Reassessment of Exchange Rate Flexibility and Fear of Floating in Asia: How Much Credible Are the Benchmark Floaters? Syed Kumail Abbas Rizvi Christian Bordes Bushra Naqvi
Abstract It has been widely documented that in many exchange rate arrangements, countries despite committing do not allow their exchange rate to float freely- “an epidemic case of fear of floating”. In this paper we revisit the exchange rate regimes in Asia by following the methodology of Calvo and Reinhart (2002). We augment their methodology by developing an Exchange Rate Flexibility Index (ERFI) based on normalized scores of orthogonal principal components. It appears to us that Asian economies are getting relatively flexible but there’s still a lot of pegging particularly with USD in the region. We cannot rule out the existence of fear of floating in Asia but interestingly the so called true floaters which have been used as benchmark, seem to be the victims of this fear too. We also have doubts that IMF methodology of regime’s classification might be intermingling credible inflation targeting regime with fear of floating. Key Words: Exchange rate regime, Flexibility, Fear of Floating, Asia, Pegging JEL Codes: E42, E58, F31; F33, F41
Corresponding Author: Doctoral Student at CES Axe Finance, University of Paris 1 (Panthéon Sorbonne). Email:
[email protected], Tel: 0033625036259
Professor of Monetary Economics and Finance at CES Axe Finance, University of Paris 1 (Panthéon Sorbonne)
Research Associate and Doctoral Student at CES Axe Finance, University of Paris 1 (Panthéon Sorbonne).
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I.
Introduction The exchange rate regime particularly in emerging economies of Asia has always
been widely discussed and thoroughly investigated topic among researchers. Choice to opt one regime and reject the other is none but the integral part of overall macro management and has indeed significant impact on the economic performance of a country in the short, medium and long run and therefore decision to choose either side of exchange rate flexibility spectrum that varies in degree from very rigid to very flexible cannot be made in isolation because each side has its own merits and demerits. During the decade of 90’s pegging was the most favorite exchange rate regime among emerging and developed economies (Ghosh & Ostry, 2009) and its advantages include stability in domestic inflation, anchoring of agents’ expectations to lower foreign inflation, discipline in policy making and escape from time-inconsistency problem. However, it has its own demerits like loss of Independent Monetary Policy, Transmission of External Shocks, Speculative Attacks and Weakened Accountability. (Obstfeld & Rogoff, 1995). In addition to above, according to (Mishkin, 1998) pegging is inherently prone to fullfledged financial crisis triggered by exchange rate crisis when the peg breaks down. In 1997 when the IMF changed its exchange rate regime classification system and started focusing on de facto practices of countries instead of de jure commitments, the concept of Fear of floating emerged as a buzz word. (Calvo & Reinhart, 2002) evaluate the performance of countries against committed floaters and conclude that demise of intermediate regimes is nothing but a myth.
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In this paper our primary concern is to reevaluate the official commitments of Asian economies against their real practices regarding their exchange rate policy and to estimate their de facto degree of flexibility and recent developments in the context of fear of floating. We will also compare our judgments with the de facto classification provided by IMF and discuss any contradictions and probable reasons for them. Basic foundations of this analysis are borrowed from the methodology proposed by Calvo and Reinhart, in which probabilities of confining a percentage change in some variables within a pre defined narrow band play the pivotal role in determining the true degree of exchange rate flexibility for a particular country. We however augment this methodology by applying principal component analysis on the said probabilities to have a summarize placement of each country on orthogonal XY plane. We also present an Exchange Rate Flexibility Index (ERFI) based on the normalized scores of first principal component extracted from the reduced set of probabilities that determines de facto regime’s flexibility. As a robustness measure we also present the estimates of static and recursive Frankel-Wei regressions to identify the degree of pegging in different Asian currencies. Some of the significant findings include: On average, Asian de jure floaters seems as committed in letting their currency to float freely as de facto floating benchmark countries. Average volatility in exchange rate is not statistically significant for both groups and foreign exchange reserves for Asian de jure floaters are more stable showing lesser intervention. Interest rate in Asia is however more volatile but the intention behind rapid adjustment of policy rate is not always the stabilization of exchange rate. On individual level, there are some economies seem clearly deviating from their de jure stance and appear to intervene in foreign exchange market frequently despite their
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commitment to abstain themselves in doing so. But this fear of floating behavior is not limited to Asian economies only and the countries which generally believed to be the true floater both in de jure and de facto terms and are included in our benchmark sample, also look afraid in letting their currency to float. And at last we find some discrepancies between IMF’s judgment and our assessment about Indonesia and Philippines. It appears to us that IMF’s system of classification might be infected by the same shortcoming which Ball and Reyes has found in the Calvo and Reinhart methodology according to which volatility in interest rate is straightforwardly taken as a symptom of fear of floating thus making it difficult to distinguish between credible inflation targeting (IT) regime and fear of floating. The remainder of this paper is as follow. Section II provides a brief review of the alternative exchange rate regimes including fear of floating. It also provides brief summary of current exchange rate regimes prevailing in Asian economies both in de jure and de facto terms. Section III provides analytical framework and organization of data. Section IV presents the results and relative evaluation of benchmark and candidate countries. Static and dynamic estimates of Frankel-Wei regressions are also provided in section IV.G. Section V and VI covers the construction methodology and results of exchange rate flexibility index (ERFI) respectively and section VII concludes.
II.
Exchange Rate Regimes: Classification and Practices A.
DE JURE AND DE FACTO EXCHANGE RATE REGIMES
The exchange rate regime particularly in emerging economies of Asia has always been widely discussed and thoroughly researched topic among researchers. Choice to
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opt one regime and reject the other is none but the integral part of overall macro management and has indeed significant impact on the economic performance of a country in the short, medium and long run. After the collapse of Bretton Wood System in 1970’s, it was entirely an indigenous choice for the countries to opt any exchange rate regime subject to its compatibility with the economic needs and the prevailing policies at that time period, as well as with the intended economic policies to achieve medium and long term economic goals (IMF, 1997). During those periods countries were required to declare their official or “De Jure” stance and the data was compiled by IMF on regular basis. However, in 1998 when several researches highlighted the significant differences between official “De Jure” policies and the real time “De Facto” practices of the countries while employing their exchange rate management, the IMF changed its classification system radically. According to the IMF, “The new classification system is based on the member’s actual, de facto arrangements as identified by IMF staff, which may differ from their official announced arrangements.”1 To understand why it was necessary to differentiate de facto from de jure classification, we first need to have a very brief idea of exchange rate flexibility spectrum which varies in degree from very rigid to very flexible and its brief summary can be seen in Figure 1 below.
De Facto Classification of Exchange Rate Regimes and Monetary Policy Frameworks, Date as of April 31, 2008 http://www.imf.org/external/np/mfd/er/2008/eng/0408.htm 1
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Figure 1: Exchange Rate Flexibility Spectrum
Flexible Regimes • Managed Float • Independent Float
Intermediate Regimes • Horizontal Bands • Crawling Pegs • Crawling Bands
Fixed Rate Regimes • Currency Unions • Dollarized Regimes • Currency Boards • Conventional Fixed Pegs
B.
FIXED VS. FLEXIBLE REGIMES
During the decade of 90’s pegging was the most favorite exchange rate regime among emerging and developed economies (Ghosh & Ostry, 2009). It involves many types including the most popular choice of establishing a straight forward sustainable value link between the currencies of two economies. The strongest point in favor of pegging is that it can bring stability of Inflation in that economy which pegs its currency with some other stable currency, in two ways. At first, by fixing that part of local inflation with the inflation of base country which is coming from traded goods and second by anchoring the expectation of local agents to the inflation of base country(Mishkin, 1998). Pegging also tends to equalize interest rates in both economies depending upon the degree of commitment to make the peg credible which in result could bring discipline in policymaking and helps policymakers in avoiding their pursuit of discretionary policies to achieve short-run objectives or the so called time-
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inconsistency problem described in detail by (Kydland & Prescott, 1977), (Calvo G. , 1978) and (Barro & Gordon, 1983). There is however, a lot of criticism on the policy of exchange rate pegging, including the arguments of loss of Independent Monetary Policy, Transmission of External Shocks, Speculative Attacks and Weakened Accountability. (Obstfeld & Rogoff, 1995). In addition to above, according to (Mishkin, 1998) pegging is inherently prone to full-fledged financial crisis triggered by exchange rate crisis when the peg breaks down. These arguments and the Asian Financial crisis of 1997 first ruled out the choice of pegging in general and the intermediate regimes (soft pegs) in particular, by blaming them as a fundamental reason of crisis and provided a so called “Bipolar” prescription to the world which means either a country should go for hard peg or it should let it currency float freely. However, the Argentina’s peso crisis of 2002 disfigured the very foundations of hard pegging and left the world, especially the emerging and less developed economies, with a serious dilemma of choosing a suitable exchange rate regime and with a no choice but to go for the policy of floating. It was indeed the time when several emerging economies decided to opt floating exchange rate regime despite the fact that they had taken several advantages from pegging in their past. And it was the time when this tradeoff between fixed and floating exchange rate regime has given a birth of what we call today as “Fear of Floating”.
C.
FEAR OF FLOATING
In their seminal paper (Calvo & Reinhart, 2002) introduced the concept of Fear of Floating as a situation where the countries declare themselves having a “Floating/Managed Floating” exchange rate regime on a de jure basis, but in real practice
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either by using foreign exchange reserves or domestic interest rate as an intervention tool, they continuously resist in letting their currencies to float freely. Thus on de facto basis their policies are relatively closer to the hard side of exchange rate regime spectrum; and the stability in their exchange rates which they are able to achieve by practicing such policies, is a characteristic that could be retraced more often in pegged regimes instead of floating. It can be argued however that this contradiction in their verbal and practical stance is not totally irrational and there are indeed some strong reasons to defend exchange rate and not letting it float freely by the countries regardless of the official announcements to do so, particularly in the context of emerging or less developed economies. For example flexibility brings with itself volatility which is often found excessive in the last few decades and unfortunately most of the time this short term excessive volatility is the result of speculative attacks rather than because of some changes in underlying economic fundamentals.(Bird & Rajan, 2001a) and (Bird & Rajan, 2001b). Another important reason to exhibit fear of floating is a lack of well developed and diversified financial system in emerging economies and the resultant phenomenon of “Liability Dollarization” which is sometimes coined by a term “Original Sin” put forwarded by (Eichengree & Hausmann, 1999). By emphasizing on domestic dollarized liabilities of a large cross –country sample, (Honig, 2005) and by focusing on the case of foreign dollar based liabilities for a small open economy (Nguyen, 2010) highlight this phenomenon as the primary reason and an acceptable justification to exhibit the fear of floating behavior. Fear of distortion in domestic prices and production due to the high exchange rate pass through into import prices is an additional widely cited reason of fear of floating.
