1、1|FOCUS02/05/19 Luca Maia,Emerging Markets/Latam Strategy|Gabriel Gersztein,Global Head of Emerging Markets Strategy BNPP-EM economic surprise index:It is all about China KEY MESSAGES This is the bi-weekly update on EM-economic surprises.Our index has risen since the last update.A historical look at
2、 the index suggests there is a strong causality with EM credit;this rise therefore has been supportive of our globally bullish EM outlook for 2019.In December,the index suggested surprises had been negative for almost 13 consecutive weeks.This cycle appears to be over,with the index bottoming out in
3、 mid-January.Chinas economic surprises have moved to the upside since the beginning of the year.The index,calculated on a daily basis,is an aggregation of individual surprises and measures the differences between actual data and consensus forecasts.It also makes several technical adjustments to link
4、 surprises to market movements.The index score is positive/negative if the combined contribution from all the surprises over a period of time is positive/negative.The approach has two goals:(i)To give a quick overview of surprises in historical and the latest macroeconomic data;(ii)To be a complemen
5、tary tool in assessing the impact of surprises on EM FX and sovereign bonds.Definition of surprise A surprise is the standard deviation between macro data and the value expected by consensus.Input=Economic release consensus forecast(median)Surprise=Input (input)since year 2008/(input)since year 2008
6、 Solving the time decay problem Our economic surprise approach adjusts each surprise with a decay factor,to minimise the issue of time decay.The importance of economic data decreases in investors minds as time goes by,probably due to the unconscious bias toward marginal and new incoming information,
7、and the fact that,as time passes,prices tend to incorporate other variables and information.Moreover,new information supersedes old information and is factored into investment decisions with higher importance,and recalling details from a little while ago is more difficult than recalling yesterdays d
8、ata.This basic use-it-or-lose-it feature of our memory,known as transience,is more exacerbated in the financial market due to the quantity and frequency of data and news flow to which agents are exposed.The decay is an exponential function that decreases in value each day after the initial data rele
9、ase.Thus,different economic series will have different decay factors.The upside changes in our economic surprise indicator continues to bode well for our view on EM assets.We have been bullish on emerging markets since Q4 2018,although we note that there are some idiosyncratic stories and global ris
10、ks about which we would be cautious.Our analysis of flows,USD funding,rollover risk,financial transmission channels and the impact of corporate HY all point to a supportive scenario for EM(see links in the annex).Due to the lingering risks in place,though(China-US trade negotiations,global slowdown,
11、geopolitical tensions),our model of risk premium has been a key driver for our strategies in 2019.FOCUS|EM Global02 May 2019 EM STRATEGY Please refer to important information and MAR disclosures at the end of this reportMARKET VIEW 2|FOCUS02/05/19 BNPP-EM economic surprise index continues to edge up
12、wardsWhen we launched our EM Economic Surprise Index(link)in December 2018,it suggested negative surprises had started to bottom out.This seems to have been borne out,as the index has improved significantly since then(Figures 2 and 4).Figure 1 shows that the index has been a good indicator of EM sov
13、ereign yields since Q2 2017.Currently,South Korea,China(Fig.2),Colombia,and Indonesia have the highest positive surprises while India,Taiwan,Peru and Brazil rank at the bottom.Both the index and the 3m average trend are positive at the margin now.Surprises were consistently negative in Q2-Q3 2018 th
14、e longest negative cycle(in size and duration)of the last four years.It remains to be seen if the recovery YTD,led by China,represents the beginning of a new positive,upward trend or a one-off pick-up like in November 2018.Due to the causality effect with EM sovereign credit yields,forward-looking d
15、ynamics are crucial for our EM scenario in Q2-Q3 2019.Fig.1-3 and Table 1:EM economic surprise index and sovereign bond yields+China economic surprise index Sources:BNP Paribas,Bloomberg LLP;as of 5 May 2019 SurpriseGDPZ-ScoreWeightex-ChinaLatin America(0.34)17%31%CEEMEA(0.08)12%23%China0.7346%South
16、 Korea0.316%11%Rest of Asia ex-Japan(0.87)19%36%Current EM Surprise Value0.123-month trend0.06Current EM Surprise Value(0.21)ex-China(0.10)3-month trend(2.5)(1.3)0.01.32.53.85.0Nov-14Aug-15May-16Feb-17Nov-17Aug-18EM surprise cyclesintensityEx-ChinaCumulative negative surprises scoreCumulative positi
