The Mechanics of a Successful Exchange Rate

FEDERAL RESERVE BANK OF ST.LOUIS

that would have hinted at problems for Thailand The Mechanics of a prior to July 1997. The next section discusses alternative exchange Successful Exchange rate regimes to put the unilateral peg in context. The third section presents an empirical model of mone- Rate Peg: Lessons for tary policy to describe the mechanics with which Austria pegged its exchange rate. The fourth section Emerging Markets applies the same model to describe Thailand’s monetary policy and the contrast with Austria. Michael J. Dueker and Andreas M. Fischer ALTERNATIVE EXCHANGE RATE xchange rate pegs collapsed in many coun- REGIMES tries in the 1990s, leading to dreary assess- As a prelude to an analysis of the mechanics Ements of the merits of pegged exchange rate of a unilateral exchange rate peg, it is useful to regimes. Whether one points to the failure of describe the spectrum of alternative exchange rate Mexico’s peg in December 1994 or to the sharp regimes. In addition to unilateral exchange rate devaluations in East Asia in 1997-98, in Russia in pegs, there are five other exchange rate regimes: a August 1998, and in Brazil in January 1999, the floating rate (including managed floats), multilateral collapse of unilateral exchange rate pegs often exchange rate pegs, currency boards, dollarization, preceded acute financial and macroeconomic and currency union. We describe here where the crises. Despite recent failures, however, exchange unilateral peg lies along the spectrum. Since the rate pegs remain a prevalent policy choice. Calvo end of the Bretton Woods system of fixed exchange and Reinhart (2000) argue that the exchange rate rates in 1973, floating exchange rates have displayed volatility that accompanies a floating exchange rate a very high degree of variability without a corre- regime is particularly onerous to emerging markets, sponding increase in the variability of exchange rate and thus can be a worse policy choice than a peg fundamentals (Flood and Rose, 1999). Moreover, that reduces the variability of the exchange rate, Hausmann, Panniza, and Stein (1999) have shown even if it does not attain the complete confidence that emerging markets in Latin America that have of investors. Given the continued prevalence of attempted to allow their exchange rates to float have pegs, it is worth seeking additional understanding experienced greater interest rate volatility than of what makes a peg successful or not. fixed-rate regimes. For this reason, Calvo and For this reason, we find it useful to study what Reinhart (2000) argue that floating exchange rates was arguably the most successful unilateral can have destabilizing effects on emerging markets. exchange rate peg: Austria’s peg to the Deutsche For the next four regimes—all variants of fixed mark prior to Austria’s entry into the European exchange rates—we start with the type of fixed rate Monetary System in 1995. An estimated model of that is closest to a float and move along the spec- Austrian monetary policy mechanics helps identify trum from there. In the first three regimes, a home salient features that made the Austrian peg credible country unilaterally fixes its currency to an “anchor” to the public. We then apply the same model to currency. The unilateral nature of the regime implies monetary policy in Thailand: among the East Asian that the anchor country is not obligated to assist countries that eventually devalued, Thailand had the home country if its currency comes under maintained the tightest peg to the U.S. dollar prior speculative attack. In a pegged regime, it is incum- to July 1997. The conventional wisdom is that the bent on the pegging country to set a monetary currency crisis in Thailand came without warning policy that always appears to currency traders to and caught financial markets by surprise (Corsetti, be consistent with the preannounced conversion Pesenti, and Roubini, 1999, and Halcomb and rate. The best way to uphold this commitment is to Marshall, 2000). We investigate whether there were run a monetary policy that is similar to that in the any contrasts between the Austrian and Thai pegs anchor country in terms of inflation rates and credit expansion. A pegging regime is more resistant to Michael J. Dueker is a research officer at the Federal Reserve Bank of speculative attack if banks and other institutions St. Louis. Andreas M. Fischer is an economic adviser at the Swiss National Bank, Zurich, Switzerland. Mrinalini Lhila provided hold an amount of foreign-exchange reserves that research assistance. is at least as great as the quantity of short-term debt