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Nevertheless the concept of fear of floating is debatable itself as many economists (Ghosh, Gulde, Ostry, & Wolf, 1997) provide evidences where several countries claiming to have a peg actually allow frequent and substantial adjustments in their exchange which is contrary to the findings of Calvo and Reinhart cited previously. By following the methodology of Calvo and Reinhart, (Ball & Reyes, 2004), (Ball & Reyes, 2008), and (Nogueira, 2009) emphasize the need to differentiate Inflation Targeting framework with Fear of Floating and provide evidences that a significant part of the variations in interest rates can be attributed to the former as opposed to latter.
D.
EXCHANGE RATE REGIMES IN ASIA
Before the Asian Financial Crisis of 1997, the currencies of many East Asian economies like Hong Kong, Indonesia, Korea, Malaysia, Philippines, Singapore, Taiwan and Thailand were pegged with the US Dollar and this joint strategy of pegging was one of the major factors of the region’s stability at that time period (McKinnon & Schnabl, 2004). However if we look at the recent position of exchange rate regimes in countries included in our data set, we can observe that majority of these are unable to resist themselves against the popular view of “Go Flexible”, at least on de jure basis. The Table 1 is based on the information gathered from the official websites and documents of individual countries as well as from the compiled work of (Cavoli & Rajan, 2009).
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Table 1: Regime’s Classification in Asia Country China
De Jure Regime Managed Floating
Official Stance and Authors’ Comment Announced on July 21, 2005 the Adoption of a managed floating exchange rate regime based on market supply and demand with reference to a basket of currencies. Linked to US dollar (HK$ 7.8 = 1 US$) and maintained it by the operations of Currency Board System.
Hong Kong
Pegged
Indonesia
Floating
The exchange rate is determined wholly by market forces. However, Bank Indonesia is able to take some actions to keep the exchange rate from undergoing excessive fluctuation.
India
Managed Floating
Under the broad principles of careful monitoring and management of exchange rates with flexibility, without a fixed target or a pre announced target or a band.
Korea
Floating
Malaysia
Managed Floating
The exchange rate is, in principal, decided by the interaction of market forces. However, the Bank of Korea implements smoothing operations to deal with abrupt swings in the exchange rate. On July 21, 2005, Malaysia adopted a managed float against a basket of currencies.
Philippines
Floating
Pakistan
Floating
The Bangko Sentral ng Pilipinas (BSP) maintains a floating exchange rate system. Exchange rates are determined on the basis of supply and demand in the foreign exchange market. The role of the BSP in the foreign exchange market is principally to ensure orderly conditions in the market. The market-determination of the exchange rate is consistent with the Government’s commitment to market-oriented reforms and outwardlooking strategies of achieving competitiveness through price stability and efficiency.2 Pakistan has adopted the floating inter-bank exchange rate as the preferred option since 2001. SBP has attempted to maintain real effective exchange rate at a level that keeps the competitiveness of Pakistani exports intact. But, like other Central Banks, it does intervene from time to time to keep stability in the market and smooth excessive fluctuations (Husain, 2005). However in 2005 answering a question the Governor State Bank gave some policy stance that casted doubts on the true flexibility of country’s exchange rate regime
Taken from the official website of Bangko Sentral Ng Philipinas http://www.bsp.gov.ph/financial/forex.asp 2
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De Facto Regime Crawling peg
Currency board arrangement Managed floating with no predetermined path for the exchange rate Managed floating with no predetermined path for the exchange rate Continue Independently floating
Managed floating with no predetermined path for the exchange rate Independently floating
Managed floating with no predetermined path for the exchange rate
Singapore
Managed Floating
Thailand
Managed Floating
and were conflicting with the usual practice of free floating.3 Monetary aggregate targeting where exchange rate is managed against a basket of currencies with undisclosed weights, is more like a “Basket, Band and Crawl or BBC managed float”4
In July 2, 1997, Thailand adopted the managed float regime where exchange rate is primarily determined by market forces and the Bank of Thailand could intervene only when necessary to prevent excessive volatilities.
Managed floating with no predetermined path for the exchange rate Managed floating with no predetermined path for the exchange rate
III. Fear of Floating: Reassessment A.
INTERVENTION IN FOREIGN EXCHANGE MARKET THROUGH
RESERVES AND INTEREST RATE Change in foreign exchange reserves may not necessarily be the result of intervention in foreign exchange market and there could be several other reasons for foreign exchange reserves to change. Factors like change in the valuation of currency in which reserves are held, changes in interest or coupon payments earned on different assets and the change in the value of underlying asset itself could bring change in the value of foreign exchange reserves of a country. It is also quite possible that changes in reserves occur purely due to the transactional purposes like purchase of foreign asset or foreign debt servicing (Neely, 2000). Despite these short coming and flaws, changes in foreign exchange reserves has long been used as a proxy of govt. intervention in foreign exchange market carried on to manipulate prevailing exchange rate. [See: (Gartner, 1987), (Kearney & MacDonald, 1986), (Obstfeld, 1983) and (Taylor, 1982)]. It is not 3 4
http://www.map.org.pk/review/0205/ih_interview.htm http://www.singapore-window.org/sw05/050723ft.htm
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much difficult to understand that this change in foreign exchange reserves, however, occur not in isolation and it could affect two other dimensions of country’s financial structure which are monetary base and interest rates. Consequently the final impact on exchange rate is the combined effect of not only these domestic changes but also of the change in foreign interest rate relative to domestic. We are not going to investigate in this paper the direction and causality among these variables and are focusing merely on the fact that stabilization of exchange rate is usually be achieved at the cost of variability in some or all of these factors. Certainly one can argue that in case of sterilized intervention central bank can prevent the change in interest rates and base money and limit the impact of its intervention operations to only foreign exchange reserves. However it has been investigated that at times when domestic currency is under pressure and is expected to depreciate, this sterilized intervention is not an appropriate strategy as it restrains domestic interest rate to increase, which does not actually relieve the devaluation pressure on domestic currency. The ultimate result of sterilized intervention is nothing but just a loss of foreign exchange reserves at the time when domestic currency is under pressure. Thus we believe that non sterilized intervention is the only rational solution to defend the currency parity because it will allow domestic interest rates to increase which in result increase the total expected return of domestic assets relative to foreign assets and relieved the domestic currency from the devaluation pressure. In the light of above discussion and by following the methodology of Calvo and Reinhart we will first investigate the probability of confining a percentage change within a pre-specified narrow band, for different variables. As explained by Calvo and Reinhart, this probability is an increasing function of rigidity for exchange rate and decreasing for reserves, base money and domestic interest rate.
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Basic structure of the methodology can be explained with the help of following equations:
Equation 1 Equation 2 Equation 3 Equation 4
Left hand side of each equation represents a probability of staying a change within a pre-defined narrow interval for different variables given that country is observing fixed or pegged regime. Right hand side is the same probability for floating regimes. LB and UB are lower and upper bound for the narrow interval which is set as 2.5% for Equation 1,Equation 2 and Equation 3 and 50 basis points (0.5%) for Equation 4.
,
, and
are percentage change in Exchange rate, Reserves and Base Money
respectively, calculated as difference in the natural logarithms of concerned series 1 1
Equation 5
And
is the change in interest rate calculated as
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.
Figure 2: Hypothetical Distribution to calculate probability of % Change Z-Scores of Variable X .4
Density
.3
.2
Area Under The Curv e =
.1
Probability of Confining the % Change of v ariable w ithin a Narrow Band
.0 -3
-2
-1
0
1
2
3
4
Figure 2 above explains how to calculate the probability discussed in Equation 1 to Equation 4. Calvo and Reinhart suggested using absolute value of percentage change while calculating such probability. However, we use series of absolute percentage change only in the calculation of mean; and derive the standard deviation from the actual series of percentage change. We call this set of probabilities as S3 and prefer it because it provides the most conservative estimates.5
B.
CONDITIONS FOR FEAR OF FLOATING
Signs in Equation 1 to Equation 4 are based upon the classical expectations about different regimes. The Equation 1 tells us that probability of confining a percentage change in exchange rate within a narrow band (probability of stability) should be higher
Probabilities calculated entirely on the basis of absolute percentage change series are called S2 and we confirmed that our results are robust to it as well. S2 probabilities are little larger in value than S3, however, it does not have any significant impact either on country ranking or on interpretation of results. Appendix Table IV, Appendix Figure B and Appendix Figure C provide results based on S2 set of probabilities for comparison purpose. 5
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in pegged regimes than floating regimes where exchange rate is expected to be more volatile. In the same way Reserves, Monetary Base and interest rate (Equation 2 to Equation 4) are expected to be more stable in floating regimes because of their commitment to not intervene in the market, thus the probability of staying percentage change within a narrow band should be high for them. We would like to remind that the probability calculated is actually depicting the stability in variable. Higher the probability, higher would be the stability and lesser would be the volatility. It is indeed a fact that only a hypothetical country can be a true floater with all its theoretical requirements and at least in our knowledge there exists no country which absolutely follows the free floating regime. Nonetheless to assess the degree of commitment towards floating in Asia we need to have some benchmark countries to compare their relative degree of flexibility. We develop only a “Floating” benchmark consist of five economies which have been categorized as de facto floaters by the latest classification provided by IMF6. To determine the degree of fear of floating we primarily focus on de jure floating economies of Asia in comparison with those which have been included in our de facto floating benchmark. Fear of floating would be present if following conditions hold:
Country I is De jure floater and
and
and or
6
http://www.imf.org/external/np/mfd/er/2008/eng/0408.htm
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and or
C.
ORGANIZATION OF DATA
Primarily our data set is composed of ten Asian economies; China, Hong Kong, Indonesia, India, South Korea (refer as Korea), Malaysia, Philippines, Pakistan, Singapore and Thailand categorized broadly as “Candidate” countries. We further include five economies in our data set categorized as “Benchmark” countries which are Canada, European Union, Japan, United Kingdom and United States. All benchmark economies are de jure as well as de facto floaters and the purpose of their inclusion is to provide statistics for relative evaluation. For analysis purpose we classify each country according to its De Jure and De Facto regimes provided in Table 1, however, instead of having several categories we reduce each regime in only three broad categories i.e. Floating, Managed Float and Pegged. For e.g. Crawling Peg and Currency Board arrangements both have been given the title of “Pegged”. We used the terms floating, free floating and independently floating as same and interchangeably.
IV.
Analysis and Results A.