17、ve surprises score0.124.913.13.74.34.95.56.1(0.4)(0.3)(0.2)(0.0)0.10.20.4Nov-14Jul-15Mar-16Nov-16Jul-17Mar-18Nov-18Emerging Markets Surprise IndexTrendEM Hard Curr Yield Tot Ret Unhedged(rhs;inverted)0.73(0.7)(0.4)0.00.40.71.1Jun-15Mar-16Dec-16Sep-17Jun-18Mar-19China Surprise IndexSmoothedEM STRATEG
18、Y 3|FOCUS02/05/19 Country and data weight The economic surprise approach maps 17 countries in Latin America,Asia,and CEEMEA with a combined GDP of USD26.5trn.The weight of each country is equivalent to its GDP in US dollar terms,as per the World Bank norms.We established three different weights for
19、each economic indicator based on their historical impact on FX,credit,and rate markets:1=low impact;2=medium impact;3=high impact.The distribution of weights on a country basis was done keeping the arithmetical mean of the weights at 2.See Table 1 for details.Table 1:Country and data weight Sources:
20、BNP Paribas,Bloomberg LLP;World Bank Indicators WeightIndicators WeightCHINAM22SOUTH KOREAIndustrial Production312,238Trade balance21,531Unemployment2(GDP USD mn)Industrial Production3Exports2PMI manufacturing3Imports1New Yuan Loans2Average2Retail Sales1Reserves1REST OF ASIAMalaysia Ind Production1A
21、verage25,055Taiwan Ind Production2Thailand Exports3ARGENTINAIndustrial Production3Indonesia Exports2637Economic Activity2India Ind Production3GDP YoY1Philippines remittances1Average2Average2COLOMBIARetail Sales3BRAZILIndustrial Production3314Trade Balance22,056Caged2GDP YoY1Retail Sales3Average2Budg
22、et2Tax revenues2CHILEEconomic Activity3Trade balance1277Unemployment1GDP1Retail Sales2Average2Trade balance2Average2MEXICOIndustrial Production31,151Trade Balance2TURKEYIndustrial Production2Consumer Confidence1852Trade Balance1Gross Fixed Investment2Current Account3IAGE Economic Activity3Average2GD
23、P YoY1Average2POLANDCurrent Account2526Sold Industrial Output3RUSSIATrade balance3Retail Sales31,578Industrial Production3Trade balance1Retail Sales2GDP YoY1Unemployment1Average2GDP YoY1Average2SOUTH Economic Activity3AFRICAUnemployment1349Retail Sales2Trade balance2Average2EM STRATEGY 4|FOCUS02/05/
24、19 Annex:Technical explanation and previous research For illustration purposes,we use the monthly retail sales in a certain country.Assuming that six days ago the release showed a negative 8.7%y/y growth and the consensus forecast was-0.45%y/y,the input would be:-8.7%-(-0.45%)=-8.25%The problem with
25、 this input is that the number is subjective to each of the series and,therefore,cannot be compared across the board.This is solved by computing the standard deviation()over the length of the series(since 2008 in our case).Assuming that the standard deviation is 3.78%for retail sales,the surprise nu
26、mber will be:-8.7%-(-0.45%)=-8.25%/3.78%=-2.18 Going forward,the standard deviation will be calculated on a 10-year rolling basis.Then,the computation of the decay today(six days after the release or t+6)for any monthly economic release would be equal to:exp(daily decay*number of days since the rele
27、ase)In the specific example of retail sales:Surprise*6-day decay=-2.18*exp(-*6)=-1.58 The time decay()adjustment six days after the release would be 0.60 or the difference between-2.18 and-1.58.Tomorrow(t+7),the hypothetical input of retail sales in the economic surprise index will be:Surprise*7-day
28、 decay=-2.18*exp(-*7)=-1.50 The lambda()coefficient assumes that,at the time of the new retail sales data,there is a 20%of lingering effect on prices from the prior retail sales release.exp(-30 days*)=0.20 ln(exp(-30*)=ln(0.20)-30*=ln(0.20)=log(0.20)/-30 =0.0536 Links to previous EM publications Mod
29、el of portfolio flows calling for a reversal:EM Flows Projection-One of the drivers of our bullish EM view.Risk of US Corporate HY:EM sovereign credit-Look at US corporates and see the future Liquidity Analysis:Global Liquidity and EM:USD the key driver Dollar Funding:USD funding still supportive,bu
30、t weak at the margin EM rollover risk:EM scenario:Harbingers of FX and rollover risk.Understanding EM transmission channels:EM Strategy:Understanding the transmission channels on local yields China and EM:Emerging Markets FX:The RMB is the new US dollar.Inferring cycles from market prices:EM Strateg
31、y-Inferring credit cycles from EM asset prices EM STRATEGY LEGAL NOTICE This document has been written by our Strategist and Economist teams within the BNP Paribas group of companies(collectively“BNPP”);it does not purport to be an exhaustive analysis,and may be subject to conflicts of interest resu
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