1、24 January 2019MULTI-ASSETGLOBALPierre BlanchetHead of Multi-Asset StrategyHSBC Bank plc+44 20 7991 5388Duncan TomsMulti-Asset StrategistHSBC Bank plc+44 20 7991 3025Max KettnerMulti-Asset StrategistHSBC Bank plc+44 20 7991 504520%15%10%6E 號a W8XX山Maximum Risk-Return Ratio。一行a5 0 5 02 2 I ISource: H

2、SBC, BloombergVolatility (Std. Deviation)00Last year we saw unusual cross asset behaviour. This led many to ask if the characteristics of havens have changed.We would answer no, and went a step further to rank safe havens on a cross-asset basisThe champions are US Treasuries, JPY and gold, in that o

3、rderIt appears the days of historically low volatility are numbered. When vol rises, safe havens may act as an effective hedge when risk assets sell off. Yet, many such assets have failed to protect investors, most prominently USTs in early-18. We therefore put safe havens through their paces to det

4、ermine the best safe haven under higher vol.Our analysis focuses on simulating risk-return characteristics - chart 1 is a scatter plot that includes 10 million simulations of various portfolio weightings. From this analysis we find that short-end USTs are our safe haven champions. This is followed b

5、y the JPY in second place, and gold gets the bronze medal in third. Hence, despite short-end UST yields broadly rising as the Fed has hiked since 2017, it is clear that their safehaven characteristics snap back during times of market turbulence. Additionally, USTs appear to be the best safe haven fo

6、r EUR-based investors too. Meanwhile, we find the CHFs safe-haven status has significantly diminished during the last two years.Hedging risksIn Top 10 risks for 2019 we find investors, top concerns were a Eurozone crisis 2.0 and US corporate margins fall. USTs and the JPY can be used to hedge these

7、risks.Ten million simulations for a basket of US equities, US Treasuries, and JPY-USDEfficient Frontier 25% 3530USTs: short end vs. long end of the curveUp to this point we have given little regard to why we selected the short end of the curve for our analysis as opposed to the long end. Ultimately,

8、 an investors preference for a certain part of the curve depends on a number of factors, but from a mean-variance standpoint, the short-end of the curve appears more attractive.Purely from a mean-variance standpoint, short-end USTs are preferable to US 10YChart 11 overlays the mean-variance frontier

9、s of two groups of portfolios. The only difference between these two groups is UST maturity: one group includes the total return of 1-3Y USTs (red data cloud), and another the total return of the 10Y UST instead (grey data cloud). Broadly, the group with 1-3Y USTs has an enhanced efficient frontier

10、beyond that of the group with the 10Y UST. As the highest risk-return ratios across the two data clouds occur in the group with 1-3Y USTs, we can infer that, from a mean-variance standpoint, the short end is more attractive. Only investors with a high level of risk tolerance would choose portfolios

11、which include the 10Y UST rather than 1-3Y USTs.US rates: 1-3Y vs 10Y25%20%15%10%5%0%25%20%15%10%5%0%0%1%2%3%4%5%6%7%8%Volatility Short-end USTs, JPY-USD, S&P US 10Y, JPY-USD, S&P Maximium Risk-Return ratio across all weightings Source: HSBC, BloombergEUR-based investors We ran the equivalent analys

12、is for a EUR-based investor The results were largely unchanged. .as US Treasuries remained safe-haven championsA different harbour for EUR-based investors?So far weve taken the perspective of a USD-based multi-asset investor. But for a EUR-based investor, the outcome might be different. However, our

13、 analysis shows that its not and the equivalent safe-haven championship draws largely similar conclusions.USTs still safe-haven champions for EUR-based investorsWe used the EUROSTOXX in place of the S&P 500 for equities, filtered for a high level of VSTOXX, in the same way as we filtered for a high

14、level of VIX, and priced our safe havens in EUR. Here our model found preferences for (1) Schatz, (2) Gold priced in EUR, (3) JPY-EUR, and finally (4) CHF-EUR. However, proving that any great champion can triumph both at home and abroad, USTs are still the winner. Indeed USTs continue to trump all o

15、f the above, including Schatz, even when short-end USTs are priced in EUR. They remain the number one safe haven.Charts 12-15 display these results. Note that for all max risk-return ratio portfolios (white diamonds), there is a 0% weighting to equities. Chart 12 has a basket of Schatz and JPY-EUR,

