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Russian Federation

Author(s):
International Monetary Fund. European Dept.
Published Date:
October 2013
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The Exchange Rate vs. Interest Rate Volatility Trade-Off: the Role of Inflation Targeting1

  • This chapter analyzes the impact of the adoption of inflation targeting (IT) on exchange rate volatility. The experience in more than a dozen inflation-targeting countries suggests that after controlling for the exchange rate regime, the adoption of IT has been often associated with a reduction in exchange rate volatility. Overall, the adoption of IT tends to partly or fully offset the increase in exchange rate volatility associated with the adoption of a floating exchange rate regime, which is usually followed by inflation-targeting countries. There are two main channels for the volatility-reducing effect of IT: (1) IT helps reduce unexpected shocks by making monetary policy transparent and predictable; (2) the introduction of IT tends to reduce pass-through from the exchange rate to domestic prices.

A. Introduction

1. Theory does not suggest a definite prediction about the relationship between exchange rate and interest rate volatility. There is widely believed to be a trade-off, with higher interest rate volatility resulting from a monetary policy geared towards stabilizing the exchange rate, in particular if there is a high pass-through from exchange rates to prices. Indeed, Mohanti and Klau (2004) find support for the “fear of floating” hypothesis in emerging economies, in as much as the exchange rate features prominently in monetary policy makers’ reaction function, with interest rates responding strongly to exchange rate changes. Another source of interest rate volatility, however, would be unanchored inflation and exchange rate expectations, reflecting a low degree of monetary policy credibility. Thus, the degree of interest rate volatility depends on both the policymakers’ reaction function and their degree of credibility (Calvo and Rheinhard 2002).

2. The role of the exchange rate for macroeconomic performance is generally higher in emerging and transition countries than in advanced economies. It has a higher impact on inflation, trade, economic activity, and financial stability, due to a generally higher degree of pass-through of the exchange rate to inflation; a stronger effect on the competitiveness of the tradable sector owing to a less diversified and advanced production base; and higher foreign exchange exposures in the financial, corporate, and household sectors. For a discussion of emerging economies’ experiences with alternative exchange rate and monetary policy regimes, see Frankel (2002); Mohanti and Klau (2004); Ca’Zorzi, Hahn, and Sánchez (2007); and Ostry, Ghosh, and Chamon (2012), among others.

3. Historically, most emerging economies have used the exchange rate as the nominal anchor and adopted rigid exchange rate regimes—more or less firm pegs or at least strongly managed exchange rates. Since the 1990s, however, many countries have moved towards more flexible exchange rate regimes. In many cases, this development has coincided with the adoption of inflation targeting (IT) monetary policy regimes, with the latter relying on short-term interest rates as the main monetary policy instrument.

4. The coincidence of IT and flexible rates has provoked considerable controversy over the “fear of floating”: Some degree of exchange rate flexibility is a requirement for a well-functioning IT regime, because in a world of capital mobility, independent monetary policy is incompatible with a pegged exchange rate regime. This connection between IT and exchange rate flexibility has led some authors to argue that since IT requires a floating exchange rate regime, it necessarily results in higher exchange rate volatility. Thus, higher exchange rate volatility would be the price for achieving lower interest rate volatility. It is important, however, to separate the effects of IT, on the one hand, and of a more flexible exchange rate regime, on the other, on exchange rate volatility (Edwards, 2006).

5. This paper examines the interrelation of exchange rates, price levels, and IT via two routes: First, what has been the impact of adopting IT on the volatility of exchange rates? Second, how has the adoption of IT impinged on the magnitude of pass-through from exchange rates to domestic inflation? To this end, it reviews the experiences in a large number of inflation targeting countries, both advanced and emerging economies.

6. Gali and Monacelli (2005) find a trade-off between the stabilization of the nominal exchange rate and terms of trade on the one hand, and the stabilization of domestic inflation and the output gap on the other hand: domestic inflation targeting implies a substantially greater volatility in the nominal exchange rate and terms of trade than it would be the case under alternative policy regimes. Demiroz (2001) finds negative cross relationships between the volatilities of foreign exchange and interest rates in Turkey for the period 2000–2001. Duarte et al. (2008) find a trade-off between exchange rate volatility and interest rate differential volatility in Portugal during participation in the Exchange Rate Mechanism of the European Monetary System.