© 2001, THE FEDERAL RESERVE BANK OF ST.LOUIS SEPTEMBER/OCTOBER 2001 47 R EVIEW that is denominated in foreign currencies. Taiwan, A MODEL OF MONETARY POLICY for example, was largely immune to the Asian crisis MECHANICS FOR A UNILATERAL PEG of 1997-98 due to its large holdings of foreign exchange reserves. Many other emerging markets, In practice, nearly all central banks implement however, intend to be net borrowers in foreign cur- monetary policy by setting a short-term interest rencies, and they attract foreign lending by estab- rate as a policy instrument. A central bank trying lishing a peg and promising a stable exchange rate. to maintain an exchange rate peg will focus on the The best way to keep this promise is to run a mon- interest rate differential between the short-term etary policy that closely mimics that of the anchor rate in its domestic currency and the prevailing country. short-term rate in the anchor currency. If the home A currency board differs from a unilateral peg currency comes under selling pressure, an increase in that the home country no longer sets its own in the interest rate differential can attract buyers monetary policy. Instead, the size of the monetary by convincing them that higher domestic interest base is determined by monetary policy in the anchor rates will keep domestic inflation in check, prevent country and capital flows. The currency board a devaluation, and result in excess returns to the arrangement leaves no room for policies that are domestic currency relative to the anchor currency. inconsistent with the fixed exchange rate because In the long run, the pegging central bank must the only policy is a commitment to adjust the mone- keep domestic inflation rates close to inflation in tary base in tandem with flows of foreign exchange the anchor currency. By harmonizing the inflation reserves in and out of the central bank. As a conse- rates, the central bank prevents the real exchange quence, the home country’s central bank can no rate from appreciating to unsustainable levels at longer act as a lender of last resort to the domestic the pegged nominal exchange rate. Speculators often banking sector; thus, speculative attacks can take bet that central banks that have allowed substantial place against banks instead of the currency. appreciation of the real exchange rate through rel- Dollarization represents the unilateral decision atively high domestic inflation will choose to break to enact two formal changes.1 The first change is the peg and devalue, rather than let the domestic that all local currency in circulation plus vault cash economy stagnate for a prolonged period with a in banks is redeemed for U.S. dollars at some high, uncompetitive real exchange rate. announced conversion rate and is then destroyed. We preface our presentation of a model of The second change involves transforming all con- monetary policy mechanics by noting that monetary tracts denominated in local currency into contracts policy decisions do not strictly obey a particular in U.S. dollars at the conversion rate. Dollarization, formula or equation. Nevertheless, central banks do which has recently taken place in Ecuador, has not have to implement in a literal fashion a model received increasing attention in academic and policy of monetary policy for the model to be useful. In circles. fact, central banks often monitor such models them- Exchange rates can also be fixed through multi- selves because these models provide useful informa- lateral arrangements, although these require more tion about the rate of inflation that is likely to result coordination and negotiation than unilateral pegs. from recent policy decisions. Two multilateral systems are multilateral pegs and In our empirical model of monetary policy currency unions. In a multilateral peg, the distinc- mechanics, we assume that a pegging central bank tion between the anchor currency and the pegging adjusts the policy instrument, i (the interest rate currency becomes blurred because the participating differential), according to a forecast of the relation- countries are obligated to take monetary policy ship between the policy instrument and domestic π measures to defend the exchange rate peg. The price inflation, : prime example of a multilateral peg is the European (1) ∆Εii=+[] ∆ππ− , Monetary System prior to the adoption of a single t tt−1 tt0 t currency in January 1999. A currency union, in π where 0t is the desired inflation rate, which is pre- contrast, consists of an agreement to merge several sumably close to the rate of inflation expected in currencies to fix the exchange rates and unify their the anchor currency. Note that this use of a forecast monetary policymaking permanently. The European Monetary Union, undertaken in 1999, is the most 1 The term dollarization pertains to adopting the U.S. dollar; however, prominent currency union. another major currency could be adopted as well.

