Systematic Managed Floating Jeffrey Frankel Harpel Professor of

Systematic Managed Floating Jeffrey Frankel Harpel Professor of

Systematic Managed Floating Jeffrey Frankel Harpel Professor of Capital Formation and Growth Harvard Kennedy School, Harvard University 4th Asian Monetary Policy Forum Singapore, 26 May, 2017 under the auspices of the Asian Bureau of Finance and Economic Research (ABFER), with support from the University of Chicago Booth School of Business, the National University of Singapore Business School and the Monetary Authority of Singapore (MAS). Countries choice of exchange rate regimes A majority neither freely float nor firmly peg. Intermediate exchange rate regimes, then. But, in practice, they also seldom obey well-defined target zones or basket pegs. Many are murky or flaky. Proposed: a regime of systematic managed floating, where the central bank regularly responds to changes in total exchange market pressure by allowing some fraction to be reflected as exchange rate, and the remaining fraction to be absorbed as FX reserves. Introductory motivation: Consider the external shocks hitting EMEs since 2003. 2 Asian central bank reactions to 2010 inflows: Korea & Singapore mostly took them in the form of reserve gains, while India & Malaysia mostly took them in the form of currency appreciation. FX reserve gains vs. currency appreciation more-managed floating less-managed floating Source: GS Global ECS Research, Goldman Sachs ,10/13/2010. 3 Data: Haver Analytics and Bloomberg Reactions to outflows in Taper Tantrum, May-Aug., 2013. Again Singapore intervened, India & Philippines mostly depreciated.

FX reserve loss vs. currency depreciation less-managed floating more-managed floating 4 Reactions to outflows in China Tantrum, July-Dec. 2015. Again, Singapore mostly gave up FX reserves. & the Philippines mostly depreciated. FX reserve loss vs. currency depreciation less-managed floating more-managed floating 5 Why choose a systematically managed float? Textbook view: intermediate regimes allow an intermediate degree of monetary independence, including freedom from external shocks, in return for an intermediate degree of exchange rate flexibility. But -- four challenges: (a) the corners hypothesis, (b) dilemma vs. trilemma, (c) intervention ineffectiveness and (d) exchange rate disconnect. 6 Challenge (a): Corners Hypothesis Intermediate regimes are increasingly unviable. Countries are forced to move to corners: free float or firm fix. An impressive pedigree, including: Eichengreen (1994), CFR (1999), Summers (1999), Meltzer (2000), and Fischer (2001). But, theoretically, there are perfectly well-developed theories of intermediate regimes, e.g., target zones: Krugman (1991); and

empirically, managed floats are now the biggest category though many of them remain murky. Ghosh, Ostry, & Qureshi (2015). Ilzetzki, Reinhart and Rogoff (2017). 7 Managed floats have been rising as a share of EM exchange rate regimes Distribution of Exchange Rate Regimes in Emerging Markets, 1980-2011 (% of total) } Ghosh, Ostry & Qureshi (2015) 8 The Trilemma or Impossible Trinity At each corner of the triangle, it is possible to obtain 2 attributes. But not all 3. . Full capital controls Pure Float Firm fix Intermediate regime => (a) Forced to choose between corners? No. Triangles have sides! 9 Challenge (b): Dilemma not trilemma Challenge to trilemma from Rey (2014) and Agrippino & Rey (2014), Farhi & Werning (2014), Edwards (2015). Claim: Floating rates dont offer insulation from external shocks such as VIX. The triangle collapses into a single line segment, running from monetary independence via controls to open capital markets,

with the choice of exchange rate regime not relevant. Monetary independence Full capital controls Monetary dependence Open capital markets But floating does allow some monetary independence: Aizenman, Chinn, & Ito (2010, 2011), Di Giovanni & Shambaugh (2008), Klein & Shambaugh (2012, 2015), Obstfeld (2015), Obstfeld, Shambaugh & Taylor (2005), Shambaugh (2004), and Frankel, Schmukler & Servn (2004). 10 Challenge (c): FX intervention is powerless to affect nominal exchange rates unless it is non-sterilized, in which case it is just another kind of monetary policy. These days, G-7 countries dont intervene. But major EMEs do have managed floats. Studies of EMEs tend to show intervention has effects: Fratzscher et al (2016), Adler, Lisack & Mano (2015), Adler & Tovar (2011), Blanchard, Adler, & de Carvalho Filho (2015), Daude, Levy-Yeyati & Nagengast (2014), and Disyatat & Galati (2007). Survey by Menkhoff (2013). 11 Challenge (d): Exchange rate disconnect Claim: The nominal exchange rate has no implications for real economic factors such as the real exchange rate, trade, or output. Empirical studies often fail to find correlations between nominal exchange rates and real fundamentals. E.g., Flood & Rose (1999), Devereux & Engel (2002) and Rose (2011). Many theoretical models say that shocks have the same effect