EXCHANGE RATES
Though our benchmark group consist of five economies, Canada, European Union, Japan, United Kingdom and Unites States however only while analyzing exchange rates we have total of seven components in our benchmark panel because of the inclusion of three different exchange rates of US$ named as USA1, USA2 and USA3 against EUR, GBP
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and JPY. Figure 3 below is showing these exchange rates and their percentage change measured on right and left axes respectively. Figure 3: Exchange Rates for US Dollar USD Exchange Rates and % Change 2.4 2.0 1.6 1.2
.10
0.8 .05 0.4
Exchange Rate USD/Euro Exchange Rate USD/Pound Exchange Rate USD/Yen100 Percentage Change USD/Euro Percentage Change USD/Pound Percentage Change USD/Yen
.00 -.05 -.10 01
02
03
04
05
06
07
08
09
Average probability of confining the percentage change in exchange rates within the narrow band of 2.5% (Equation 1) is 0.58 for benchmark panel in which all regimes are floating. Under candidate countries panel this average probability is 0.65 for Floating, 0.81 for Managed float and 1.00 for Pegged regimes. (See Figure 4 and APPENDIX
Appendix Table I)
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Figure 4: Probability that Exchange rate stays within the Narrow band of +/-2.5% Exchange Rate Probability of % Change w ithin the 2.5 Percent Band Candidate
Benchmark 1.0
1.0
0.90
1.00
0.8
0.8
0.66
0.6
0.58
0.6
0.2
0.2
0.0
0.0
Benchmark
Candidate
1.0
1.0
1.00
1.00
0.98 0.93 0.89
0.8 0.62 0.56
0.57
0.58
0.58
0.55
0.57
0.76
0.6
0.54 0.43
0.0
0.0
SA U
SA U
SA U
3
0.2
2
0.2
1
0.4
C Eu an ro ad pe a an U ni on Ja pa n
0.4
C H hin on gK a on g I In ndi do a ne si a Ko M rea al ay Pa sia ki Ph st ilip an p Si ine ng s ap Th ore ai la nd
0.6
0.83
0.82
0.8
U K
Floating Managed Float Pegged
Fl oa M an tin ag g ed Fl oa t Pe gg ed
0.4
Fl oa M an tin ag g ed Fl oa t Pe gg ed
0.4
This increasing order of average probabilities (upper panel) is fully consistent with the theory described above that exchange rate stability is an increasing function of regime’s rigidity. To have an idea whether the exchange rate stability in Asian de jure floaters is different from true de facto floaters in benchmark panel, we perform test of equality of mean (Appendix Table II) between group averages and find that difference of these two probabilities (0.58 and 0.66) is statistically insignificant and zero leading us to the acceptance of null hypothesis that average volatility in exchange rates of benchmark de facto floaters and candidate de jure floaters is same. If we look at individual countries we find that probability for both Indonesia and Korea is lower than any of their floating counterpart in benchmark group which indicates the high degree of commitment towards floating regime in these two countries. However the rest of the two de jure
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floaters (Pakistan and Philippines) in candidate group with significantly higher probability seem less committed towards flexible exchange rate regime despite their official commitments and announcements to do so. Among managed floaters China’s and Malaysia’s highly stable exchange rate with a probability of 1.00 and 0.98 seems to be an indication of pegged regime instead of managed float which they claim to follow.
1.
Reserves
Reserves stability has to be a decreasing function of exchange rate regime’s rigidity. This means that we should expect more volatile reserves in pegged regimes and less volatile in floating. Thus according to Equation 2 the expected probabilities that percentage change in reserves falls within the narrow band of 2.5% (stability) must be highest for floating and lowest for fixed regimes while somewhere in middle for managed floats and can be written as follows
Equation 6
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Figure 5: Probability that Foreign Exchange Reserves stays within the Narrow band of +/2.5% Fore ign Exchange Re s erve s Probability of % Change w ithin the 2.5 Perce nt Band Benchmark
Candidate
1.0
1.0
0.8
0.8
0.6
0.6
0.66 0.52 0.45
0.44
0.2
0.2
0.0
0.0
Fl
oa tin ag g ed Fl oa t Pe gg ed
0.4
Fl oa tin ag g ed Fl oa t Pe gg ed
0.4
Floating Managed Float Pegged
an
Candidate
M
M
an
Benchmark
1.0
1.0
0.8
0.8 0.68
0.68
0.66
0.6
0.64
0.6
0.55 0.51
0.49
0.48
0.45
0.4
0.44
0.45
0.46
0.4
0.33 0.28
0.15
Eu
ro
H
pe
on
C hi n gK a on g I In ndi do a ne sia Ko M rea al ay Pa sia kis Ph t ilip an p Si ine ng s ap Th ore ai la nd
SA 3 U
SA 2 U
U
U
Ja pa
a ad
U
an
an
C
K
0.0 SA 1
0.0 n
0.2
ni on
0.2
In contrast to the expectations mentioned in Equation 6 the actual pattern in our sample of Asian economies is entirely opposite on group average basis. From Figure 5 it is evident that the said probability is 0.66 highest for dejure pegged regime (only Korea), 0.44 lowest for floating and 0.52 (middle of the two) for managed float regimes on average basis. This is exactly opposite to what we have shown in Equation 6 above: .
. .
B.
CREDIBILITY OF BENCHMARK FLOATERS
This finding about reserves seems time invariant and is indeed consistent with the findings of Calvo and Reinhart, who also observed that reserves variability is highest
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for floaters and least for the limited flexibility arrangements in their larger sample. However, there are some interesting implications for this observed trend. On one hand this reverse pattern in probabilities does not only highlights the persistence in fear of floating by indicating the high volatility of reserves in floating regimes which is obviously in contradiction with their expected behavior. It also point outs that degree of “fear of floating” is directly proportional to the degree of flexibility committed in de jure stance. Put it in other way, the higher the degree of flexibility committed by an economy in its official policy, the more fearful behavior they show on average basis. However the second flip of the coin is also very interesting. Subject to the authentication of above inferences, the picture becomes little complicated when we compare these statistics against the benchmark de facto floating economies where this average probability for reserves is 0.45, almost equal to what we observe for the “so called fear of floating economies”. This finding raises some very serious questions about the credibility of benchmark countries which are widely recognized as true floaters both in de jure and de facto terms. Given this scenario we have to admit that our benchmark “de facto” floating economies are also exhibiting the high degree of reserve intervention thus suspecting them of fear of floating and making their selection as a benchmark questionable at least while investigating the fear of floating itself. It also raises questions about the IMF’s criteria that declared them as “de facto” floater despite showing such a high volatility in reserves. If there is any justification for such high volatility in the reserves of these de facto floaters, the same should have been applied on the emerging economies too before convicting them guilty for fear of floating behavior. Strong differences within the benchmark and candidate groups are also important to consider for e.g. in benchmark group Japan’s reserves are significantly stable compared to other countries and if we exclude Japan from our benchmark panel, the average comes down to 0.39 from 0.45. All
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countries in the candidate panel have probabilities higher than this new benchmark average of 0.39 except Pakistan where only 15 percent of the time, the percentage change in reserves is confined to 2.5% narrow band. So regardless of the heterogeneity in their exchange rate regimes, it seems that except Pakistan, all countries in candidate panel (floating, managed float and pegged) have reserves showing more stability than the average of benchmark de facto floating economies (excluding Japan).
C.
NOMINAL INTEREST RATE:
Use of foreign exchange reserves as a tool to stabilize exchange rate is not the only option available to monetary authorities. There is another measure which is frequently used by emerging economies to intervene in foreign exchange market i.e. policy rate. It has been argued that the cause of pressure on exchange rate is actually generated by the differential returns between domestic and foreign asset explained in uncovered interest rate parity. If domestic rate of return is lower than foreign return, exchange rate has to be increased to the extent that the total return on domestic asset and currency equalizes the return on foreign asset. However this adjustment means depreciation in currency which is not a desirable phenomenon in many emerging economies despite of their commitment towards flexibility of exchange rate. One way to counter that pressure on currency is to intervene through monetary policy by increasing/decreasing interest rate to strengthen/weaken domestic currency in response to upward/downward pressure on exchange rate (Flood & Jeanne, 2005). For sake of argument one can quote the examples of US and Japan where change in interest rate primarily influenced by the domestic policy objectives and has nothing to do with intervention to exchange rate. Nonetheless, this cannot be argued in same spirit for the emerging economies where it is very difficult to justify the high volatility in interest
P a g e | 22
rates solely on the basis of change in domestic policy objectives and fundamentals (Calvo & Reinhart, 2002). We calculate the probability of falling interest rate differential within the plus minus 0.5% or 50 basis points band for our sample countries (Figure 6). The average probability of falling interest rate differential within this narrow band is 0.88 for benchmark de facto floating economies. This average would be even higher (0.97) if we exclude UK from our benchmark sample whose interest rate distribution is much more volatile7 in general compared to its peers. In candidate panel, average probability for de jure floating, managed float and pegged regime is 0.56, 0.81 and 0.52 respectively, much lower particularly for floating regime than what we expect it to be relative to the average of benchmark. This lower probability in de jure floating regimes obviously strengthen the argument of fear of floating however before concluding anything we need to look little deeper at individual country level to have a better understanding of what’s happening where. For at least two out of four de jure floaters in a candidate panel (Korea and Philippines), the interest rate differential seems to be a policy rule with a very low volatility. Probability of falling interest rate differential within 50 basis points is 0.99 for Korea and 0.85 for Philippines, well above or almost equal to the benchmark average of 0.88 and we can easily point out that the remaining two floaters, Indonesia and Pakistan with a probability of 0.15 and 0.19 respectively are the primary reason of bringing down the average of floating group to 0.56. Will this high variability in interest rate enough to prove high degree of intervention in these two economies? At least one argument can be made to justify this variability which is a fact that Indonesia and Pakistan are the countries with the highest chances (95% and 87% of the times
7
Only 52% chance that interest rate differential falls within the plus minus 50 basis points.
P a g e | 23
respectively)8 of having monthly inflation consistently above 2.5 percent. However in case of Philippine, another floater with 86% chance of having monthly inflation above 2.5 percent, this argument doesn’t seem to work as its interest rate differentials’ distribution is 5.7 and 4.8 times less volatile than Indonesia and Pakistan. We will discuss in details some other arguments for these discrepancies while analyzing individual countries. Figure 6: Probability that Nominal interest rate stays within the Narrow band of +/-0.5% Nominal Interest Rate Probability of Change within the 0.5% or 50 Basis Points Benchmark
Candidate
1.0
1.0 0.88
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.96
1.0
0.94 0.98
0.52
M
M
1.0
0.55
Candidate
Benchmark
Floating Managed Float Pegged
0.81
Fl oa an tin ag g ed Fl oa t Pe gg ed
0.8
Fl oa an tin ag g ed Fl oa t Pe gg ed
0.8
0.99
1.00 0.93
1.00
0.96
0.89 0.85
0.8
0.8 0.6
0.6
0.52
0.52
0.4
0.4
0.2
0.2
0.0
0.0 C h on ina gK on g I In ndi do a ne sia Ko r M ea al ay Pa sia k Ph ist ilip an p Si ine ng s ap Th ore ai la nd H
SA 3 U
SA 2 U
K U
SA 1 U
0.19 0.15
Eu r
C an ad op a ea n U ni on Ja pa n
0.25
8See
APPENDIX
Appendix Table I
P a g e | 24
D.