48 SEPTEMBER/OCTOBER 2001 FEDERAL RESERVE BANK OF ST.LOUIS to choose the policy instrument setting is analogous (4)ee=+−δδ−−()1 e . to setting a money-supply instrument, m in logs, ˜˜tt11 t according to a velocity forecast: Gradual rebasing of the target occurs for values of δ less than one. Small shifts in the exchange rate (2) ∆∆Ε∆∆my=−[] ym −, tt0 tt−1 are gradually accommodated into the target rate. As δ decreases from one, the rate of accommodation where y is nominal gross domestic product (GDP) increases. Because δ is an estimated parameter, the in logs and ∆y is the desired rate of nominal GDP 0t model infers a path for the exchange rate target that growth at time t. One difference is that, in the latter best explains the central bank’s policy responses formulation, the forecasted quantity, ∆y – ∆m, is a as measured by interest rate adjustments. well-known relation (velocity growth), whereas in the former the forecasted quantity, ∆i+π is not.2 Applying the Model to Austria’s Peg In either case, if policy is set according to the forecast and the forecast is correct on average, then the In order to use this model as a device to describe desired inflation or nominal GDP growth rate will monetary policy mechanics over a relatively long be achieved on average.3 sample period, it is realistic to allow some of the For a pegging central bank, we add to equation parameters to vary across time. Therefore, we make (1) two feedback terms that indicate the response several parameters subject to two-state Markov to an exchange rate gap and an inflation gap. The switching, which is a parsimonious way to introduce ∼ exchange rate gap, (e – e ), is between the actual and variation into the parameter values. For example, target exchange rate. The inflation gap, (π – π f ), is even if Austria were to harmonize its intended inflation rate with Germany’s, we would not expect between inflation in the home country and the π anchor country: Austria’s baseline inflation, 0, to be constant over the entire sample period. The German Bundesbank’s ∆Ε=+ ∆ππ− informal inflation target varied between 4.5 and 2 iit tt−1 []tt0 t (3) percent (or less) between 1975 and 1994, according +−λπ() πf +− λ()ee + ε. 1 − 2 ˜ t −1 t to von Hagen (1995). Thus, we can expect that esti- t 1 π mates of 0 for Austria will switch between two Not all of these feedback terms will be significant values that lie roughly in this range. Other param- for both Austria and Thailand, but we estimated eters are not expected to remain absolutely constant identical models for both countries to highlight the across the entire sample either. For example, the differences between their policies and not different exchange rate target will sometimes be nearly ε models of policy. An error term is added to equa- constant, (δ=1), whereas at other times it will adjust tion (3) to indicate that no central bank follows to accommodate changes in the prevailing exchange such an interest rate rule perfectly. In practice, we rate, (δ<1). Markov switching is a method that lets assume the error term has a Student t distribution economists use the data and model to infer when with n degrees of freedom to allow for occasions parameter shifts occurred, rather than impose their of large deviations between the actual and model- own judgment. Also subject to switching are the implied policy settings. The coefficients λ and λ λ λ 1 2 feedback parameters, 1 and 2, and the variance, indicate to what degree the respective gaps alter this σ 2. We use three different binary Markov state vari- period’s desired rate of inflation from the baseline ables, S1, S2, and S3, with transition probabilities, π level of 0. ( pi, qi), i=1,2,3, where pi=Prob(Sit=0|Sit–1=0) 4 The model of monetary policy must infer a and qi=Prob(Sit=1|Sit–1=1). The first state vari- π target value of the exchange rate because central able governs switching in 0. The second governs λ λ δ banks allow even strongly pegged exchange rates switching in the feedback parameters 1, 2, and . to drift a bit over long periods of time, and they do not announce precisely the extent to which the 2 An equivalent set-up to equation (1) would be to forecast ∆i+∆π to π π exchange rate target has incorporated this drift. In target the change in inflation as 0t – t–1. this model, the implicit target exchange rate that 3 Dueker and Fischer (1998) discuss the forecasting of the ratio appears in the exchange-rate gap in equation (3) is between the nominal target variable and the policy instrument. a weighted average of last period’s target and last 4 Technical details regarding the estimation procedure are in the period’s actual rate (in logs): Appendix and Dueker and Fischer (1996).