on the real exchange rate regardless whether the currency floats, in which case the shock appears in the nominal exchange rate, or is fixed, in which case the same shock shows up in price levels instead. E.g., Real Business Cycle models. We will see if we can reject the null hypothesis that the exchange rate regime doesnt matter for the real exchange rate. 12 Three approaches to identifying which countries are systematic managed floaters 1) Regressions to estimate CB reaction function for intervention An advantage of using data from Turkey: Can compare the use of intervention data vs. reserve changes. 2) Frankel-Wei-Xie regression of s against EMP where EMP Exchange Market Pressure (s + Res /MB). An advantage: allows anchor to have whatever reference currency or basket of currencies the data support. 3) Simple-minded correlation (s , Res /MB), where s log (value of currency); Res FX reserves; MB monetary base Advantages: Very easy Dont have to presume anything about direction of causality. An advantage of all 3: Look at both s and Res to figure regime, Not just Var(s). 13 (1) Case study: Turkeys intervention data have been found to support a systematic reaction function Basu and Varoudakis (2013) Also Frmmel and Midili (2016) 14

The two separate measures of intervention look quite different, as expected, though highly correlated. Figure 5: Foreign Exchange Actions by Turkey: Intervention Data vs. Reserve Changes 15 Check that Turkey CB reaction to exchange rate is systematic, whether using intervention data or FX reserves. FX acquisition = c + a (s t strend) + (s t s t-1) + (Res/GDP)t + (inflation target). The dependent variable, FX acquisition, is measured first by FX intervention data and then by changes in FX reserves. Regression to estimate reaction function of Turkeys central bank t-statistics are reported ______Measure of FX Reserve Accumulation____ Independent Variable ________Intervention_______ __ Reserves_ s t strend s t s t-1 Reserves/ GDP constant Table 4.3. 3.6 *** 2.3 ** 4.3 *** 2.7 ** -2.9 *** 3.4 *** 2.7 *** 1.6 -2.6 *** 3.1 *** 1.8* 4.5 *** 1.2 -0.8 133 monthly observations: 2003m1-2014m1

t-statistic significant at: *** 1% level ** 5 % level * 10 % level 16 (2) Technique to estimate flexibility parameter and currency weights at the same time from Frankel & Wei (1994, 2008, 09) and Frankel & Xie (2011): log Ht = c + logXj,t ) + EMPt + ut (2) where H value of the home currency (measured in SDR); Xj value of the $, , yen, RMB, or other foreign currencies j that are candidates for components of the basket, basket weights to be estimated; EMP t Exchange Market Pressure log Ht + (Res)/MB t , and flexibility coefficient to be estimated. If =1 => pure float; =0 & high R2 => fixed exchange rate; 0<<1 & high R2 => systematic managed float. 17 India shows systematic managed float in sub-periods log Ht = c + logXj,t ) + EMPt + ut where EMP t log Ht + (Res)/MB t Table 3. Identifying Break Points in India's Exchange Rate Regime (M1:2000-M5:2009) VARIABLES US dollar Euro Jpn yen EMP Observations R2 Br. Pound (1)

1/14/200010/27/2000 (2) (3) 11/3/2000- 6/24/20016/17/2001 12/31/2001 (4) 1/14/20029/23/2003 (5) 9/30/20032/25/2007 (6) 3/4/20075/6/2009 0.77*** (0.06) 0.12*** (0.03) 0.09*** (0.02) 0.44*** (0.06) 0.92*** (0.04) 0.10*** (0.03) 0.04* (0.02) 0.04 (0.04) 0.66*** (0.08) 0.23*** (0.07) 0.05 (0.05) 0.46*** (0.10) 0.91*** (0.04) 0.03

(0.03) 0.03 (0.02) 0.06 (0.04) 0.72*** (0.06) 0.06 (0.05) 0.24*** (0.06) 0.15*** (0.05) 0.59*** (0.10) 0.32*** (0.07) 0.02 (0.07) 0.37*** (0.07) 42 0.98 0.02 32 0.98 -0.06 28 0.98 0.06 88 0.98 0.03 172 0.86 -0.01 109