MONETARY BASE
We have already explained earlier that non sterilized interventions are more effective in stabilizing exchange rate however, irrespective of its effectiveness most economies choose to opt sterilized intervention as a policy stance to minimize the impact of intervention on monetary base and subsequently on interest rate. We can easily speculate on the existence of sterilization in countries with highly stabilized exchange rate and volatile reserves if their monetary base is relatively stable. Because of the high variability in monetary base for all countries irrespective of the fact that they are in benchmark or candidate panel, it seems to us that they all need to alter their monetary base frequently at medium to large extent (Figure 7). However if we compare volatility of monetary base with the volatility of M19, we see that at least for China, Hong Kong and Pakistan M1 volatility is much higher than monetary base which could be an indication of sterilization through open market operations to stabilize monetary base but which eventually cause M1 to become more volatile. A significantly higher volatility in M1 relative to monetary base for China and Hong Kong could also be an indication of difference in the elasticity of various sterilization tools and weak relationship between China’s base money and monetary aggregates (Xie, 2004). For rest of the countries, monetary aggregates (M1 and M2) are more stable than monetary base indicating their heavy reliance on unconventional and novel tools of sterilization. For e.g. it has been briefly documented by many researchers that Thailand is following the policy of fiscal adjustment to make its sterilization operations more effective, Korea has accelerated its trade liberalization practices and Indonesia moved to the use of foreign exchange swaps along with their traditional practices (Lee, 1997).
9
Results not reported here however can be made available by author on request.
P a g e | 25
Figure 7: Probability that Base Money stays within the Narrow band of +/-2.5% Monetary Base Probability of % Change w ithin the 2.5 Percent Band Benchmark
Candidate
1.0
1.0
0.8
0.8
0.6
0.6 0.42
0.4
0.4
0.34
0.37
0.27
0.0
0.0
Fl
Fl oa tin ag g ed Fl oa t Pe gg ed
0.2
oa tin ag g ed Fl oa t Pe gg ed
0.2
Candidate
Floating Managed Float Pegged
an
1.0
1.0
0.8
0.8
M
M
an
Benchmark
0.6
0.6 0.50
0.48
0.33
0.45
0.44
0.41
0.4
0.40
0.4
0.37
0.42
0.34
0.30
0.24
0.2
0.17
0.2
0.19
0.21
SA 3
H
U
SA 2 U
K U
SA 1 U
Eu
C an ro ad pe a an U ni on Ja pa n
C h on ina gK on g In In d do ia ne si a Ko r M ea al ay Pa sia k Ph ist ilip an p Si ine ng s ap Th ore ai la nd
0.0
0.0
E.
FEAR OF FLOATING: THE VERDICT!
To finally conclude whether a country is exhibiting fear of floating and letting its de facto exchange rate regime different from its de jure announcement, we need to have an aggregate picture of decision variables. Through Figure 8 we try to provide this aggregate view somehow
P a g e | 26
Figure 8: Summarized View of All Probabilities Including Group Averages CMR_0_5PB_S3 for Benchmark
0.8
0.8
0.8
0.96
0.98
M0_2_5PB_S3 for Benchmark
0.94
1.00
INF_2_5PB_S3 for Benchmark
1.0
1.0
0.8
0.8
0.68 0.58
0.55
0.58
0.57
0.65
0.6
0.6 0.48
0.33
0.6
0.52
0.49
0.4
0.4
1 .0 0 1 .0 0
RES_2_5PB_S3 for Candidate
0 .9 8
0.33
0 .9 3
0 .7 6
0.0
M0_2_5PB_S3 for Candid ate 0 .9 6
INF_2_5PB_S3 for Candidate
1.0
1.0
0 .8 5
0.8
0.8
0 .5 4
0 .6 3 0 .5 5 0 .5 1
0 .4 5
0.4
0.4
0.2
0.2
0.0
0.0
0.8
0 .6 4
0.6
0 .4 3
0.8
0 .6 8
0 .6 6
0.6
0.2
0 .8 9 0 .8 3
0.8
0.30
Ca Eu na ro pe da an Un ion Ja pa n
pa n
UK US A1 US A2 US A3 0 .9 9 1 .0 0
1.0
0 .8 9 0 .8 2
0.46
0.4
0.17
CMR_0_5PB_S3 for Candidate
1.0
0 .9 3
Ja
Ca Eu na ro pe da an Un ion
Ja
Ca Eu na ro pe da an Un ion
Ja
XR_2_5PB_S3 for Candidate 1.0
pa n
0.0
UK US A1 US A2 US A3
0.2
0.0
pa n
0.2
0.0 UK US A1 US A2 US A3
0.2
0.0 Ca Eu na ro pe da an Un ion
0.2
0.44
0.41
0.28
Floating Managed Float Pegged
0.66
0.6 0.50
0.4
Ca Eu na ro pe da an Un ion
0.57
0.56
0.4
UK US A1 US A2 US A3
0.62
0.6
0.98
pa n
1.0
UK US A1 US A2 US A3
RES_2_5PB_S3 for Benchmark 1.0
Ja
XR_2_5PB_S3 for Benchmark 1.0
0 .4 4 0 .4 5
0.6
0.6
0 .5 2
0.6
0 .4 6
0 .4 5
0 .4 4
0.4
0 .4 0
0.4
0 .3 7
0 .4 3
0.4 0 .2 4
0 .1 9
0.2
0 .5 0
0 .4 2
0 .3 4
0 .2 5 0 .1 5
0 .5 3 0 .4 8
0 .4 8
0 .1 5
0 .1 9
0.2
0 .2 3
0 .2 1
0.2
0 .1 3 0 .1 3
0 .1 0
XR_2_5PB_S3 for Benchmark 1.0
RES_2_5PB_S3 for Benchmark 1.0
CMR_0_5PB_S3 for Benchmark 1.0
M0_2_5PB_S3 for Benchmark
Ho Chin ng a Ko ng In India do ne sia Ko r M e ala a Pa ysia Ph kista ilip n Sin pine ga s p Th ore ail an d
0.0 Ho Chin ng a Ko ng In India do ne sia Ko M rea ala Pa ysia Ph kista ilip n Sin pine ga s p Th ore ail an d
0.0 Ho Chin ng a Ko ng In India do ne sia Ko M rea ala Pa ysia Ph kista ilip n Sin pine ga s p Th ore ail an d
Ho Chin ng a Ko ng In India do ne sia Ko M rea ala Pa ysia Ph kista ilip n Sin pine ga s p Th ore ail an d
Ho Chin ng a Ko ng In India do ne sia Ko M rea ala Pa ysia Ph kista ilip n Sin pine ga s p Th ore ail an d
0 .0 4
0.0
INF_2_5PB_S3 for Benchmark
1.0
1.0 0.8
0.88
0.8
0.8
0.8
0.8
0.6
0.6
0.6
0.64
0.6
0.58
0.6
0.45
0.2
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.0
XR_2_5PB_S3 for Candid ate 1.0
0.90
1.00
0.8
RES_2_5PB_S3 for Candidate 1.0
CMR_0_5PB_S3 for Candidate 1.0
0.8
0.81
0.8
0.66
0.4
Flo M ati an ng ag ed Flo a Pe t gg ed
0.34
M0_2_5PB_S3 for Candidate
Flo M ati an ng ag ed Flo a Pe t gg ed
0.4
Flo M ati an ng ag ed Flo a Pe t gg ed
0.4
Flo M ati an ng ag ed Flo a Pe t gg ed
0.4
Flo M ati an ng ag ed Flo a Pe t gg ed
0.4
INF_2_5PB_S3 for Candidate
1.0
1.0
0.8
0.8
0.66
0.6
0.6
0.52
0.6
0.55
0.52
0.6
0.6
0.44
0.4
0.4
0.42
0.4
0.4
0.53 0.43
0.37
0.4
0.27
0.0
0.13
Flo M ati an ng ag ed Flo a Pe t gg ed
0.2
0.0 Flo M ati an ng ag ed Flo a Pe t gg ed
0.2
0.0 Flo M ati an ng ag ed Flo a Pe t gg ed
0.2
0.0 Flo M ati an ng ag ed Flo a Pe t gg ed
0.2
0.0 Flo M ati an ng ag ed Flo a Pe t gg ed
0.2
In this section we focus primarily on four de jure floating economies in candidate panel i.e. Indonesia, Korea, Pakistan and Philippines and discuss each country’s situation across all decision variables.
1.
Korea
Exchange rate in Korea is more volatile than benchmark countries as the probability of staying within the narrow band of
2.5% is only 54% against the
benchmark average of 58 . Individual comparison tells us that Korea’s exchange rate is
P a g e | 27
more volatile than any benchmark country’s exchange rate making it very strong and committed floater on relative basis. Korea possesses the most stable reserves only after Japan (Benchmark floater) and Hong Kong (Candidate Pegged) with 64% chance that percentage change in reserves stays within the 2.5% band against the benchmark average of 45%. Interest rate in Korea is also very stable with 99% chances that interest rate differential wouldn’t exceed 50 basis points. The average probability of this event is only 88% in benchmark countries. According to these three criteria Korea seems to be a de facto floater. Its exchange rate is more volatile than committed floaters, and its reserves and interest rate are more stable. However monetary base there in Korea is quite volatile showing a very low probability of 21% against the benchmark average of 34%. But this could be a consequence of active monetary management which seems necessary due to highly volatile inflation with almost 77% chances that country would face a price shock (inflation rate) exceeding 2.5%. The average probability for such a shock in benchmark countries is only 36% almost half of that in Korea.
2.
Indonesia
Among ten candidate and five benchmark countries, Indonesia is the one having most volatile exchange rate with only 43% chances that percentage change falls within the narrow band of 2.5% against the benchmark average of 58%. Though its reserves are also relatively volatile to what we expect with only 45% chances of staying within a narrow band but that probability is exactly equal to the benchmark de facto floating average. These two facts taken together make the case of fear of floating very weak for Indonesia as there are no evidences of stabilizing exchange rate by means of intervention through reserves. Yet interest rate and monetary base are quite volatile there in Indonesia which could be an evidence of fear of floating if and only if Indonesia’s
P a g e | 28
exchange rate were more stable and its reserves were more volatile than the benchmark floating economies which is not actually the situation. Keeping in view the 96% chances of having an inflationary shock of more than 2.5 percent and also the fact that Indonesia is an Inflation Targeter, this high volatility of interest rates and monetary base seems very rational and compatible with the economy’s stated policies. (Ball & Reyes, 2008) have criticized Calvo and Reinhart’s work on the same grounds claiming that high interest rate volatility is not necessarily an indication of fear of floating especially if the country is pursuing inflation targeting regime. Correlation between real interest rate and inflation which has been suggested by Ball and Reyes as a criterion to differentiate credible inflation targeting regime with fear of floating is -0.46 and highly significant for Indonesia excluding it from the suspects of those exhibit fear of floating.
3.