SEPTEMBER/OCTOBER 2001 49 R EVIEW

Figure 1 Table 1 Inflation Targets Parameter Estimates for Austria π Percent 0a,b 1.740 (0.612) 3.494 (0.304) 8 λ Austria’s Model-Implied Inflation Target 1a,b 00 Without Feedback from Gaps p1, q1 0.887 (0.087) 0.941 (0.048) λ 6 Germany’s Model-Implied Inflation Target 2a,b 1.124 (0.105) 0.338 (0.091) δ a,b 1 0.823 (0.084)

p2, q2 0.231 (0.269) 0.743 (0.174) 4 σ 2 a,b 0.057 (0.028) 2.121 (1.074)

p3, q3 0.948 (0.041) 0.931 (0.578) 2 1/n 0.199 (0.161) Log-likelihood –115.9

0 NOTE: Standard errors are in parentheses; p1, q1 are transition π probabilities for switching in 0; p2, q2 are transition probabili- λ λ δ ties for switching in 1, 2, ; p3, q3 are transition probabilities 2 -2 for switching in σ . 1972 1975 1978 1981 1984 1987 1990 1993

The third governs switching in σ 2, the variance of The estimates of Austria’s baseline inflation π the error term. A more parsimonious model would rates, 0a,b=(1.74, 3.49), from Table 1 are quite close tie all of these parameters to a single state variable, to the range of Germany’s informal inflation targets but it seems too restrictive to force the inflation of 4.5 to 2 percent or less.6 The unconditional value π π target to move in tandem with the rebasing of the of Austria’s 0 is 2.89. We call 0 a baseline inflation exchange rate target. rate because it would be the inflation target if both The data used to estimate the model are short- the exchange rate gap and the inflation gap were term interest rates and inflation rates for Austria zero. To assess further whether Austrian monetary and Germany, as well as the exchange rate between policy was aiming at a common rate of inflation with the Austrian schilling and the Deutsche mark. We Germany, we estimated equation (3) for Germany, λ λ with the feedback coefficients 1 and 2 set to zero. use the three-month repurchase rates for Austria π and Germany, which are the most representative The estimates of 0a,b for Germany are (0.71, 3.50), short-term interest rates. The consumer price index with an unconditional value of 2.86, which is extremely close to Austria’s 2.89. Thus, Austria’s (CPI) is the inflation measure. Our sample consists monetary policymakers revealed through their of quarterly data from 1972 to the end of 1994. interest rate instrument settings a preference for On January 1, 1995, Austria officially entered the the same inflation rate as that of Germany, even in Exchange Rate Mechanism of the European the absence of feedback from the exchange rate Monetary System, whereupon the exchange rate and inflation gaps. became part of a multilateral peg.5 Discussion of the ∆ π Figure 1 shows a plot of the probability-weighted construction of the forecasts, Et|t–1[ it+ t], and the π values of 0 for Austria and Germany and shows a likelihood function is included in the Appendix. high degree of correspondence between the two. Parameter estimates for Austria from 1972:Q2 Austria’s period-by-period inflation target, condi- through 1994:Q3 are in Table 1, where subscripts tional on the inflation and exchange rate gaps, a and b denote the pair of values of parameters π λ π π f λ ∼ equals 0t – 1t( – )t–1– 2t(e – e )t–1. subject to Markov switching. The a values correspond with the p transition probabilities, and the 5 From 1974 to 1995, the Austrian National Bank unilaterally pegged b values correspond with the q transition probabili- the Austrian schilling to the Deutsche mark. This policy was known ties. Parameter values reported as equal to either as the “hard-currency policy.” Hochreiter and Winckler (1995) discuss zero or one converged arbitrarily close to those this policy regime in detail. values and were not restricted in the estimation. 6 These results are presented in detail in Dueker and Fischer (2000).

50 SEPTEMBER/OCTOBER 2001 FEDERAL RESERVE BANK OF ST.LOUIS

Figure 2 Figure 3 Austria’s Feedback Rule Schilling/Mark Exchange Rate

Percent 7.4 8 Actual Rate

Model-Implied Target 6 7.3 Rate

4 7.2

7.1 Austria’s Model-Implied Target

0 Conditional on Feedback from Gap

German Inflation Rate 7 -2 1972 1975 1978 1981 1984 1987 1990 1993 1972 1975 1978 1981 1984 1987 1990 1993