7. Other studies have not found a trade-off between exchange rate and interest rate volatility: Sarno (1997) concludes that the greater exchange rate stability in the European Monetary System did not generate any “volatility transfer” onto interest rates (and even finds some reduction in interest rate volatility in some countries). Schmidt-Hebbel and Tapia (2002) suggest that the volatility of nominal exchange rates has been no higher under inflation targeting than in other countries with floating exchange rate regimes. Edwards (2006), covering seven countries, shows that IT did not result in an increase in exchange rate volatility, emphasizing that IT helps reduce unexpected shocks by making monetary policy transparent and predictable. Prasertnukul, Kakinaka, and Kim (2008) come to similar results examining four Asian economies (Indonesia, Korea, the Philippines, and Thailand).

8. Finally, Russ (2011) finds positive correlation between exchange rate volatility and interest rate volatility—with an increase in monetary volatility increasing exchange rate volatility. Studying the case of Turkey, Berument and Gunay (2003) examine the effect of exchange rate risk on interest rates, and find a positive relationship for the period December 1986 to January 2001.

9. The relationship between exchange rate, interest rate, and inflation volatilities depends not least on the degree of pass-through from exchange rate changes to domestic prices. The degree of inflationary inertia plays an important role in determining the magnitude of pass-through. Studying 25 OECD countries, Campa and Goldberg (2002) find that pass-through tends to be lower for countries with low inflation as well as low exchange rate variability. Examining 20 industrial countries, Gagnon and Ihrig (2004) also suggest that stabilizing inflation reduces the pass-through. Consistent with these results, Choudri and Hakura (2006), covering 71 countries, find a positive relation between pass-through and the average inflation rate. Prasertnukul, Kakinaka, and Kim (2008) indicate that the degree of pass-through declined with the adoption of IT for either the PPI or the CPI in Korea and Thailand; while Ito and Sato (2006) find that pass-through to the consumer price index (CPI) is relatively low compared with that to the producer price index (PPI) in Indonesia, Thailand, Malaysia, Singapore, and South Korea.

10. The following empirical investigation confirms that while the move to a floating exchange rate regime can be expected to increase exchange rate variability, there is some evidence that the adoption of IT has reduced exchange rate volatility in many cases. This is consistent with the empirical results of Edwards (2006), Rose (2007), and Prasertnukul, Kakinaka, and Kim (2008), contradicting the model-based simulations by Gali and Monacelli (2005). Empirically, there is evidence that the volatility-increasing effect of moving to a more flexible exchange rate regime and the volatility-reducing effect of moving to IT by and large cancel each other out in many countries. Hence, IT could be effective in stabilizing price levels and lowering inflation volatility through both reduced exchange rate pass-through and reduced exchange rate volatility.

B. Empirical Analysis

11. In a first step, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to examine the effect of IT on exchange rate volatility. In a second step, we analyze how the introduction of IT impacts the exchange-rate pass-through. In both cases, we follow the approaches taken by Edwards (2006) or Prasertnukul, Kakinaka, and Kim (2008). Model specifications are discussed in Appendix I. Figure 1 gives an overview of NEER volatility in periods before and after adoption of flexible exchange rate regimes and inflation targeting. IT adoption dates are taken from Rogers (2009).

Figure 1.Nominal Effective Exchange Rates (log differences), Exchange Rate Regime, and Inflation Targeting

C. Exchange Rate Regime, Inflation Targeting, and NEER Volatility

12. The observation period for GARCH estimations is January 1990 to December 2007, using monthly data. Periods of hyperinflation (e.g., Peru 1990–92) are omitted in the regressions, as is the more recent period 2008–12 in order to avoid the major disturbances during the global financial crisis. Crisis dummies were employed in individual equations to take account of various regional crises (Finland 1991–93; EMS crisis 1992–93; Mexico 1994; East Asia 1997–98, etc.). We focus our attention on two dummy variables, DIT and FLT, which are included in the variance equation to capture the impact of IT and floating exchange rate regimes, respectively. Estimation results are summarized in Table 1.