Pakistan
Pakistan’s exchange rate is unexpectedly stable with a 93 percent chance that percentage change wouldn’t exceed the narrow band of 2.5 . This seems inconsistent not only with the country’s official declaration of practicing free floating regime but also with the present situation of terrorism and Afghan war carrying on there. This much stability in its exchange rate makes the country a perfect suspect of fear of floating subject to further investigations. When comes to foreign exchange reserves, they are highest in volatility ranking with only 0.15 probability that change falls within 2.5% band. Interest rates, monetary base and inflation are also significantly volatile strengthening our doubts that country is exhibiting high degree of fear of floating. Highly volatile reserves and interest rate together with relatively stable monetary base could be a result of mixed strategy of sterilized intervention and monetary aggregate targeting which are contradictory in nature given the current scenario of the country. Sterilization
P a g e | 29
stabilizes monetary base but pushes interest rates downward which should have been appreciated after the sale of foreign reserves to counter downward pressure on exchange rate. But because the outcome of sterilization on interest rate is incompatible with the high inflationary scenario, central bank is bound to increase interest rate again to curb inflation. This is a classic case of confused strategy, had the central bank opted to let intervention unsterilized, the interest rate would have been increased exactly as required by the policy makers to curb inflation. However this unsterilized intervention would leave monetary base relatively more volatile than it currently is that might be in contradiction to the policy of monetary aggregate targeting that Pakistan is pursuing. So Pakistan is in a troublesome position, if it practices floating regime in its true sense it could be disastrous for the economy during the times of devaluation pressure on local currency through exchange rate pass through channel. If it intervenes, it is definitely exhibiting a fear of floating and letting this intervention unsterilized would also be in contradiction to monetary aggregate targeting policy. If it opts for sterilized intervention, it eventually needs to reverse the downward pressure on interest generated as a result of sterilization to control already existing inflation.
4.
Philippines
The last de jure floater in our candidate sample is Philippines and its exchange rate is also very stable with a probability of 0.76. It is though less stable than Pakistan’s exchange rate but well above the average stability of benchmark de facto floaters. Reserves variability is however not significantly high when compared to benchmark. The probability of falling percentage change within the 2.5 percent band is 0.51 which tells us that reserves in Philippines are indeed more stable than any individual benchmark economy except Japan. Also their stability is well above the benchmark
P a g e | 30
average of 0.45. On standalone basis reserves do not show any significant sign of intervention; however the stability of exchange rate indicates that there are chances that it is being stabilized by some set of sterilized intervention. Interest rate and monetary base situation in Philippines is very similar to Korea. Interest rates are highly stable with a probability of 0.83 that change is confined within the 50 basis points and monetary base is highly volatile with only 24 percent chance that change lies within the 2.5% band. Like its other neighbors, probability of having inflation more than 2.5 percent is very high in Philippines i.e. 87%. Despite clearing the formal criteria of testing, Philippines stable exchange rate indicates some sort of pegging through intervention thus making it another suspect of fear of floating.
F.
COMPARISON WITH IMF CLASSIFICATION
In Figure 9 below we can see that among the four countries claiming to be a de jure floater, IMF validates the claim of only two (Korea and Philippines) and rejects for the other two (Indonesia and Pakistan). Figure 9: Exchange Rate Regime’s Classification Map Benchmark DeJure: Floating, DeFacto: Floating
Canada
Candidate DeJure Classification
Candidate DeFacto Classification
China
China
HongKong
HongKong
European Union
India
India
Japan
Indonesia
Indonesia
UK
Korea
Korea
Malaysia
Malaysia
USA1
Pakistan
Pakistan
USA2
Philippines
Philippines
Singapore
Singapore
USA3
Thailand
Thailand 0
1
2
0
1
2
Floating Managed Float Pegged
P a g e | 31
0
1
2
We agree to their assessment for two countries (Pakistan and Korea), however we have some reservations about Indonesia and Philippines. Pakistan’s de facto practices are clearly different from its de jure stance and it exhibits clearly the high degree of fear of floating and IMF rightly classified it as Managed Floating on de facto basis which is different from its de jure stance. Same is the case with Korea where our analysis provides evidences that Korea is truly practicing its de jure floating regime acknowledged by IMF too. However in case of Indonesia, we do not find any genuine evidence that it is trying to stabilize its exchange rate through intervention. The high variability in interest rate is consistent with its monetary stance of Inflation Targeting and cannot be linked to fear of floating given its highly volatile exchange rate; relatively stable reserves; and highly significant correlation between real interest rate and inflation. Contrary to that, IMF has accepted the claim of Philippines and placed it in the category of de facto floater. According to our opinion and analysis Philippines doesn’t seem to qualify for that category particularly when IMF rejected the claim of Indonesia in the same classification. Exchange rate is highly stable in Philippine that casts serious doubt about its claim of being floater. If we look at interest rate, it is relatively more stable in Philippines than Indonesia and seems to dominate the decision of IMF regarding these two economies. It gives the impression that IMF methodology is highly inspired by the classification methodology proposed by (Calvo & Reinhart, 2002)which ignored the influence of Inflation targeting regime on interest rate behavior and linked it directly to the intervention in exchange rate market because of prevailing fear of floating. This phenomenon is pointed out and explained by (Ball & Reyes, 2004) and (Ball & Reyes, 2008) and out of the scope of this paper10. Nonetheless, it seems that Philippines has taken an undue advantage of its interest rate stability despite the fact 10
Work in Progress in Co-authorship with Bushra Naqvi.
P a g e | 32
that its stable exchange rate is significantly in contradiction with its stated floating regime, on the other hand Indonesia seems to pay an undue penalty for its volatile interest rate despite being fully committed with both of its exchange rate and monetary policy stance.
G.
FRANKEL WEI REGRESSION 1.
Static Analysis
It is a bitter reality that identifying the true defacto exchange rate regime for a country is much more difficult than one thinks it is. In this section we used one of the most popular ways of identifying the implicit weights a country assigned to its probable currency basket, known as Frankel-Wei regression. This methodology was used by (Frankel, 1993); (Frankel & Wei, 1994)and (Frankel & Wei, 1995) and can be shown by Equation 7
Equation 7
Where
is the log difference of exchange rate in the domestic currency of
individual country and et , et , et , and et are US Dollar, Pound Sterling, Euro and Japanese Yen’s exchange rates. It is important to note that Frankel and Wei used Swiss Franc as a base (Numéraire) currency for the calculation of all exchange rates. However in some recent research (Frankel & Wei, 2007), use of SDR and gold is also suggested as a robustness test in response to the argument of strong correlation of Swiss France with Euro. Estimated coefficients in this equation,
1
,
2,
3,
4
represents the implicit weight
each country assigns to USD, GBP, EUR and JPY on the basis of proportionate variations in the domestic currency explained by each of these benchmark currencies. However,
P a g e | 33
merely the higher value of estimated coefficient may not necessarily an indication of pegging as it might result from the natural market driven correlation between two currencies. It is important to look at the significance of each coefficient as well as the overall variations explained by the regressor currencies (R-Square) to infer about the degree of pegging or flexibility (Baig, 2001). In the following table we present the two sets of OLS estimates obtained from Equation 7 for each country. First set is based on regression where we use Swiss Franc as a numérarie currency and the second where we replace it with SDR. US dollar seems to have an uncontestable influence on each currency irrespective of the choice of numéraire. However a significant decrease in R-Square and either an entire shift of significance from Euro to Yen or an increase in the significance of Yen can be observed often when we replace Swiss Franc with SDR. Due to this instability we now focusing our interpretations on regressions based on Swiss Franc. China, Hong Kong and Pakistan seems to have a strong peg with dollar which is the single highly significant currency explaining more than 95 percent of variation in case of China and Hong Kong and almost 75 percent for Pakistan. India, Singapore and Thailand seems to follow a basket peg where three out of four currencies are highly significant and explaining at least 70 percent or more variations in the domestic exchange rate. Malaysia is one step ahead where only two currencies; USD and Euro with their highly significant weights explaining almost 85 of variations. Philippines has also significant weights of USD and Euro but the proportion of explained variation is relatively lower there i.e. 65 percent. South Korean Won and Indonesian Rupiah seems to be the least influenced currencies by these benchmark currencies. In spite of the individual significance of USD, Euro or Pound, the four benchmark currencies are explaining 37 percent variations for Korea and only 16 percent for Indonesia.
P a g e | 34
These findings are consistent with our previous results where we disagree with the classification of IMF for Indonesia and Philippines. Here we have another strong evidence that Indonesia is not intervening in its exchange rate market or at least intervening less than Philippines, thus given the fact that IMF placed Philippines in de facto “Floating” regime, Indonesia should have been placed in the same regime at least or more flexible, if possible, instead of being placed in a “Managed Float”. Table 2: Static Estimates from Frankel-Wei Regression Country
Reg Type
Constant
USD
GBP
EUR
JPY
China
CHF Coef. T-Stat
-0.00161*** (-4.634801)
0.995896*** (52.51903)
-0.036478* (-1.750619)
-0.020006 (-0.566243)
-0.022639 (-1.160044)
SDR Coef. T-Stat
-0.00167*** (-4.758285)
0.924904*** (23.11958)
-0.045163* (-1.772368)
-0.016221 (-0.617304)
-0.019427 (-0.901323)
0.913927
CHF Coef. T-Stat
-1.61E-05 (-0.134128)
0.987115*** (151.2195)
-0.001456 (-0.202965)
-0.013543 (-1.113542)
0.01314* (1.955907)
0.997411
SDR Coef. T-Stat
-2.51E-05 (-0.209791)
0.986182*** (72.2791)
-0.000287 (-0.03305)
-0.003038 (-0.33896)
0.017168** (2.335455)
0.989701
CHF Coef. T-Stat
0.002516 (0.78269)
0.358325** (2.044577)
0.081375 (0.422543)
0.987002*** (3.022623)
-0.129411 (-0.717469)
0.166211
SDR Coef. T-Stat
0.002518 (0.780989)
-0.270413 (-0.73629)
-0.046114 (-0.197126)
0.192939 (0.799803)
-0.379876* (-1.919814)
0.042597
CHF Coef. T-Stat
0.000227 (0.166819)
0.684167*** (9.207798)
0.263455*** (3.226663)
0.437489*** (3.160093)
0.094324 (1.233449)
0.706890
SDR Coef. T-Stat
0.000549 (0.388492)
0.678939*** (4.216144)
0.207088** (2.018951)
0.075706 (0.715747)
-0.051173 (-0.589817)
0.192722
CHF Coef. T-Stat
0.000645 (0.297867)
0.313147*** (2.653338)
0.403447*** (3.110886)
0.708851*** (3.223588)
-0.006647 (-0.054724)
0.369899
SDR Coef. T-Stat
0.001028 (0.478808)
-0.09932 (-0.406053)
0.229847 (1.475275)
0.104233 (0.648776)
-0.267306** (-2.02839)
0.108938
CHF Coef. T-Stat
-0.00029 (-0.355384)
0.803385*** (18.04537)
0.051305 (1.048705)
0.19513** (2.352372)
-0.044526 (-0.971758)
0.851702
SDR Coef. T-Stat
-0.000301 (-0.37319)
0.579935*** (6.322144)
-0.003202 (-0.054809)
0.006914 (0.114757)
-0.11296** (-2.285634)
0.444193
CHF Coef. T-Stat
-0.000245 (-0.151772)
0.871139*** (9.915648)
-0.045055 (-0.466694)
0.432594*** (2.642736)
0.002329 (0.02576)
0.637509
SDR Coef. T-Stat
-0.000113 (-0.069835)
0.643005*** (3.488961)
-0.124446 (-1.060107)
0.038673 (0.31947)
-0.141788 (-1.427961)
0.200539
CHF Coef. T-Stat
0.003979*** (3.415307)
0.911517*** (14.35285)
-0.038313 (-0.548994)
0.088501 (0.747928)
-0.098879 (-1.51281)
0.755937
SDR Coef. T-Stat
0.003934*** (3.421165)
0.814724*** (6.219829)
-0.03385 (-0.405707)
0.136077 (1.581589)
-0.080413 (-1.139433)
0.375498
CHF Coef. T-Stat
-0.00051 (-0.653737)
0.526085*** (12.35934)
0.061092 (1.306109)
0.40939*** (5.161983)
0.128377*** (2.930444)
0.799573
SDR Coef. T-Stat
-0.000469 (-0.602552)
0.231572** (2.614783)
-0.012546 (-0.2224)
0.065937 (1.133498)
0.00824 (0.172687)
0.055396
Hong Kong
Indonesia
India
Korea
Malaysia
Philippines
Pakistan
Singapore
P a g e | 35
Adj. R Square 0.977763
Thailand
CHF Coef. T-Stat
-0.001314 (-1.231934)
0.478093*** (8.222858)
0.177975*** (2.785626)
0.163956 (1.513482)
0.268569*** (4.488201)
0.702172
SDR Coef. T-Stat
-0.001267 (-1.197374)
0.309607** (2.568139)
0.117691 (1.53262)
-0.04411 (-0.557039)
0.186971*** (2.878566)
0.134236
2.