λ Since 1 equals zero in both states, Austria’s The Model of Peg Mechanics Applied monetary policy took feedback from the exchange to Thailand rate only. One conclusion we can draw is that strong feedback from the inflation gap is not necessary Among the East Asian countries that were for a peg to succeed, provided that the pegging forced to break an exchange rate peg between 1997 country has chosen the same baseline inflation and 1998, Thailand was the first, and perhaps the π rates, 0, as the anchor country. most surprising, to devalue. Prior to 1997, Thailand Figure 2 shows Austria’s period-by-period infla- had maintained one of the tightest and most long- tion target plotted against the actual rate of inflation standing pegs in Asia. To understand the mechanics in Germany calculated as the change in the CPI in behind Thailand’s peg of the Thai baht to the U.S. the four most recent quarters. This chart suggests dollar, we estimate equation (3) for Thailand and that Austria imported inflation from Germany during compare the results with Austria. As with Austria, the two peaks in German inflation, the first in 1975 we used the three-month interest rate and the CPI and the second in 1982. German inflation influenced along with the exchange rate. For Thailand, however, Austrian monetary policy through the exchange ∼ we used monthly data from January 1990 through rate because e – e tended to be negative when German inflation was high. Figure 3 presents the June 1997, one month before the peg was broken. ∼ Table 2 reports the parameter estimates: these model-implied exchange rate target, e , and the π actual exchange rate.7 In studying Figure 3, one show that Thailand’s baseline inflation rate, 0t , must keep the scale in mind because the schilling had an unconditional probability-weighted value fluctuated in a relatively narrow band throughout of 6.5, which is well above the average level of U.S. these 20 years. For most of the period, the exchange inflation for that period, 3.1 percent. Hence the only rate gap was negligible; therefore, Austrian monetary way that Thailand’s period-by-period inflation target policy focused on keeping its inflation rate close to could remain close to the U.S. rate would be through π 0, which Austrian policymakers had chosen to be feedback from the inflation and exchange rate gaps. close to Germany’s inflation target. Nevertheless, In contrast, Austria’s baseline inflation rate closely the magnitude and significance of the feedback matched the corresponding rate in Germany. Figure λ coefficients on exchange rate gaps, 2 in Table 1, 4 shows that Thailand’s inflation rate consistently indicate that Austrian monetary policy remained poised to act decisively to close any exchange rate 7 In the graph the rates appear in levels, but they enter equation (3) in gap that developed. logarithms × 100.

SEPTEMBER/OCTOBER 2001 51 R EVIEW

Figure 4 Table 2 Year-Over-Year Inflation Rates of Thailand Parameter Estimates for Thailand and the United States Monthly Data 1990:01 to 1997:06

Percent π 0a,b 3.863 (2.102) 15.426 (3.882) 8 p , q 0.851 (0.139) 0.501 (0.018) Thailand United States 1 1 λ 1a,b 0 1.415 (1.422) 7 λ 2a,b 0 1.022 (0.532) δ a,b 0.514 (0.074) 1 6 p2, q2 0.253 (0.368) 0.429 (0.264) σ 2 2.917 (26.8) 2.993 (0.889) 5 a,b p3, q3 0.207 (1.216) 0.998 (0.163)

4 1/n 0 Log-likelihood –197.1 3 NOTE: Standard errors are in parentheses; p1, q1 are transition π probabilities for switching in 0; p2, q2 are transition probabili- 2 λ λ δ ties for switching in 1, 2, ; p3, q3 are transition probabilities 1990 1991 1992 1993 1994 1995 1996 1997 for switching in σ 2. exceeded the inflation rate in the United States, but discussion that follows centers on why Thailand’s by less than 3.4 percent (6.5 –3.1), because of feed- policy feedback coefficients became more volatile back from the inflation gap. Parameters p2 and q2 starting in mid-1995. are the transition probabilities for switching in the The exchange value of the U.S. dollar—to which λ γ feedback coefficients, 1 and 2, and both show the baht was pegged—reached a record low in May very little persistence. In fact, since p2+q2<1, the 1995 against the Japanese yen, at which time the feedback coefficients show negative serial correla- dollar was also weak against other major currencies. tion, which implies oscillatory behavior in the Prior to May 1995, the dollar had depreciated con- period-by-period inflation target, sistently against the yen since early 1990 (as shown in Figure 7). Since Japan is both a major trading πλππ−−US −− λ (5) 01tt()2t ()ee . t −1 ˜ t −1 partner and a rival exporter with Thailand, the baht- dollar peg was able to sustain a rising real exchange For Thailand, the feedback coefficients λ and λ 1b 2b rate with the dollar during the period that the yen imply strong responses to inflation and exchange was appreciating against the dollar.9 In May 1995, rate gaps. For Austria, the feedback coefficients however, the dollar-yen exchange rate peaked and display no serial correlation—either positive or the real exchange value of the yen began to depre- negative—because p +q is essentially equal to 2 2 ciate against the dollar. To remain competitive in one; moreover, feedback from the gaps does not international markets, Thailand felt compelled at play an important role in determining Austria’s this juncture to prevent further appreciation of the interest rate. Figure 5 shows that—after 1995 especially— real exchange value of the baht relative to the dollar. Thailand’s period-by-period inflation target, which Clearly, it would have been difficult for Thailand is conditional on feedback from the gaps, appears if the real exchange value of the baht had been to inherit negative serial correlation from switch- expected to continue to increase relative to the ing in the feedback coefficients. Figure 6 plots the 8 posterior probability of the high-feedback state The posterior probability is the probability of a state at time t conditional on the data up to and including time t. and confirms that the fluctuation in the probability 9 from month to month went from a relatively narrow The real exchange rate rises for Thailand if the inflation rate is greater in Thailand than in the United States and the nominal exchange range, between 30 percent and 60 percent prior to rate (expressed in baht per dollar) does not increase by an equal mid-1995, to a much greater range thereafter.8 The magnitude.

