor sentiment turns bearish. Value investors suffer losses when macroeconomic policy and investor sentiment delays the convergence of exchange rates to the long run equilibrium. Trend following strategies perform poorly when the signal-to-noise ratio is low and when price trends reverse abruptly. The three types of risk events do not necessarily overlay; evidence for which is provided in the returns profile in the sections above and performance attribution below. Performance attribution The returns of a currency strategy can be decomposed into two parts—movements in the FX spot rate and the accrued carry from the interest rate differential. Analyzing the breakdown on a per strategy basis will highlight differences between strategies and the possible synergies within a portfolio context. By design, the carry return is positive (6.8%) over the full sample for the FX carry strategy (Figure 22). This is partially offset by the negative spot return (-2.3%) and confirms prior results in the literature regarding the so-called violation in the short run of uncovered interest rate parity (UIP).3 The results are quite different for the value and trend strategies in two aspects; both carry and spot return contributions are positive and the majority of returns is from movement in the spot rate. This indicates a high degree of complementarity between carry and either value or trend. Figure 22: Style performance attribution
FX Carry FX Value FX Trend Period: January 2000 - May 2020 Annualized return 4.5% 3.8% 2.5% Spot return -2.3% 3.5% 2.0% Carry return 6.8% 0.3% 0.5%
Source: Bloomberg
How does this breakdown between carry and spot vary over time? We plot the contributions for each of the three strategies to assess how representative the full sample attribution is and what the respective contributions are during periods of market stress. The carry return for FX Carry is positive with negligible volatility (Figure 23) while the spot return is time varying; with moderately positive returns until the Credit Crisis— following which returns have displayed a steady pattern. As can be seen, the spot returns determine the overall volatility of the strategy. In the case of the FX Value strategy (Figure 24), the spot return is consistently positive barring a period between 2010-2015, while the carry return—once again displaying negligible volatility, is approximately flat
3 The G10 FX Carry Premium, Nov 2010, Bloomberg LP Bloomberg Systematic Strategies A Bloomberg Professional Service offering
over the full sample; having a positive return till the Credit Crisis and negative thereafter. Figure 23: FX Carry Figure 24: FX Value
Source: Bloomberg Source: Bloomberg
Positioning in the FX Trend strategy is more dynamic than both the carry and value strategies—which should lead to higher volatility in the carry return contribution. As we see from Figure 25, this is precisely the case, though the spot volatility still dominates. The carry contribution is slightly positive over the full sample (10% since 2000). Notably, it is negative during the immediate period around October 2008 when risk-off sentiment dominated and the strategy was long the low yielding currencies. As seen from Figure 22, the spot return is positive over the sample. Of particular note is the marked positive return during Q3/Q4 2008 and more recently towards the end of 2014 on global growth concerns.
Figure 25: FX Trend
Source: Bloomberg
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Constructing the portfolio Given the differences in the drivers of returns, combining investment styles is likely to deliver diversification benefits. Over the full sample, the pairwise strategy correlations are zero. If we repeat this exercise using non-overlapping subsamples, we get a similar pattern (Figure 26)
Figure 26: Correlations are stable over subsamples Figure 27: Conditional returns highlight (2000 – 2020) diversification properties (2000 – 2020)
FX Carry FX Value FX Trend
FX Carry -2.2% 0.8% -0.1%
FX Value 0.5% -2.0% 0.4%
Bottom 25% 25% returns Bottom FX Trend 0.2% 0.4% -1.2%
Source: Bloomberg Source: Bloomberg
The average subsample correlations (Figure 26 in dark blue) indicates the style returns display a lower level of correlation. The mean absolute correlation (light blue) confirms this (since this calculation avoids the offsetting of negative and positive correlations). It is also clear that low correlations do not imply good tail hedging properties; conditional correlations might be elevated and the realized returns might be negative even if correlations remain low. To examine this, we measure the median strategy returns by conditioning on the bottom quartile of monthly returns per strategy (Figure 27). Doing so illustrates the hedging qualities between strategies—which should translate to improved downside performance for a multi-style portfolio. Since correlations are low and changeable between strategies, a transparent and robust approach is to use a risk-based approach. Strategy weights are allocated based on historical volatility using a 26 weeks (half year) lookback window. Taking into account operational considerati0ns and that correlations are relatively slow-moving, the multi- style portfolio is rebalanced monthly.