Time Varying (Dynamic) Assessment
To have a deeper insight of how the influences of benchmark currencies over Asian currencies have evolved over the time period, we augment the static Frankel Wei estimates by presenting recursive OLS estimates. Recursive time varying estimates are obtained through iterative estimation of the same regression presented in Equation 7 where the sample size is increased by one, in each iteration. These recursive estimates are extremely helpful in analyzing the dynamic trend in benchmark currency weighting each country has shown while determining the value of its own currency. Figure 10 shows that for China and Hong Kong, despite some variations, dollar remains the sole anchor even after 2005 when China announced to follow the managed floating regime. There was some degree of substitution of EUR for USD in China immediately after 2005 but the dollar regained its weight during 2007-2008. Figure 10: Recursive Coefficients from Frankel-Wei Regression HONG KONG: Dynamic Currency Weighting
CHINA: Dynamic Currency Weighting
1.01
1.015 1.010
1.00
1.005 0.99
1.000 0.995 .10
0.990
0.98 .04
0.97
0.985
.05
.02
.00
.00
-.05
-.02
-.10
-.04 01
02
03
04
05
06
07
08
01
09 Beta(USD) Beta(EUR)
02
03
Beta(GBP) Beta(JPY)
P a g e | 36
04
05
06
07
08
09
Philippines and Pakistan are also strongly influenced by USD like China and Hong Kong. Contrary to above two countries, these two have a very smooth pattern in dynamic estimates of all currencies. Indeed the observed pattern indicates a de facto pegging with very minor fluctuations at the end of sample period. These fluctuations could be the natural outcome of increasing concerns about the weakening dollar which have been spread around the globe after the blow of subprime crisis in USA. Figure 11: Recursive Coefficients from Frankel-Wei Regression PAKISTAN: Dynamic Currency Weighting
PHILIPPINES: Dynamic Currency Weighting
1.6
3.0 2.5
1.2
2.0 1.5 10
0.8 1.5
1.0
0.4
1.0 0.5
0.0
0.5
5
0.0 0 -0.5 -1.0
-5 01
02
03
04
05
06
07
08
09 Beta(USD) Beta(EUR)
01
02
03
04
05
06
07
08
09
Beta(GBP) Beta(JPY)
India seems to have some tilt towards EUR since its inception and one can easily observe that USD influence on Indian Rupee has been started declining in 2002 with a moderate increase in the weights of EUR. During the period of nine years, almost 40 percent of USD’s share has shifted towards other currencies with a EUR being the significant recipient. Exactly the same has happened in Malaysia but during lesser time frame and at a faster pace where at times USD and EUR weights seem to equate each other.
P a g e | 37
Figure 12: Recursive Coefficients from Frankel-Wei Regression INDIA: Dynamic Currency Weighting
MALAYSIA: Dynamic Currency Weighting 1.2
1.05
1.1
1.00
1.0
0.95
0.9
0.90
0.8
0.85
.3 0.7
.8
0.6
0.80 .2
0.75
.4 .1 .0
.0
-.4
-.1 01
02
03
04
05
06
07
08
09
01
Beta(USD) Beta(EUR)
02
03
04
05
06
07
08
09
Beta(GBP) Beta(JPY)
Figure 13: Recursive Coefficients from Frankel-Wei Regression SINGAPORE: Dynamic Currency Weighting
THAILAND: Dynamic Currency Weighting .6
1.0 0.9
.4
0.8 .2 1.5
.0
0.7 0.6
2
0.5
1.0 -.2
1 0.4
0.5
0
0.0
-1
-0.5
-2
-1.0
-3 01
02
03
04
05
06
07
08
09
01 Beta(USD) Beta(EUR)
02
03
Beta(GBP) Beta(JPY)
P a g e | 38
04
05
06
07
08
09
Figure 14: Recursive Coefficients from Frankel-Wei Regression INDONESIA: Dynamic Currency Weighting
KOREA: Dynamic Currency Weighting 1
1.2
0
0.8
-1
0.4
-2 10
0.0
-3 1.5 -4
5
-0.4
1.0 0.5 0.0
0
-0.5 -5
-1.0 01
02
03
04
05
06
07
08
09
01
Beta(USD) Beta(EUR)
Beta(GBP) Beta(JPY)
02
03
04
05
06
07
08
09
Singapore, Thailand, Indonesia and Korea are the set of countries having reliance on at least two or more currencies. For Singapore and Thailand, dollar weight is almost 0.5 on average over the whole sample and for Indonesia and Korea even less (approx 0.35) with the positive weights to at least two other benchmark currencies on consistent basis. This might be an indication of basket peg for Singapore and Thailand where the proportion of explained variations in domestic currency is about 70 percent. However as for Indonesia and Korea this proportion is only 16 and 36 percent respectively suggesting the existence of market driven natural correlation of local currencies with benchmark currencies thus precluding any possibility of basket peg.
P a g e | 39
V.
Principal Component Analysis (PCA) To augment the methodology provided by Calvo and Reinhart that we follow in
this paper, we perform principal component analysis (PCA). This analysis helps us in having a concise picture for each country on the basis of many variables. Principal components are the orthogonal vectors which we obtained after compressing the data and reducing its dimensions. Figure 15 is the Orthogonal loading biplot for five different sets of probabilities across countries.
These are the same probabilities we have
calculated and interpreted in previous sections. For exchange rate, reserves, monetary base and price level it measures the chances of confining percentage change within the narrow band of plus/minus 2.5%, and for nominal interest rate (CMR) it measures the probability of difference falling within the band of plus/minus 50 basis points. Higher value of probability is an indication of stability in concerned variable. In Figure 15 we position countries on the basis of first two principal components which together account for 71.7% of variations in all variables. PC 1 (x-axis) has strong positive loading for Reserves, Interest Rate and Inflation Stability and has neutral to slightly positive loading for the stability of Exchange rate and Monetary Base. PC 2 has strong positive loading for Exchange rate and monetary base, neutral for Inflation and slightly negative loading for Reserves and Interest Rate. With this structure it is very easy to classify the whole map in four categories or quadrants. Farther the country moves on the right side (Upper or lower quadrant), higher it will have the stability in Inflation, Interest Rate and/or Reserves and vice versa. In the same way higher the country moves up (right or left quadrant), higher the probability that its exchange rate and/or monetary base are stable and vice versa. We classify bottom right quadrant as “Free Floating Quadrant” because it combines the stability in reserves and interest rate with the volatility in exchange rate.
P a g e | 40
Top left quadrant is classified as “Fear of Floating Quadrant” or we can also call it as exchange rate manipulation quadrant because it combines the high stability in exchange rate and monetary base with volatile Reserves and/or volatile interest rate. Few countries are at extreme corner positions and can easily be placed in different categories. For example Pakistan and India are at the significant distance from other countries in the top left quadrant indicating relatively higher stability in their exchange rate and monetary base but significant volatility in their Reserves, Interest rate and/or Inflation. Stable exchange rate despite volatile inflation can only be achieved by intervention either by reserves or by interest rate which is evident in the case of these two countries. Stable monetary base point outs towards the probable sterilization acts conducted by these countries to mitigate the effect of intervention on monetary base. European Union, Japan and Korea are in the bottom right quadrant which we named as “Free Floating Quadrant”. These economies share the characteristics of a true floater i.e. volatile exchange rate and stable Reserves and Interest rate which are in consistency with their claim of following freely floating regime. Interestingly the position of three other benchmark countries (Canada, USA and UK) is not consistent with their claim of being a free floater. China, Hong Kong, Singapore, Malaysia and Thailand are positioned in top right quadrant jointly showing stability in all five variables. It would be interesting to investigate how these countries are achieving this simultaneous relative stability in all these variables especially in exchange rate when there is no sign of intervention through reserves or interest rate. Positioning in bottom left quadrant is an indication of volatility in all variables, an exactly opposite situation to top right quadrant. Philippines, UK and Indonesia clearly fall in this quadrant and are difficult to be categorized in one or other particular regime. For example in Indonesia reserves and interest rate are volatile but could it be an indication of intervention? Surely not as its exchange rate is the most
P a g e | 41
volatile among all 15 countries and this volatile exchange rate restricts us to put an allegation of fear of floating on Indonesia. This finding is consistent with the analysis we performed in previous sections where we discussed that pursuing an Inflation Targeting regime could be one possibility among many for this anomaly. Philippines’ position also preclude it from the list of free floaters as its exchange rate is not as volatile and its reserves and interest rate are not as stable, as they should have been being a floater. USA is positioned almost at origin indicating the average stability of its economic variables relative to other sample countries. However according to the criteria we observe in this paper, its de facto ranking as a free floater by IMF and its inclusion in benchmark countries raises serious questions about these decisions. The complete result of principal component analysis is reported in (Appendix Table III). Figure 15: Orthogonal Biplot Including Country Scores and Variable Loadings
Country Position Based on First two PCs 4 Fear of Floating Quadrant
PC_BASE_MONEY PC_EXCHANGE_RATE
Component 2 (30.5%)
3 2
Pakistan India
China HongKong Singapore Malaysia Thailand Canada
1 0
PC_INFLATION
USA1
PC_NOMINAL_INT_RATE Philippines PC_RESERVES European Union Japan
-1 UK
-2
Korea
Indonesia
-3 Free Floating Quadrant
-4 -4
-2
0
2
Component 1 (41.2%)
P a g e | 42
4
VI.