52 SEPTEMBER/OCTOBER 2001 FEDERAL RESERVE BANK OF ST.LOUIS

Figure 5 Figure 6 Thailand’s Feedback Rule Thailand: Posterior Probability of the High- Feedback Coefficients on the Gaps Percent 25 Percent Thailand’s Model-Implied Inflation Target Conditional on Feedback from Gaps 1 20 U.S. Inflation Rate 0.8 15

0.6 10

0.4 5

0 0.2

-5 0 1990 1991 1992 1993 1994 1995 1996 1997 1990 1991 1992 1993 1994 1995 1996 1997 dollar at a time when the real exchange value of Figure 7 the dollar was rising relative to the world’s other major currencies. Yen/U.S. Dollar Exchange Rate

One key aspect of the credibility of an exchange 160 rate peg is whether the market believes that the pegging country’s economy remains competitive internationally, given any appreciation of its real exchange rate that has taken place during the peg. 140 Thailand’s appreciating real exchange value relative to the dollar may have appeared sustainable during a period when many of the world’s other major currencies were appreciating relative to the dollar, 120 but not when this course reversed. For this reason, it is not surprising that Figure 5 shows that Thailand’s period-by-period inflation target was kept centered 100 on a mean closer to the U.S. inflation rate after mid- 1995. An obvious question, however, is why the inflation target was so volatile around this lower mean. The answer probably lies in the extreme 80 inflows of foreign capital that Thailand was receiving 1990 1991 1992 1993 1994 1995 1996 1997 at the time. On one hand, raising the short-term interest rate helped to reduce domestic demand and inflation. On the other hand, high interest rates quences of the huge capital inflows could explain helped spur additional flows of foreign capital to the apparent stop-go behavior of Thailand’s mone- Thailand in search of high returns. In fact, the tary policy after mid-1995. Such a balancing act— amount of foreign capital that flowed to Thailand the rapid fluctuation of the feedback coefficients in 1996 was massive, at a level equal to 13 percent after 1995, shown in Figure 6—was not a sustainable of GDP (Grenville, 2000, p. 6). The tension between policy for the long run. By July 1997, speculators wanting to control domestic demand and inflation had broken the exchange rate peg. Halcomb and in the short run and worrying about the conse- Marshall (2000) review evidence that Thailand’s

SEPTEMBER/OCTOBER 2001 53 R EVIEW

Figure 8 Figure 9 Baht/U.S. Dollar Exchange Rate Short-Term Interest Rates in Thailand and the United States