Table 1.GARCH Estimates of Floating Rate and Inflation Targeting Regime Dummies
FLTDIT
BRA4.27 ***−3.23 ***
0.0010.010
CHL2.53 ***−2.21 **
0.0000.038
COL0.25−0.59
0.7950.548
FIN3.90 ***−3.59 ***
0.0000.000
GBR0.450.13
0.5730.789
IDN0.520.22
0.2790.742
ISR1.10 ***−1.28 ***
0.0030.003
KOR5.59 ***−5.44 ***
0.0000.000
MEX1.41 ***−1.08 ***
0.0000.000
POL−1.50 ***1.39 ***
0.0000.000
THA3.36 ***−3.19 ***
0.0000.000
TUR2.27 ***−0.32
0.0000.395
CAN0.28
0.883
PER−0.22
0.643
PHL−0.07
0.823
Notes: ***, **, and * denote significance on the 1 percent, 5 percent, and 10 percent level, respectively. Value of z-statistics in italics. FLT is a dummy for periods with floating exchange rate regimes, and DIT is a dummy for periods with inflation targeting. Canada, Peru, and Philippines had (de facto) floating exchange rate regimes throughout the observation period (1990M1 to 2007M12, with the exception of Peru: 1992M1 to 2007M12).
Notes: ***, **, and * denote significance on the 1 percent, 5 percent, and 10 percent level, respectively. Value of z-statistics in italics. FLT is a dummy for periods with floating exchange rate regimes, and DIT is a dummy for periods with inflation targeting. Canada, Peru, and Philippines had (de facto) floating exchange rate regimes throughout the observation period (1990M1 to 2007M12, with the exception of Peru: 1992M1 to 2007M12).

13. Unsurprisingly, the introduction of a floating exchange rate regime has increased exchange rate volatility in almost all observed countries. (The FLT dummy was omitted in the cases of Canada, Peru, and Philippines, which had floating rate regimes throughout the observation period). The exception is Poland, which experienced a period of significant macroeconomic stabilization prior to the implementation of floating rates. The effect on exchange rate volatility is generally larger in emerging market economies, while it is insignificant in the case of most advanced economies.

14. The sign of the inflation targeting dummy, on the other hand, is negative in eleven out of 15 countries, and significantly so in seven cases. Again with the exception of Poland, all other positive coefficient values (CAN, GBR, and IDN) are insignificant. Interestingly, the DIT coefficient values are broadly in the same range as the FLT coefficient values, suggesting that the introduction of IT tends to compensate for most of the higher exchange rate volatility induced by moving to floating exchange rate regimes.

15. Presumably, the adoption of IT reduces exchange rate volatility only over time, as the monetary authorities gradually obtain policy credibility through building a track-record of inflation-fighting credentials. Therefore, estimation results for the IT dummy might be stronger if we would allow for a lagged instead of coincident impact of this variable. However, such lags would likely differ widely between countries, depending on the degree of monetary policy credibility already obtained at the time of the official announcement of the move towards IT, and the choice of lag may remain somewhat arbitrary in a cross-country context. Ideally, any gradual strengthening of the IT impact might be discernible via rolling GARCH estimations.

16. Like Russia, many countries which already have adopted IT are exporters of natural resources (for example, Australia, Canada, Indonesia). This potentially introduces a (spurious) correlation between exchange rate volatility and IT adoption. In principle, this could be dealt with by controlling for natural resource intensity—however; such a variable would be largely time-invariant and therefore not suited for inclusion in a time-series context. Instead, we capture this issue by including changes in the oil price as a control variable in the model—with the rationale that in natural resource-intensive economies, macroeconomic variables such as exchange rates, money, and GDP are closely linked to natural resource price developments.