16、and we find higher weightings in Schatz for those simulations with the highest risk-reward ratios.Basket: EUROSTOXX, Schatz, JPY-EUREfficient FrontierEfficient FrontierVolatility (Std. Deviation)Source: HSBC, BloombergOptimal weighting based on risk-reward (white diamond):90% Schatz 1-3Y10% JPY-EURC

17、hart 13 has a basket of gold (priced in EUR) and JPY-EUR. As the efficient frontier has shifted right, we can conclude that gold is not as strong a safe-haven asset as Schatz. Given the marginally higher weighting in gold (priced in EUR) for the simulation with the highest riskreward ratio, we can a

18、lso conclude that gold edges JPY as a better safe-haven asset for a EUR investor.Basket: EUROSTOXX, Gold in EUR, JPY-EUREfficient Frontier 20%18%12%10%皿40350%2%4%e%Volatility (Std Deviation)Source: HSBC, Bloomberg10%1000Optimal weighting based on risk-reward (white diamond):58% Gold (priced in EUR)

19、42% JPY-EURChart 14 shows once again the CHFs diminished safe-haven status.Basket: EUROSTOXX, Gold in EUR, CHF-EUREfficient Frontier 20%18%9%BEnlBa peu&x 山2%Volatilitv (Std Deviation)1005Q0o行a0 5 0 5a2 2 14010%Source: HSBC, BloombergOptimal weighting based on risk-reward (white diamond): 100% Gold (

20、in EUR) 0%CHF-EURFinally, chart 15 has a basket of USTs (priced in EUR) and JPY-EUR. Due to the yellow, and some red, points indicating higher risk-reward ratios, we can conclude that USTs trump Schatz as the best safe-haven asset, even when priced in EUR.15. Basket: EUROSTOXX, USTs in EUR, JPY-EURE

21、fficient FrontierVolatolity (Std Devwtjon)Source: HSBC, BloombergOptimal weighting based on risk-reward (white diamond):89% USTs in EUR11% JPY-EURWhen looking through charts 12-15, it is striking how the scatter plots present a sea of blue and turquoise points rather than including the yellow and re

22、d dots as per all previous mean-variance diagrams. This indicates lower risk-return simulations, and ultimately poorer efficient frontiers, than in the case of USD-priced assets above. With the exception of Schatz, this is therefore likely due to the FX implications of pricing safe-havens in EUR rat

23、her than USD. Such implications are partly a function of a weaker EUR during the period analysed, but more likely a function of the USDs risk-off allure. With the exception of the JPY, the USD is the strongest risk-off currency. Moreover, the USDs current hedging costs also eat into profits for EUR-

24、based investors. And yes, the allure of USTs on a global scale has a large part to play in the USDs risk-off role.So our model does not find a materially different conclusion for a EUR-based investor as opposed to a USD-based investor: US Treasuries are the best safe-haven asset of choice.Hedging ri

25、sksOur recent publication “Top 10 risks for 2019J, included an online pollThe results show that investors, top concerns are a Eurozone crisis 20 and US corporate margins fallOur analysis shows that, under those scenarios, front-end US Treasuries and JPY can be used as both hedges and safe havensThe

26、big unknownsIn Top 10 risks for 2019we included an online poll in which we asked investors to tell us which risks were among their top concerns for the coming year.16. Top 10 risks for 2019 一 readers5 poll results Percentage of respondents who selected this riskEurozone 2.0US corporate margins fallT

27、he Fed keeps hikingNo bid in a credit sell-offLeverage risks and accounting tacticsTrade tensions endFixed income volatility comes backBrace for (climate) impactThe ECB initiates new unconventional policiesEM reform surprises10%20%30%40%50%60%Source: HSBCIn this section, we look for potential hedges

28、 for the top two risks according to the poll i.e. assets which show non-positive correlations over five years with the assets impacted by these risks. We find that 1-3Y US Treasuries and JPY have these characteristics and can therefore act as hedge assets for those risks as well as safe havens in th

29、e event of broader market turmoil.Eurozone crisis 2.0Risk scenarioIn a scenario where stimulus might be needed, the Eurozone may face problems in a year of - possibly disruptive - leadership changes. The political landscape has shifted since 2012, with populist movements gaining more traction across

30、 Europe. Shaky economic foundations and political uncertainty would not constitute a healthy backdrop and could lead to renewed risks for the Eurozone.Impacts and potential hedgesThe EUR could come under pressure and, according to our FX strategists, could go to parity with the USD. Therefore, buyin