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Figure 28: Portfolio weights (July 2000 – May 2020) Figure 29: Multi-style portfolio: Index performance
Source: Bloomberg Source: Bloomberg
Strategy weights do vary over time (Figure 28) with an average monthly turnover of approximately 10%. Given currency transaction costs range from 1-5 bps and the underlying instruments are one-month forwards, this should add little to transaction costs. The native volatility of the trend strategy is lower than that of the value and carry strategies which results in it having the largest weight (on average) in the portfolio. The combination of low correlations between the constituent benchmarks and the risk weighting approach to portfolio construction delivers risk adjusted returns that are significantly higher than the constituent strategies (Figure 30). It also results in a neutral skew and a volatility-to-drawdown ratio that is superior to the component parts (-1.8). Correlation to both the US dollar and market sentiment (as measured by FX and equity implied volatility) is low. This translates to a more stable returns profile as can be seen by the performance during the two sub-sample periods which displayed very different market environments. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Figure 30: Performance statistics: Multi-style benchmark versus the constituents
Style Multi-style FX Carry FX Value FX Trend Period: July 2000 - May 2020 Annualized return 4.6% 3.9% 2.4% 3.7% Annualized volatility 7.4% 7.0% 4.1% 3.1% Sharpe ratio 0.61 0.56 0.57 1.17 Drawdown/Volatility -3.4 -2.3 -2.5 -1.78 Skewness -0.96 -0.01 0.42 0.13
Correlations US dollar -21% 22% -7% -1% FX volatility -61% 8% 26% -6% Equity volatility -53% 4% 16% -16%
Sub period: July 2000 - December 2009 Annualized return 9.0% 6.1% 4.7% 6.5% Annualized volatility 8.2% 8.1% 4.5% 3.7% Sharpe ratio 1.10 0.75 1.06 1.76
Sub period: December 2009 - May 2020 Annualized return 0.7% 2.0% 0.3% 1.2% Annualized volatility 6.6% 5.7% 3.7% 2.3% Sharpe ratio 0.11 0.34 0.08 0.51
Source: Bloomberg
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Scenario analysis Part of any investment process is to undertake a ‘what-if’ analysis to assess the robustness of the portfolio under conditions of severe market stress. Since 2000, we have identified two events that qualify—the Global Financial Crisis (May 2007 – March 2009) and the current economic and social crisis caused by the infectious disease COVID-19 (January – May 2020). Figure 31: Performance during the Global Financial Figure 32: Impact of COVID-19 Crisis
Source: Bloomberg Source: Bloomberg
During the Global Financial Crisis (GFC) the three style benchmarks displayed high levels of volatility with carry experiencing a sharp sell-off (Figure 31). The composite portfolio however displayed a level of volatility in-line with the full sample and gained 1.5% during this period (Figure 33). This performance was in part due to the increasing allocation to trend during the course of 2008 (Figure 28). The limiting of the drawdown to -3.7% provides further evidences of tail hedging discussed earlier (Figure 27). Figure 33: Performance statistics during market shocks
FX Carry FX Value FX Trend Multi-style Global Financial Crisis Annualized return -5.4% 4.5% 4.4% 1.5% Annualized volatility 12.8% 9.5% 5.2% 3.3% Sharpe ratio NA 0.47 0.85 0.46 Drawdown -25.3% -11.9% -4.8% -3.7%
COVID-19 Return -6.7% 1.7% -1.3% -1.8% Drawdown -10.4% -2.9% -3.1% -3.0%
Source: Bloomberg
The ongoing pandemic-driven economic crisis has seen the multi-style portfolio experience a moderate decline (-1.8%) between January-May 2020. This was despite the large drawdown experienced by the carry portfolio that was not compensated by trend and value—which appear to have offsetting returns for much of period (Figure 32). Bloomberg Systematic Strategies A Bloomberg Professional Service offering
Conclusion The three risk premia investment styles discussed in this publication are among the most popular within the systematic investment community. Each strategy provides investors with exposure to a different risk factor in exchange for a positive long-run return. The strategy benchmarks are intended as building blocks that isolate exposure to a single risk premium. As we show, this is helpful in constructing multi-style portfolios that are both robust and provide transparency through returns attribution. The aim of this discussion was to highlight the broad long-run performance characteristics of each style along with providing a roadmap by which investors can customize the strategies to suit individual needs. This can either be in the form of a risk premia portfolio or indirectly to enhance the returns of an international portfolio with embedded currency exposures. Bloomberg Systematic Strategies A Bloomberg Professional Service offering
References 1. Ghia K. (2014), “Diversified trend following”, Bloomberg LP 2. Ghia K. (2016), “Refining and timing the crash risk premium”, Bloomberg LP 3. Ghia K. (2010), “The EM FX carry premium”, Bloomberg LP 4. Ghia (2010), “The G10 FX Carry Premium”, Bloomberg LP 5. Ghia K., Donat M., Khambatta Z. (2019), “Deconstructing currency carry: Is there a single benchmark”, Bloomberg LP 6. Menkhoff L., Sarno L., Schmeling M., Schrimpf A. (2016), “Currency Value”, The Review of Financial Studies 7. Ghia K., Lazanas A. (2011), “Sequencing the strategy genome”, Bloomberg LP 8. Khambatta Z., Ghia K., Lazanas A. (2015), “Currency risk management in bond portfolios”, Bloomberg LP 9. Brunnermeier M., Nagel S., Pedersen L. (2008), “Carry trades and currency crashes”, NBER Macroeconomic Annual
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