D. Exchange Rate Regime, Inflation Targeting, and Exchange Rate Pass-Through

17. This section examines in how far the adoption of inflation targeting is likely to affect the effectiveness of nominal exchange rates as shock absorbers. The consumer price index (CPI) is used as a proxy for the domestic price of nontradables, and producer prices (PPI) as a proxy for the domestic price of tradables. Hence, the ratio PPI/CPI would be a measure of the real exchange rate. The observation period is generally 1985Q2 to 2012:Q4, or shorter for some countries (1991:Q1 for Romania; 1991:Q2 for Brazil; 1993:Q2 for Czech Republic).

18. The magnitude of pass-through has implications not only for domestic inflation, but also for the effectiveness of the nominal exchange rate as a shock absorber (Edwards, 2006). Regarding the first notion, a lower pass-through is welcome as it reduces external inflation pressures. In the latter context, it is important to distinguish between tradables and nontradables prices, and the impact of nominal exchange rate changes on the real exchange rate. While a high pass-through to nontradables reduces the effectiveness of the nominal exchange rate as a shock absorber, a high pass-through for tradables will enhance its effectiveness.

19. Pass-through tends to be much lower in advanced countries with low and stable inflation than in emerging markets with historically high inflation (Table 2). In most countries, the pre-IT short-term pass-through coefficient in the CPI and PPI equations is positive, and unsurprisingly it is usually higher for tradables (PPI) than nontradables (CPI). In the pre-IT period, due to a significant degree of inflation inertia, long-term pass through is also generally higher than short-term pass-through. The interaction terms ΔlnE_t×DIT are not significant in most cases, suggesting that short-term pass-through has not been substantially affected by the adoption of inflation targeting. The estimated coefficients for the interaction term ΔlnP_(t-1)×DIT are mostly negative, but significant only in the CPI equations. This suggests that in many countries long-run pass-through to non-tradable prices (and thus inflation inertia) has declined in the post-IT period.

Table 2.Short-Run and Long-Run Exchange Pass-Through Before and After IT
CPIPPI
PRE-FLTPost-ITPRE-FLTPost-IT
SRLRSRLRSRLRSRLR
BRA0.890.970.050.110.891.020.240.75
HUN0.120.190.060.040.070.060.060.04
CAN0.000.01−0.01−0.010.090.100.300.32
FIN0.040.09−0.08−0.090.150.250.170.27
AUS0.050.120.020.020.070.100.140.14
NZL0.080.150.030.030.060.140.150.21
ISR0.320.400.030.030.380.460.080.09
KOR0.020.030.020.010.100.180.070.09
MEX0.420.840.030.020.480.880.140.17
NOR0.140.290.040.030.440.370.310.26
CZE0.080.17−0.04−0.040.040.09−0.05−0.07
ROM0.190.56−0.01−0.010.450.510.060.04
TUR0.861.000.931.180.861.010.951.21
GBR0.150.17-0.04-0.030.130.290.020.02

20. The results obtained above are broadly confirmed by estimations of the pass-through using the impulse-response functions derived from a VAR model that is estimated separately for the pre-IT and post-IT periods in various countries. Following Ito and Sato (2006), we estimate a 5-variable VAR model with global inflation (proxied by ΔlnUS PPI); output gap (GAP); broad money growth (ΔlnM2); changes in the nominal effective exchange rate (ΔlnNEER); and domestic inflation (Δlnp), in this ordering. The VAR is also estimated separately using the proxies for nontradable prices (CPI) and tradable prices (PPI). Results are presented in Appendix II.

21. As in the previous regressions, the pass-through from the nominal effective exchange rate (1) is generally higher for the PPI than for the CPI, and (2) decreases after the adoption of an inflation targeting regime. Furthermore, (3) the decline in exchange rate pass-through is mostly larger for the CPI than for the PPI. In several countries, however, the exchange rate pass-through to the PPI is more pronounced in the post-IT period compared to the pre-IT period.

E. Conclusions

22. This chapter examined the potential trade-off between interest rate volatility and exchange rate volatility in the context of the move from an exchange rate-targeting to an inflation targeting monetary policy regime. The impact of inflation targeting on exchange rate volatility was analyzed by studying the experiences in more than a dozen countries that have adopted inflation targeting.