0.78 0.08 18 *** p<0.01, ** p<0.05, * p<0.1 (Robust s.e.s in parentheses.) All data are weekly. Thailand shows systematic managed float throughout. Table 2. Identifying Break Points in Thailands Exchange Rate Regime (M1:1999-M5:2009) VARIABLES US dollar Euro Jpn yen EMP Constant Observations R2 Br. Pound (1) (2) (3) (4) 1/21/1999-8/5/2001 8/12/2001-9/9/2006 9/16/2006-3/25/2007 4/1/2007-5/6/2009 0.62*** (0.09) 0.26*** (0.08) 0.15*** (0.04) 0.20*** (0.05) -0.00**

(0.00) 0.61*** (0.04) 0.17*** (0.06) 0.25*** (0.03) 0.06*** (0.02) 0.00 (0.00) 0.80*** (0.28) -0.08 (0.59) 0.16 (0.30) 0.50*** (0.17) -0.01 (0.00) 0.70*** (0.05) 0.19*** (0.04) 0.04 (0.03) 0.03** (0.01) -0.00 (0.00) 129 0.66 -0.02 257 0.76 -0.04 27 0.64

0.12 108 0.90 0.07 *** p<0.01, ** p<0.05, * p<0.1 (Robust s.e.s in parentheses.) All data are weekly. 19 (3) Simple-minded Correlation (s , Res /MB), A truly fixed exchange rate => Correlation = 0, because the exchange rate by definition never changes. A pure float => again, Correlation = 0, because reserves by definition never change. Haphazard interveners should also show low correlation. Only systematic managed floaters show high correlations. We arbitrarily set the threshold at > 0.25. In the hypothetical case of a perfectly systematic managed float, correlation = 1 and the relationship is proportionate: where was the coefficient on EMP in the Frankel-Wei-Xie regressions. 20 Table 1: Simple-minded correlation between s and ( Res)/MB. (Jan.1997 - Dec.2015) Asia/Pac. commodityexporters Australia Bahrain Brunei Indonesia ) Kazakhstan Kuwait Mongolia New Zealand PNG Qatar Saudi A. UAE

0.176 0 0.045 -0.006 0.151 -0.103 0.189 0.220 0.241 0 -0.032 0.044 Other Asian economies Hong Kong 0.045 India 0.445 Korea, Rep. 0.553 Malaysia 0.269 Philippines 0.302 Singapore 0.607 Thailand 0.264 Turkey 0.295 Vietnam 0.114 Corr. > 0.25: Systematic managed floaters Other commodity exporters Brazil Canada Chile Colombia Peru

Russia South Africa 0.288 0.102 0.101 0.210 0.276 0.264 0.274 Corr. < 0.25: firm fixers, & free floaters, & miscellaneous. s log of the exchange rate defined as the $ price of the domestic currency. 21 The final exercise: Does the regime choice matter? Does it make a difference for the real exchange rate? Null hypothesis: Shocks produce the same real exchange rate regardless: They show up in nominal exchange rate under floating, in price level if exchange rate is fixed. Alternative hypothesis: A positive external shock will lead to real appreciation, under floating; the same under systematic managed floating, though smaller; no real appreciation, if nominal exchange rate is fixed. Our econometric tests use two external shock measures For emerging markets: the VIX; For commodity exporters: a country-specific index of global prices for the basket of oil, minerals, and agricultural products it exports. 22 Effects of Shocks on Real Exchange Rates Adverse shock => real depreciation, for all 7 systematic managed floaters; but not for the firm fixer, Hong Kong. A: Effect of VIX Shocks on Real Exchange Rates among Asia Non-Commodity-Exporters (1) (2)

VARIABLES H Kong India Log of VIX 0.002 -0.006* -0.047*** -0.009* -0.011*** -0.005*** -0.011*** -0.019*** (0.004) (0.003) (0.009) (0.005) (0.003) (0.002) (0.003) (0.006) 0.996*** 0.997***

0.970*** 0.955*** REER Lag Constant Observatns R2 0.993*** 0.987*** (3) (4) (5) (6) (7) Korea, R Malaysia Philippines Singapore Thailand 0.874*** 0.935*** (8) Turkey (0.008) (0.012) (0.027) (0.028) (0.007) (0.005) (0.024) (0.016)

0.027 0.080 0.703*** 0.326** 0.053 0.028 0.171 0.254*** (0.035) (0.056) (0.141) (0.126) (0.033) (0.026) (0.112) (0.077) 227 227 227 227 227 227