27 Percent Actual Rate 18 Thailand Model-Implied Target Rate 16 United States

26 14

10 25 8

4 24 1990 1991 1992 1993 1994 1995 1996 1997 2 1990 1991 1992 1993 1994 1995 1996 1997 devaluation of the baht in July 1997 was not widely squeezing speculators who tried to take short posi- anticipated in financial markets. They observe that tions in baht by imposing high interest rates and the timing of a currency crisis can be difficult to pressure on domestic banks not to lend to off-shore predict, even if one knows that a peg is not on solid currency traders (Halcomb and Marshall, 2000). footing for the long run. In the face of such massive capital inflows, it SUMMARY AND CONCLUSIONS seems apparent in hindsight that Thailand proba- Our empirical results for Austria’s successful bly should not have maintained such a hard peg. exchange rate peg highlight the importance the Instead, the monetary policy authority could have Austrian National Bank placed on consistently signaled by mid-1995 a greater degree of flexibility maintaining Austria’s inflation rate close to that with respect to adjusting the peg. Indeed, the Bank of Germany. In so doing, Austria prevented the of Thailand now practices inflation targeting with real exchange value of the schilling vis-a-vis the a floating exchange rate (Sonakul, 2000, p. 2). Deutsche mark from drifting far from its initial Figure 8 shows the baht-dollar exchange rate along ∼ value. Furthermore, the Austrian economy had with the model-implied target rate, e . This chart enough in common with the German economy suggests that the Bank of Thailand allowed the baht that the Austrian National Bank was willing to let to depreciate by about 4 percent in the 18 months the real exchange value of the schilling experience prior to July 1997. Clearly this rate of depreciation the vicissitudes in the real exchange value of the was not enough to counteract the large interest Deutsche mark against other major currencies. rate differential shown in Figure 9. The size of the One lesson for pegging countries is that they ought interest rate differential between Thailand and the to behave like assiduous inflation targeters even United States in the early part of 1995 suggested when there is no pressure on the exchange rate. The that the Bank of Thailand might have signaled a key is that the inflation target should be the same willingness to let the baht depreciate at a rate of inflation target used in the anchor country because about 5 percent per year. Such a rate of expected the nominal exchange rate can no longer move to depreciation also might have helped alleviate the correct an overvalued real exchange rate. Feedback capital inflows by discouraging domestic borrow- from the inflation and exchange rate gaps did not ers from taking dollar-denominated loans. Instead, appear to play an important role in Austria’s success- the Bank of Thailand chose to defend the peg by ful peg, given that Austria followed Germany’s infla-