23. The introduction of IT appears to reduce exchange rate volatility in most countries: After controlling for the exchange rate regime, the adoption of IT has reduced nominal exchange rate volatility in many cases. Overall, the adoption of IT tends to partly or fully offset the (mechanical) increase in exchange rate volatility associated with the adoption of a floating regime.

24. There are two main channels for the volatility-reducing effect of IT: (1) IT helps reduce unexpected shocks by making monetary policy transparent and predictable, which serves to reduce both exchange rate and interest rate volatility; (2) the introduction of IT tends to reduce pass-through from the exchange rate to domestic prices. Regarding the latter, IT appears to also have a positive impact on the effectiveness of the nominal exchange rate as a shock absorber, as the reduction in pass-through is generally larger for nontradables than tradables prices. Furthermore, if IT reduces pass-through and exchange rate volatility, it may help not only to stabilize price levels but also mitigate the volatility of domestic prices.

25. Russia has seen an increase in exchange rate volatility in the past few years compared to the period before the global financial crisis, as the authorities have chosen to allow a higher degree of ruble exchange rate flexibility in preparation for the adoption of IT. The average of the 12-month coefficient of variation of the ruble/dollar exchange rate has increased from 2.2 percent in the period December 2005–September 2008 to around 3.7 percent in the period March 2010–June 2013.2 Since the pass-through from the exchange rate to domestic prices remains substantial—around 0.25 in the short term and 0.5 in the long term—this increased exchange rate volatility could translate into higher volatility of domestic inflation, thus complicating the conduct of monetary policy. Swiftly putting in place the remaining building blocks of the operational framework for IT and quickly building policy credibility in the context of the new monetary regime will be of utmost importance to reap the pass-through-reducing and volatility-moderating benefits of adopting IT by 2015, as currently planned.

Appendix I.Model Specifications

In a first step, a GARCH model of the following specification is used to analyze the impact of IT on exchange rate volatility.

where Et is the nominal effective exchange rate; the xj’s represent other explanatory variables that could impact the nominal effective exchange rate (domestic inflation; US inflation; oil price changes; interest rate differential domestic/US; crisis dummy variables); the £t is a disturbance with the properties of zero mean and conditional variance σt2;

and the yk’s are the other control variables that affect exchange rate volatility: two dummy variables, DIT and FLT, are included in the variance equation to capture the inflation-targeting and floating exchange rate regimes, respectively.

In a second step, we analyze the impact of IT on the exchange rate pass-through from changes in the nominal exchange rates to domestic prices, using the following model:

where Pt, Et and Pt*

denote a domestic price index (consumer or producer price index), the nominal effective effective exchange rate, and the index of foreign prices, respectively. The xt’s are other control variables expected to capture a change in price levels (lagged consumer price inflation; US producer price inflation as a proxy for world inflation; output gap measure based on industrial production or GDP; monthly change in the global oil price average); and £t is a disturbance term with standard characteristics. All variables but the dummies are in logarithms.

The model is estimated using OLS. The choice of exchange rate is endogenous, so the estimations likely suffer from simultaneity bias. However, given that there don’t seem to be viable instruments for the exchange rate, simultaneous equation methods such as two-stage least squares and generalized method of moments will not be able to address this issue satisfactorily (see Meese and Rogoff (1983) and Edwards (2006)).

DIT and FLT dummies are again used to capture the effects of IT and floating exchange rate regimes. Short-run and long-run pass-through coefficients are derived as follows:

Alternatively, the exchange rate pass-through is estimated using a 5-variable VAR model (separately for pre-IT and post-IT periods) with the following variables: global inflation (proxied by ΔlnUS PPI); output gap (GAP); broad money growth (ΔlnM2); changes in the nominal effective exchange rate (ΔlnNEER); and domestic inflation (Δlnp).

Appendix II.Impulse-Response Functions NEER to CPI and PPI, Pre- and Post-IT

References

Prepared by Holger Floerkemeier (EUR).

The volatility of short-term interest rates, on the other hand, has seen a marked reduction, with the average 12-month coefficient of variation declining from 32 percent to below 13 percent in the same time periods.

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