Scoring Index To have a more precise idea about the relative position of each country we
develop a regime flexibility index based on normalized scores of first principal component obtained from three probabilities. We include Exchange rate, Reserves and Real interest rate instead of nominal to keep more focus on fundamentals as change in monetary base is subject to sterilization and monetary aggregate targeting and thus could be quite volatile or stable irrespective of de facto exchange rate regime. In the same way substitution of nominal interest rate with real is because the former could be more responsive to inflation shocks and it can complicate the picture if a country is giving more importance to price stability without having any intention to stabilize exchange rate (Ball & Reyes, 2008). We reverse the probability of exchange rate stability by subtracting it from 1 so that the high value of resulting probability would indicate the higher volatility in exchange rate (High Flexibility). In the same way higher probabilities that change in reserves and real interest rate stays within a narrow band can also be taken directly as an indicator of highly flexible regime. 1
Equation 8
Equation 9
Equation 10
Where, as we have mentioned earlier, LB and UB are lower and upper bound for the narrow interval which is set as 2.5% for Equation 8, and Equation 9 whereas 50 basis points for Equation 10.
and
are the percentage change in Exchange rate
P a g e | 43
and Reserves respectively, calculated previously as Equation 5 and mentioned below again:
And
is the change in real interest rate calculated as
1.
We name the normalized scores of first principal component (PC1) based on these probabilities as ERFI which stands for “Exchange Rate Flexibility Index” and its structure can easily be understand from the schematic diagram given below (Figure 16). By construction its mean is zero, positive values indicates the regime flexibility based on higher volatility in exchange rate and higher stability in reserves and real interest rate. Negative values indicate regime’s rigidity based on higher stability in exchange rate and volatility in reserves and real interest rate. Figure 16: Schematic Diagram of Exchange Rate Flexibility Index (ERFI)
ERFI (PC 1)
Exchange Rate Volatility
Reserves Stability
Real Interest Rate Stability
•+ve Values •Flexibility
ERFI (PC 1) •-ve Values •Rigidity
Results of principal component analysis while constructing ERFI are reported in Table 3.
P a g e | 44
Table 3: PCA Based Construction of Exchange Rate Flexibility Index (ERFI) Principal Component Analysis Eigenvalues: (Sum = 3, Average = 1) Number
Value
Difference
Proportion
Cumulative Value
Cumulative Proportion
1 2 3
1.558394 1.026148 0.415458
0.532246 0.610690 ---
0.5195 0.3420 0.1385
1.558394 2.584542 3.000000
0.5195 0.8615 1.0000
Eigenvectors (loadings): Variable
PC1 (ERFI)
PC 2
PC 3
Exchange Rate Volatility Reserves Stability Real Int Rate Stability
0.488326 0.500100 0.715149
0.714766 -0.699363 0.000997
0.500648 0.510677 -0.698971
Bold value in upper panel reflects that 51% of total variance is explained by PC 1. Orthogonal loadings for all principal components are provided in lower panel, in which bold values are used as weights assigned to three variables for the calculation of component score of each country. These normalized weighted component scores are the actual index values of ERFI and are used to rank different countries on the basis of their exchange rate flexibility (Appendix Table IV). Figure 17 provides a graphical representation of benchmark and candidate countries on the basis of ERFI index values, classified according to their de jure regime.11
11
For benchmark countries, de jure and de facto regimes are same.
P a g e | 45
Figure 17: Exchange Rate Flexibility Index (ERFI) Ranking ERFI Inex Benchmark
Candidate
3
3 2.4
2
2
2.0
1.6
1
1 0.6
0.4
0
-3
HongKong
-2.6
China
USA3
USA2
USA1
UK
Japan
European Union
Canada
-1.2
Thailand
-3
-1.1
Singapore
-2
-0.7
Philippines
-2
-0.3
-0.4
-0.5
Pakistan
-1
Korea
-1
0.0
Malaysia
-0.1
Indonesia
-0.0
India
0
Floating Ma n a g e d F lo a t Pegged
These results are robust to our previous findings where we raised questions on the authenticity of de facto classification for UK and USA. Also for candidate countries this ranking is in full consistency with the previous analysis except that ERFI gives Indonesia a lower rank than Philippines in exchange rate flexibility. This is because the eigenvector which we used to construct ERFI scores has relatively higher correlation with real interest rate stability than other variables, thus a country with volatile real interest rate has to pay moderately higher penalty which is actually the reason for Indonesia’s low ranking whose real interest rate is three times more volatile than Philippines’ despite that other two variables for Indonesia show the signs of higher flexibility.
VII. Conclusion We analyzed small sample of Asian economies in this paper with reference to their exchange rate regimes. Primary focus is to reevaluate the official commitments of Asian economies (De Jure Regime) against their real practices (De Facto Regime) and to
P a g e | 46
estimate recent developments in the context of fear of floating. We also analyze the exchange rate management in benchmark countries on the very same criteria applicable on candidate countries and compare our results with the de facto classification provided by IMF. Out of ten countries in our candidate sample, four claim to be de jure floaters, five claim to be managed float and one follows pegging. We find that most of the sample countries are committed to their de jure exchange rate regime. Volatility of exchange rate is a decreasing function of regime rigidity and the average volatility of exchange rate between benchmark de facto floaters and candidate de jure floaters is statistically equivalent. Exchange rate however is significantly stable in candidate de jure managed floating regime pointing out the probable existence of fear of floating in these economies. Second integral variable in determining the de facto exchange rate flexibility is reserves volatility and it provides very interesting picture. In contrast to theoretical expectations, we find that on average reserves stability decreases as flexibility increases. This finding is nonetheless consistent with the findings of Calvo and Reinhart and indicates the persistence in fear of floating. It can be said that higher the degree of flexibility committed by an economy in its official policy, the more severely it is affected by fear of floating. On the other hand average volatility in the reserves of benchmark countries is as high as in candidate countries which casts some serious doubts about their de facto “Floating” status and makes their selection as a benchmark questionable. On the basis of principal component, and Frankel-Wei regression analyses, we find some inconsistencies in the procedure adopted by IMF to categorize economies. IMF methodology seems to be infected by the same shortcoming which Ball and Reyes has
P a g e | 47
found in the Calvo and Reinhart methodology according to which volatility in interest rate is straightforwardly taken as a symptom of fear of floating ignoring the facts that whether or not country is pursuing inflation targeting regime; and that central bank uses policy instrument in response to exchange rate shocks or inflation shocks. It looks to us that Indonesia is paying the penalty of this flaw in IMF’s methodology in the shape of being categorized as Managed float contrary to its de jure claim of Free Floater and Philippines seems to be taken an advantage in the shape of approval of its de jure claim of Free floater despite that its real practices clearly exhibit fear of floating. As a robustness test we supplement this analysis with the static and dynamic Frankel-Wei tests to assess the degree and time varying behavior of pegging or flexibility in sample countries. These results also support our concerns, about the methodology used by IMF, identified in the categorization of Indonesia and Philippines. According to Frankel-Wei analysis, USD appears as the first choice to make de facto peg for many countries like China, Hong Kong, Philippines and Pakistan. The remaining countries, except Korea and Indonesia, seem to have a basket peg with two or more currencies and rapidly increasing weight to Euro.
P a g e | 48
VIII. Bibliography Baig, T. (2001). Characterizing Exchange Rate Regimes Post-Crisis East Asia. IMF Working Paper , 01/152. Ball, C., & Reyes, J. (2008). Inflation Targeting or Fear of Floating in Disguise: A Broader Perspective. Journal of Macroeconomics , 30, 306-326. Ball, C., & Reyes, J. (2004). Inflation Targeting or Fear of Floating in Disguise: The Case of Mexico. International Journal of Finance and Economics , 9, 49-69. Barro, R. J., & Gordon, D. B. (1983). A Positive Theory of Monetary Policy in a Nature Rate Model. Journal of Political Economy , 91, 589-610. Bird, G., & Rajan, R. S. (2001a). Cashing In On and Coping With Capital Volatility. Journal of International Development , 13, 1-23. Bird, G., & Rajan, R. S. (2001b). International Currency Taxation and Currency Stabilization in Developing Countries. Journal of Development Studies , 37, 21-38. Calvo, G. A., & Reinhart, C. M. (2002). Fear of Floating. The Quarterly Journal of Economics , CXVII (2), 379-408. Calvo, G. (1978). On the Time Consistency of Optimal Policy in the Monetary Economy. Econometrica , 46, 1411-28. Cavoli, T., & Rajan, R. S. (2009). Exchange Rate Regimes and Macroeconomic Management in Asia. Aberdeen, Hong Kong: Hong Kong University Press. Eichengree, B., & Hausmann, R. (1999). Exchange Rates and Financial Fragility. NBER Working Papers , 7418. Flood, R. P., & Jeanne, O. (2005). An interest rate defense of a fixed exchange rate? Journal of International Economics , 66 (2), 471-484. Frankel, J. (1993). Is Japan Creating a Yen Bloc in East Asia and the Pacific? In J. Frankel, & M. Khaler, Regionalism and Rivalry: Japan and the US in Pacific Asia. Chicago: University of Chicago Press. Frankel, J., & Wei, S. (2007). Assessing China's Exchange Rate Regime. Economic Policy , 51, 575-614. Frankel, J., & Wei, S. (1995). Emerging Currency Blocs. Dans H. Genberg, The International Monetary System: its Institutions and its Future (pp. 111-143). Berlin: Springer. Frankel, J., & Wei, S. (1994). Yen Bloc or Dollar Bloc? Exchange Rate Policies of the East Asian Economies. Dans T. Ito, & A. O. Krueger, Macroeconomic Linkages: Savings, Exchange Rates and Capital Flows. Chicago: University of Chicago Press.