54 SEPTEMBER/OCTOBER 2001 FEDERAL RESERVE BANK OF ST.LOUIS tion target closely even before gaps developed. A REFERENCES second lesson is to take care in choosing an anchor currency because the major currencies experience Calvo, Guillermo A. and Reinhart, Carmen M. “Fixing for wide swings against one another. It makes no sense Your Life.” Working Paper 8606, National Bureau of to tie one’s currency to the dollar if the fluctuations Economic Research, November 2000. in the exchange value of the dollar against other major currencies are difficult to withstand. Corsetti, Giancarlo; Pesenti, Paolo and Roubini, Nouriel. Both of these lessons appear to apply to “What Caused the Asian Currency and Financial Crisis?” Japan and the World Economy, 1999, 11, pp. 305-73. Thailand’s peg to the U.S. dollar. The Bank of Thailand allowed the domestic inflation rate to Dueker, Michael J. and Fischer, Andreas M. “Austria’s Hard- exceed the U.S. inflation rate prior to mid-1995, Currency Policy: The Mechanics of a Successful Exchange based on the depreciation of the U.S. dollar against Rate Peg.” Working Paper No. 2479, Centre for Economic other major currencies, principally the Japanese yen. Policy Research, March 2000. In fact, the estimates of Thailand’s baseline inflation rate were more than twice the average U.S. inflation ______and ______. “A Guide to Nominal rate. If the Bank of Thailand truly had a long-term Feedback Rules and Their Use for Monetary Policy.” commitment to pegging its currency to the dollar, it Federal Reserve Bank of St. Louis Review, July/August would not have tried to take advantage of the depre- 1998, 80(4), pp. 55-63. ciation in the dollar against the yen by inflating. This policy led to trouble when the U.S. dollar began to ______and ______. “Inflation Targeting in a Small appreciate against the yen in the second half of Open Economy: Empirical Results for Switzerland.” Journal 1995. At this point, Thailand’s policy response to of Monetary Economics, February 1996, 37, pp. 89-103. the inflation gap between Thailand and the United States was strong, but it was not implemented con- Flood, Robert P. and Rose, Andrew K. “Understanding sistently. The model estimates reveal unstable, Exchange Rate Volatility with the Contrivance of oscillatory behavior in the feedback from the infla- Macroeconomics.” Economic Journal, November 1999, tion gap, probably due to the tension between the pp. F660-72. desire for high interest rates to control inflation and concern for the size of the capital inflows that Grenville, Stephen. “Exchange Rate Regimes for Emerging high interest rates were attracting. In these circum- Markets.” Bank for International Settlements Review, No. stances, it would have been exceedingly difficult 97, 3 November 2000, pp. 5-14. for inflation in Thailand to undershoot the U.S. inflation rate by a significant margin. The Bank of Halcomb, Darrin and Marshall, David. “A Retrospective on Thailand might have fared better by announcing the Asian Crisis of 1997: Was it Foreseen?” Chicago Fed gradual depreciation of the nominal exchange rate, Letter, January 2001. starting in mid-1995, before speculators began to apply their own pressure. Since the crisis in 1997- Hausmann, Ricardo; Panizza, Ugo and Stein, Ernesto. “Why Do Countries Float the Way They Float?” Working Paper 98, the Bank of Thailand has announced a new 418, Inter-American Development Bank, May 1999. inflation-targeting regime in place of an exchange rate peg. The Bank of Thailand believes that the Hochreiter, E. and Winckler, G. “The Advantage of Tying new regime will be less prone to boom and bust Austria’s Hands: The Success of the Hard Currency cycles than was the peg to the dollar (Sonakul, 2000). Strategy.” European Journal of Political Economy, 1995, Thus, Thailand is one emerging market that has 11, pp. 83-111. decided that it can find greater stability by promising low inflation than by promising a particular Sonakul, M.R. Chantu Mongol. “Inflation Targeting—A New exchange rate. Time will tell whether the disadvan- Monetary Policy Approach for Thailand.” Bank for tages of floating exchange rates to emerging markets International Settlements Review, No. 106, 27 November will weigh as heavily as Calvo and Reinhart (2000) 2000, pp. 1-2. suggest. What is clear from the results presented here is that Thailand’s exchange rate peg prior to Von Hagen, Juergen. “Inflation and Monetary Targeting in July 1997 never had the strong underpinnings that Germany,” in Leonardo Leiderman and Lars E.O. Svensson, sustained Austria’s peg to the Deutsche mark. eds., Inflation Targets. London: CEPR Press, 1995.

SEPTEMBER/OCTOBER 2001 55 R EVIEW

Appendix The forecasts for equation (3) are taken from about the relationships among the variables. More- a model that allows for two types of uncertainty. over, the time-varying structure of the forecasts The first arises from heteroscedasticity in the allows it to adapt to structural breaks in the rela- error terms. This is modeled by a Markov switch- tionships between the dependent and explanatory ing process, which tries to match the persistence variables. of periods of high and low volatility in the data. The maximum-likelihood estimates reported The second source of uncertainty arises as econ- in Tables 1 and 2 are the result of estimating the omic agents are obliged to infer unknown or following density function, which includes three changing regression coefficients. Markov state variables denoted S1, S2, and S3, The model generating the forecasts is where Yt–1 is all information available through time t –1: ∆ + π = (A1) ()itt ββ++++∆∆ βπβ (A2) 01ttttttttieu−− 1213 − 1 , T ∼ ∑ ut Normal (0,ht). ln t =1 h =v2+(v2 – v2)R , t 0 1 0 t  1 1 1 () ∑ ∑ ∑ === ijk,, where Rt is 0 or 1  Prob()SiSjSkYL123ttttt , , −1  .  i=0 j=0 k=0  Probability (Rt=0|Rt–1=0)=r1

Probability (Rt=1|Rt–1=1)=r2. The Student t densities are

Variable i is the interest rate differential, π is con- ()ijk,, (A3) lnL = ln Γ ()05.n()+ 1 sumer price inflation, and e is the exchange rate t in logs. The time-varying coefficients assume that the Γ ()− πσ2 –ln05..n 05 ln ()n = k β S3t state variables, t, follow a random walk process: β β υ  ε 2  t= t 1+ t == –  + SiSj12tt,  υ ∼ –0.5 n+1) ln1 2 , t Normal (0,Q).  nσ =  Sk3t The random walk assumption suggests that agents need new information before changing their views and Γ is the gamma function.

56 SEPTEMBER/OCTOBER 2001