31、g USD and JPY versus the EUR would be relevant in this scenario. GBP would also face uncertainty as the post-Brexit trade negotiations could becomemore difficult. In addition, correlation data suggests that USD-GBP could be an interesting hedge (the ten year average 120d correlation is -0.6).Our Fix

32、ed Income strategists believe the impact on fixed income markets would be mixed. With renewed focus on credit risks, they would be cautious on Eurozone Sovereigns with weaker fundamentals, for example non-core, and to some extent semi-core. In this situation, Bunds would most likely perform best. We

33、 also believe that, given German bond valuations and doubts over the fate of the eurozone, flight-to-quality would be to US Treasuries. These would become a safe haven for investors in case of significant turmoil (see previous section). Therefore, frontend US Treasuries would be a good hedge in a Eu

34、rozone crisis 2.0 scenario and would also become a safe haven if the situation was to deteriorate into a crisis.European equities should be negatively impacted by rising non-core eurozone bond yields and renewed loss in confidence. The analysis of cross asset correlations (see Data Matters、17 Oct 20

35、18) shows that front-end Treasuries have interesting correlation characteristics with European equities (Chart 17). However, JPY-USD is the best candidate as a hedge (Chart 18 five year average 120 day correlation of -0.4 with MSCI Europe) for European equities. Moreover, similar to front-end US Tre

36、asuries, the Japanese yen has safe-haven characteristics and could therefore act as both a hedge and a safe haven under this scenario.Front-end USTs and MSCI Europe are negatively correlated on averageJPY-USD shows a constant negative correlation with European equitiesJun-14 Nov-14 Apf-15 Sep-15 Feb

37、-16 JuH6 Dec-16 May-17 Oct-17 Mar-18 Aug-18 Jan-19Jun-14 Nov-14 Apf-15 Sep-15 Feb-16 JuH6 Dec-16 May-17 Oct-17 Mar-18 Aug-18 Jan-19120d correiatiofl MSCI Europe and 1 -3y UST (USD)5y average120d price return correlation of JPYUSD and MSCI Europe5v averageSource: HSBCSource: HSBCUS corporate margins

38、fallRisk scenarioRising profit margins have been the major driver of US earnings growth since the end of the global financial crisis. Average US corporate net profit margins are now at an all-time high of 11.7%. However, companies are facing rising cost pressures, including wage growth, trade tariff

39、s and financing costs, which could hurt margins going forward.Impacts and potential hedgesFaster-than-expected wage growth could cause a significant miss to earnings estimates. Were net profit margins to fall back to their 10-year average of 9%, US equities EPS would fall by 20%, which would negativ

40、ely impact US equity returns. Our analysis of cross-asset correlations suggests, once again, that front-end US Treasuries and the JPY are potential hedge candidates, but also UK Gilts (5y average 120d correlation with MSCI US = -0.3) could be used to diversify from USTs.With leverage measures close

41、to all time high, USD Credit would also become vulnerable in a falling US corporate margin scenario.AppendicesSelection of potential safe havensChart 1 shows our current multi-asset correlation heatmap, where a number of risk-off assets are at the top of the heatmap - they are highly positively corr

42、elated with one another and negatively correlated with risk-on assets. This mostly includes DM credit, JPY-USD, and DM rates in countries such as the Japan, the US, and the UK.We began by ruling out UK rates as a safe haven on the basis that ongoing uncertainty surrounding Brexit may well have erode

43、d any safe-haven characteristics. Idiosyncrasies surrounding a sudden change in UK-EU relations may well de-couple UK rates from behaving in a safe-haven manner consistent with the broader global backdrop.We then also decided to rule out all investment grade (IG) credit as a safe haven. While IG cre

44、dit might often be viewed as a usaferM asset than high yield debt, or perhaps equities, we think risks surrounding the asset class are too high to deem it a safe haven. Indeed, credit is really a spread product and not a safe haven. A possible reason why it falls into the risk-off basket is due to d

45、uration and high correlation with government bonds. Furthermore, our Fixed Income team have a bearish view on global credit, and view any strong equity sell-off to likely be coupled with widening credit spreads. So despite IG credit displaying risk-off characteristics, we ruled out using credit as a