227 227 0.990 0.968 0.928 0.904 0.986 0.992 0.954 0.956 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 23 A majority of firm-fixers show no effect on the RER, including oil-exporters: P: Effect of Commodity Shocks on RERs among Firm-fixing Oil-Exporters (13) VARIABLES Commodity Price Indices Bahrain -0.002 (0.004) REER Lag Constant Observatns R2 0.979*** (14)

(17) (18) (20) (9) Brunei 0.004*** Kuwait 0.003* Qatar 0.002 Saudi A. 0.004** UAE -0.030 (0.001) (0.002) (0.003) (0.002) (0.020) 0.980*** 0.996*** 1.001*** 1.015*** 0.942*** (0.021)

(0.008) (0.010) (0.013) (0.010) (0.049) 0.100 0.094** 0.022 -0.003 -0.069 0.273 (0.095) (0.039) (0.049) (0.059) (0.048) (0.233) 227 227 156 227 107

0.982 0.976 0.978 0.980 0.936 227 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 24 Brunei is an exception: a highly significant effect, perhaps because its hard peg is to Singapore. All four floaters show significant RER effects of commodity prices. C: Effect of Commodity Shocks on RERs among Asia/Pacific Commodity-Exporters (1) (2) (10) (11) (16) (21) Papua NG Kazakhstan Mongolia VARIABLES Australia Commodity Price Indices 0.038***

0.086** 0.091*** 0.025*** 0.014*** 0.044*** (0.015) (0.042) (0.033) (0.006) (0.005) (0.015) REER Lag 0.944*** 0.955*** 0.890*** 0.963*** 0.958*** 0.946*** (0.019) (0.022) (0.041) (0.013)

(0.018) (0.025) 0.269*** 0.244** 0.535*** 0.187*** 0.198** 0.264** (0.092) (0.114) (0.197) (0.062) (0.084) (0.118) 226 226 227 227 227 227 0.983 0.975 0.908

0.973 0. 965 0.968 Constant Observations R2 New Zeald Indonesia Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 25 Among other commodity exporters, Commodity shocks have no significant RER effect in the firm-fixer (Ecuador) but do in most of the managed floaters. B: Effect of Commodity Shocks on RERs among Non-Asia Commodity-Exporters (4) VARIABLES Brazil Commodity 0.144*** Price Indices (0.052) REER Lag Constant 0.952*** (3) (6) (7) S. Africa Colombia Ecuador (8) (5)

(15) (19) Peru Chile Canada Russia 0.000 0.011 0.010 0.008** 0.012* (0.010) (0.008) (0.010) (0.004) (0.006) 0.970*** 0.981*** 0.965*** 0.970*** 0.960*** 0.013*** 0.033** (0.004) (0.016) 0.939*** 0.926***

(0.017) (0.021) (0.016) (0.036) (0.013) (0.014) (0.019) (0.028) 0.229*** 0.138 0.091 0.170 0.138** 0.170*** 0.279*** 0.349*** (0.079) (0.095) (0.077) (0.170) (0.059) (0.064)

(0.086) (0.130) Observatns 227 227 227 227 227 227 227 227 R2 0.973 0.928 0.963 0.935 0.965 0.949 0.984 0.974 Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 South Africa is an exception. But it shows a positive effect in IV regressions on the BoP. 26

Summary of conclusions The paper offers the idea of a systematic managed float, defined as systematic intervention as a proportion of total Exchange Market Pressure: EMP t st + (Res)/MB t identified as countries with Correlation between s and ( Res)/MB > 0.25; supplemented by regression of s against EMP, a technique which allows baskets as anchors, not just $. and by regression of fx intervention against s for Turkey, which allows a check on FX Reserves vs. intervention data. 7 examples of systematic managed floaters in Asia: India, S.Korea, Malaysia, Philippines, Singapore, Thailand & Turkey 4 more among commodity-exporters: Brazil, Peru, Russia & South Africa. 27 The choice of exchange rate regime matters. Null hypothesis: external shocks like the VIX and global commodity prices lead to the same Real Exchange Rate regardless of regime. Alternative hypothesis: External shocks are reflected in the RER for systematic managed floaters, more often than for firm-fixers, and more often for free-floaters than for managed floaters. Note: The paper offers no hypothesis about murky others. Qualifications are needed, including a need for refinement of time series estimation and results that are not uniformly consistent But the findings generally support the alternative hypothesis. 28

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