P a g e | 49
Gartner, M. (1987). Intervention Policy under Floating Exchange Rates: An Analysis of the Swiss Case. Economica , 439-453. Ghosh, A. R., & Ostry, J. D. (2009). Choosing an Exchange Rate Regime. Finance & Development , 46 (4), 38-40. Ghosh, A., Gulde, A. M., Ostry, J., & Wolf, H. (1997). Does the Nominal Exchange Rate Regime Matter? NBER Working Paper , 5874. Honig, A. (2005). Fear of Floating and Domestic Liability Dollarization. Emerging Markets Review , 6 (3), 289-307. Husain, I. (2005). Monetary-Cum-Exchange Rate Regime What Works Best For Emerging Market Economies? SBP Conference on Monetary Cum Exchange Rate Regime. IMF. (1997). Articles of Agreement of the International Monetary Fund. Récupéré sur International Monetary Fund: http://www.imf.org/external/pubs/ft/aa/index.htm Kearney, C., & MacDonald, R. (1986). Intervention and Sterilisation under Floating Exchange Rates: The UK 1973-1983. European Economic Review , 345-364. Kydland, F. E., & Prescott, E. C. (1977). Rules Rather Than Discretion: The Inconsistency of Optimal Plans. Journal of Political Economy , 85, 473-491. Lee, J.-Y. (1997). Sterilizing Capital Inflows. Economic Issues by IMF . McKinnon, R., & Schnabl, G. (2004). The East Asian Dollar Standard, Fear of Floating, and Original Sin. Review of Development Economics , 8 (3), 331-360. Mishkin, F. S. (1998, June). Exchange Rate Pegging in Emerging-Market Countries? Retrieved October 2010, from Colombia University: http://www0.gsb.columbia.edu/faculty/fmishkin/PDFpapers/IF98.pdf Neely, C. J. (2000, September). Are Changes in Foreign Exchange Reserves Well Correlated with Official Intervention? Consulté le November 2010, sur Federal Reserve Bank of St. Louis: http://research.stlouisfed.org/publications/review/00/09/0009cn.pdf Nguyen, Q. H. (2010). Liability Dollarization and Fear of Floating. Institute of Developing Economies, IDE Discussion Paper , 247. Nogueira, R. P. (2009). Inflation Targeting and Fear of Floating in Brazil, Mexico and South Korea. Economia , 10 (2). Obstfeld, M. (1983). Exchange Rates, Inflation, and the Sterilization Problem: Germany, 1975-1981. European Economic Review , 161-189. Obstfeld, M., & Rogoff, K. (1995). The Mirage of Fixed Exchange Rates. Journal of Economic Perspectives , 9 (Fall), 73-96.
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Taylor, D. (1982). Official Intervention in the Foreign Exchange Market, or, Bet Against the Central Bank. Journal of Political Economy , 356-368. Xie, P. (2004). China's Monetary Policy: 1998-2002. Working Paper: Stanford Center for International Development , Working Paper No: 217.
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APPENDIX
Appendix Table I: Probabilities for All Variables with group averages Country and Regimes Probability that monthly percentage change in variable falls within the band of +/- 2.5% +/- 50 basis points Country De De Exchange Foreign Base M1 M2 Price N. R. Interest Jure Facto Rate Reserves Money Index Interest Rate Rate Benchmark Country Panel Canada F F 61.54% 47.97% 49.62% 42.09% 16.80% 64.92% 95.78% 52.83% European F F 55.75% 49.40% 32.63% 98.18% 100.00% 66.38% 98.43% 88.00% Union Japan F F 57.35% 68.14% 30.15% 93.49% 82.46% 98.34% 100.00% 81.42% UK F F 57.66% 33.00% 16.94% 0.00% 91.85% 43.84% 52.45% 47.91% USA* F F 55.45% 27.82% 40.78% 98.40% 100.00% 46.00% 94.36% 50.63% Benchmark Mean (All) 57.5% 45.3% 34.0% 66.4% 78.2% 63.9% 88.2% 63.9% BM Mean Ex. USA 58.1% 49.6% 32.3% 58.4% 72.8% 68.4% 86.7% 68.4% BM Mean Ex. Japan
57.6%
Candidate Country Panel China MF P 100.00% HongKong P P 100.00% Indonesia F MF 42.51% India MF MF 81.74% Korea F F 53.73% Malaysia MF MF 97.78% Philippine F F 76.37% s Pakistan F MF 92.79% Singapore MF MF 88.60% Thailand MF MF 82.98% Candidate Mean (All) 81.65% Mean De facto Pegged 100.00% Mean De facto Man 81.07% Float Mean De facto Floating 65.1% Mean De jure Pegged 100.00% Mean De jure Man 90.22% Float Mean De jure Floating 66.35%
39.5%
35.0%
59.7%
77.2%
55.3%
85.3%
55.3%
45.36% 66.07% 44.79% 44.33% 63.79% 45.56% 51.32%
39.51% 37.13% 18.77% 48.13% 20.55% 34.24% 24.34%
6.24% 4.99% 44.77% 50.53% 28.84% 56.49% 42.82%
79.33% 66.64% 76.61% 81.14% 84.62% 93.55% 77.58%
48.49% 53.46% 4.35% 9.60% 22.96% 50.32% 13.35%
89.45% 51.84% 15.49% 24.86% 99.12% 100.00% 84.92%
36.03% 32.39% 15.06% 20.97% 62.65% 46.71% 44.91%
15.23% 68.50% 54.75% 49.97% 55.72% 45.53%
43.75% 44.54% 41.68% 35.26% 38.32% 38.52%
40.86% 63.55% 49.69% 38.88% 5.62% 50.98%
72.90% 92.49% 89.13% 81.40% 72.99% 84.30%
12.60% 63.00% 43.17% 32.13% 50.97% 30.51%
19.02% 93.15% 96.40% 67.42% 70.65% 58.15%
17.31% 47.54% 38.98% 32.13% 50.97% 30.51%
57.6% 66.07% 51.70%
22.4% 35.8% 81.1% 37.13% 4.99% 66.64% 41.62% 45.30% 87.13%
18.2% 53.46% 42.91%
92.0% 51.84% 80.77%
18.2% 53.46% 42.91%
43.78%
26.85% 39.32% 77.93%
13.32%
54.64%
13.32%
* Exchange rate averages are based on three rates for USA F= Floating, MF= Managed Floating, P=Pegged
P a g e | 52
Appendix Table II: Equality of Mean between Benchmark and Candidate Floating Regimes Test for Equality of Means of Exchange Rate Stability Method
Df
Value
Probability
t-test 9 Satterthwaite-Welch t-test* 3.027337 Anova F-test (1, 9) Welch F-test* (1, 3.02734) *Test allows for unequal cell variances Analysis of Variance
-1.071574 -0.779928 1.148270 0.608287
0.3118 0.4918 0.3118 0.4918
Source of Variation
Df
Sum of Sq.
Mean Sq.
Between Within
1 9
0.019793 0.155135
0.019793 0.017237
Total
10
0.174928
0.017493
Std. Dev. 0.020134 0.225612 0.132260
Std. Err. of Mean 0.007610 0.112806 0.039878
Category Statistics (For Floating Regimes) CAT Benchmark Candidate All
Count 7 4 11
Mean 0.575319 0.663500 0.607385
P a g e | 53
Appendix Table III: PCA Based on All Decision Variables Principal Components Analysis Eigenvalues: (Sum = 5, Average = 1) Number
Value
Difference
Proportion
Cumulative Value
Cumulative Proportion
1 2 3 4 5
2.059671 1.527318 0.705833 0.401459 0.305718
0.532353 0.821485 0.304374 0.095741 ---
0.4119 0.3055 0.1412 0.0803 0.0611
2.059671 3.586989 4.292823 4.694282 5.000000
0.4119 0.7174 0.8586 0.9389 1.0000
Eigenvectors (loadings): Variable
PC 1
PC 2
PC 3
PC 4
PC 5
XR_2_5PB_S3 RES_2_5PB_S3 CMR_0_5PB_S3 M0_2_5PB_S3 INF_2_5PB_S3
0.047803 0.504122 0.599430 0.125695 0.607009
0.689249 -0.158006 -0.079831 0.702488 0.010314
0.474891 0.709917 -0.229419 -0.327451 -0.332626
-0.453620 0.413683 -0.606462 0.466344 0.194482
-0.302255 0.213961 0.462474 0.407443 -0.694961
XR_2_5PB_S3 1.000000 0.026154 -0.034212 0.519552 -0.012070
RES_2_5PB_S3
CMR_0_5PB_S3
M0_2_5PB_S3
INF_2_5PB_S3
1.000000 0.456243 -0.098997 0.447952
1.000000 0.066624 0.656428
1.000000 0.194937
1.000000
Ordinary correlations:
XR_2_5PB_S3 RES_2_5PB_S3 CMR_0_5PB_S3 M0_2_5PB_S3 INF_2_5PB_S3
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Appendix Table IV: Country Ranking on the basis of ERFI scores Country Ranking ERFI based on S3* Country Ranking ERFI based on S2** Japan European Union Korea Canada Singapore Philippines UK USA Thailand Indonesia Hong Kong Malaysia China India Pakistan
2.365174913 1.99769791 1.644103857 0.553520967 0.372706652 0.005998363 -0.03339205 -0.05746859 -0.25630345 -0.40399057 -0.54040909 -0.67813323 -1.12051979 -1.21932808 -2.62965782
Japan European Union Korea Canada Singapore UK Philippines USA Indonesia Thailand Hong Kong Malaysia China India Pakistan
2.242172769 1.924678685 1.761402948 0.601798101 0.462880945 -0.02408857 -0.06557927 -0.10191479 -0.28269476 -0.37274508 -0.44417716 -0.58659458 -1.20825713 -1.29695213 -2.60992998
* S3 is the set of probabilities where Z-score is derived from the Mean of absolute percentage changes and standard deviation from the actual percentage changes in concerned variables. ** S2 is the set of probabilities where for the calculation of Z-score, both mean and standard deviation are derived from the absolute percentage change in concerned variables.
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Appendix Figure A: Country Positioning According to ERFI Orthonormal Loadings Biplot 3 1-XR_2_5PB_S3
Component 2 (34.2%)
2 USA1 Indonesia UK
1
Pakistan European Union
Canada
0
Korea
India
Philippines
REALINT_0_5PB_S3 Japan
Thailand Malaysia China
-1
Singapore HongKong
-2
RES_2_5PB_S3
-3 -3
-2
-1
0
1
2
3
Component 1 (51.9%)
Appendix Figure B: PCA Position based on All Variables (Absolute % Change Sample: S2) Orthonormal Loadings Biplot 4 M0_2_5PB_S2 XR_2_5PB_S2
Component 2 (30.7%)
3 Pakistan
2
India China ThailandSingapore Malaysia HongKong Canada
1 0 -1 UK
USA1 INF_2_5PB_S2 Philippines CMR_0_5PB_S2 European Union RES_2_5PB_S2 Japan
Indonesia
-2
Korea
-3 -4 -4
-2
0
Component 1 (41.7%)
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2
4
Appendix Figure C: Country Positioning According to ERFI (Absolute % Change Sample: S2) Orthonormal Loadings Biplot 3
1-XR_2_5PB_S2
Component 2 (34.7%)
2 USA1 Indonesia UK
1
Pakistan European Union Canada
0
Korea
REALINT_0_5PB_S2 Japan
India China
-1
Philippines Malaysia Thailand
HongKong Singapore
-2
RES_2_5PB_S2
-3 -3
-2
-1
0
1
Component 1 (51.4%)
***** *****
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2
3