46、 safe-haven in our analysis.Multi-Asset correlation heat mapJPY-USDJPY-USD19 Jul 2018-15 Jan 201。Apu山s-sw -BESnpu- clsnaq。s Enb luselz山山ZO3山2山SQ5bBw工山 s三nbdon山 84-5b oJoed s三nbEoUJT an山 asrkdcclo asndn 山 asnLln 工 asnMorx asnan4 asnaqzQeoorq p 2 山 Qsn)lqp工山 asnNX 工 s-，2snooald asn,便 N-AOL A_星AOL snpa

47、j。d0。0- asn tpal。d_0。0- an山 AOL UBE0 AOL xnNpaJ。9-0。0- dCQo xoolsgzlLivestock GBP IG corp credit UK 10Y German 10Y EUR IG corp credit USD IG corp credit US 10Y Japan 10Y Italy 10Y INR-USD Precious Metals MXN-USD EM debt (USD) EM debt (local) ZAR-USD AUD-USD KRW-USD HUF-USD EUR-USD GBP-USD EUR HY cor

48、p credit USD HY corp credit North America equities Pacific equities Europe equities EM Asia equities EM E&ME equities EM LatAm equities CAD-USD Industrial Metals Energy Agriculture BRL-USDSource: HSBC, BloombergFiltering with an indicator of fearWhen choosing data to analyse safe havens we needed to

49、 select, or rather filter for, data for periods during which investors are searching for a hedge as risk assets come under pressure. Therefore the data used must satisfy periods where developed market - particularly US - equities are selling-off, and when risk-on/risk-off correlations are evident. O

50、nly then will a flight to safety be likely to occur.We used the level of the VIX to filter the data, for two reasons.First, the VIX displays a strong negative correlation with the S&P: when US equities sell-off, the VIX rises. The 90 day average correlation between the S&P500 and the VIX Index over

51、the last five years is -0.82. Yet this should hardly be surprising given that the VIX is informally known as “the fear index among investors.Second, it is also apparent that the VIX has some association with the strength of risk-on/risk-off correlations. For example, when the VIX spiked in February

52、2018, our RORO indicator that gauges the strength of cross-asset risk-on/risk-off correlations also rapidly increased.In choosing a VIX threshold, we wanted to find a level above which asset class behaviour changes. We decided upon a level of 20 as this is just above the lower bound of the upper qua

53、rtile in the VIX since 2010.Choosing our timeframeIn terms of our time filter, we have looked at data since mid-2017. It was at this time point, following the French presidential election, that our RORO indicator dropped off significantly (Chart 2). Hence, by including data prior to mid-2017, we wou

54、ld be looking at a different phase of RORO where safe-haven assets might have performed very differently. For example, the CHF and gold may have been stronger safe-haven assets during this period, at least relative to USTs. If, in our mean-variance analysis, we were to include data from this period,

55、 then our results may be skewed by behaviour of safe-haven assets in a very different market regime. Additionally, over the post mid-2017 period, equity vol has been by and large very low, allowing us to filter for a level of VIX that corresponds with recent equity sell-offs.We have also found that

56、changing the time horizon does not significantly alter our safe-haven ranking. Both longer and shorter time horizons led to the same safe-haven ranking.HSBC RORO IndexSource: HSBC, BloombergEfficient frontiersThroughout this report you have seen clouds of data in the risk-return space. In chart 3, w

57、e have constructed a data cloud using random data. Each diamond represents a portfolio with different weightings of assets. The efficient frontier is defined by the portfolios that form theupper bound of this data cloud, highlighted in chart 3 as red diamonds. Essentially, these are the portfolios t

58、hat maximise returns for a given level of risk. These portfolios are superior to those beneath the upper bound as the former always generate higher returns for any given level of risk than the latter.As an example, consider the two data points in chart 4. Portfolio B is inefficient. It is expected t

59、o return 40% for 100% risk. This can immediately be improved upon by moving to a portfolio on the efficient frontier, e.g. point A. Portfolio A returns 55% for the same level of risk as point B (i.e. 100%).The Efficient FrontierSource: HSBCSource: HSBC80%60%40%20%0%0% 25% 50% 75% 100% 125% 150%Risk

60、(Volatility)4. Focussing on two pointsBond-equity correlationThe US bond-equity correlation is typically negative.5. US bond-equity correlation1 THistorical distributionUS 10Y bond/equity cxirrelation25-75 percentile rangeMedianSource: MSCI, Refinitiv Datastream, HSBCDisclosure appendixAnalyst Certi