Evidence of risk premiums in emerging market carry trade currencies

J. Int. Financ. Markets Inst. Money xxx (2016) xxx–xxx

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Journal of International Financial Markets, Institutions & Money j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i n t fi n

Evidence of risk premiums in emerging market carry trade currencies Marcelo Bittencourt Coelho dos Santos 1, Marcelo Cabus Klotzle ⇑, Antonio Carlos Figueiredo Pinto 1 Pontifical Catholic University of Rio de Janeiro, Department of Business Administration, Rua Marquês de São Vicente, 225 – Gávea, Rio de Janeiro, RJ 22451-900, Brazil

a r t i c l e

i n f o

Article history: Received 4 February 2015 Accepted 27 April 2016 Available online xxxx JEL classification: F30 F31

a b s t r a c t This paper studies the evidence of risk premiums in emerging market carry trade currencies. We verified evidence of a forward bias puzzle and the presence of risk premium for all currencies. Furthermore, unanticipated shocks are of greater influence than fundamental variables in explaining long-term (permanent) risk-premium volatility components. On the other hand, in moments of global market uncertainty related to speculative pressures, the short-term (transitory) risk-premium volatility component increases. Ó 2016 Elsevier B.V. All rights reserved.

Keywords: Interest rate parity Risk premium Exchange rate CGARCH-M Emerging markets

1. Introduction The relationship between interest rate differentials and expected currency depreciation is called uncovered interest parity, as forward markets are not used to hedge positions. Thus, uncovered interest parity implies that the interest rate differential is an estimate of the future spot exchange rate change. If expectations are rational, then this estimate of future exchange rate changes provided by the interest rate differential should be unbiased. As such, unbiasedness is usually tested by regressing the change in the exchange rate on the interest rate differential. In this discussion, two research fields are relevant in this paper: the Theory of Interest Rate Parity (covered and uncovered) that links the interest rate market with the foreign exchange market, and development of models for conditional heteroskedasticity such as the ARCH model (Engle, 1982) and the ARCH-M model (Engle et al., 1987). The ARCH-M model, proposed initially by Engle et al. (1987), is important because it considers the exchange rate through an assets approach wherein time-series pricing models should measure risk and movements of risk over time, and include them as price determinants. Any increase in the expected rate of return on an asset as a function of its risk should be identified as a risk premium. ⇑ Corresponding author. Tel.: +55 21 21389201, +55 21 2138 9310 (Office), +55 21 99762 9255 (Cell Phone). E-mail addresses: [email protected] (M.B. Coelho dos Santos), [email protected] (M.C. Klotzle), [email protected] (A.C. Figueiredo Pinto). 1 Tel.: +55 21 21389201. http://dx.doi.org/10.1016/j.intfin.2016.04.012 1042-4431/Ó 2016 Elsevier B.V. All rights reserved.

Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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M.B. Coelho dos Santos et al. / J. Int. Financ. Markets Inst. Money xxx (2016) xxx–xxx

If this relationship does not hold, however, what are the common explanations for a possible bias? Price deviations predicted by theoretical models in relation to market-verified prices have at least three potential explanations: a risk premium different from zero for the risk distribution, the existence of transaction costs originated from dynamic hedging, and the possibility of inefficient markets. As risk premium is one of the most frequent explanations cited in various studies, this paper focuses on the estimation of the foreign exchange risk premium based on the expectational errors generated by risk-adjusted uncovered interest rate parity (UIP) model using the CGARCH-M model for the major currencies used in carry trade strategy. According to UIP, if investors are risk neutral and form expectations rationally, then exchange rate changes will eliminate any gain arising from the differential in interest rates across countries. A number of empirical studies show, however, that exchange rate changes do not compensate for the interest rate differential. As a consequence, carry trades—which are trading strategies where the investor takes a long position in the high interest rate currency and a short position in the low interest rate currency—form a profitable investment, violate UIP, and give rise to the forward premium puzzle. Our major contribution to the literature is threefold. First, we introduce country risk in the uncovered interest rate parity equation by adopting a CGARCH-M risk-adjusted model, second we analyze eight of the most commonly used carry trade currencies, and third we examine the influences of the risk premium components. Our paper is organized as follows. We begin with a literature review in Section 2. In Section 3, we describe the methodology and the data used in this study. In Section 4, we report the results and analyze the impact of the parameter signals found in the risk-adjusted UIP Model with and without country risk and the drivers of transitory and permanent riskpremium volatility. Finally, in Section 5 we discuss these results and make suggestions for future work. 2. Literature review The dominant academic viewpoint that emerged from research in the 1980s is that foreign exchange rates could be modeled as stock prices. In the context of asset pricing, exchange rates would reflect expected future values discounted to the present. This perspective is similar to the idea that the price of an asset reflects the present value of its future expected cash flows. A second conclusion is that a currency’s price is determined by its demand as a financial asset in relation to other currencies. As consequence, a series of articles were developed in the 1980s joining capital market theory with macroeconomics to explain the complex movements of exchange rates. The seminal works of Hansen and Hodrick (1980), Bilson (1981), and Fama (1984) provided evidence that the forward exchange rate is a biased estimator of the future spot exchange rate. They observed that currencies with high interest rates tended not to depreciate as predicted by the UIP, but appreciate instead. This is inconsistent with the theory of no-arbitrage and has been confirmed by extensive literature in different countries and periods, and has become known as the forward premium puzzle. According to Levich (1983), expectations can contribute to exchange rate volatility. From the viewpoint of stock pricing, if market participants classify recent innovations as permanent and extrapolate their impacts into the future, this affects prices. This extrapolation process can be irrational and therefore is not a permanent characteristic of the exchange rate process. Considering the asset pricing approach (Levich, 1983), the spot exchange rate today (st) depends upon the current expectation of all variables (zt+k) that influence the exchange rate (demand for domestic and foreign currency, domestic and foreign income, etc.) from today until the future, assuming that the covered interest rate parity (CIP) is sustained. This means that st is equal to the expected future spot rate without a premium, discounted to the present value, as seen in Eq. (1):

st ¼

 1  1 X e k Eðztþk Þ 1 þ e k¼0 1 þ e

ð1Þ

There are two direct implications of Eq. (1): first, the current exchange rate reflects what is known or expected about the future. Second, the exchange rate only changes in response to unanticipated events. In general, there is not a consensus regarding the forward premium puzzle. Possible explanations for the deviation from UIP include a failure in rational expectations (Froot and Frankel, 1989) and a time-dependent risk premium variable (Froot and Thaler, 1990; McCallum, 1994; Chinn and Meredith, 2004). Fama (1984) argued that the time-variant risk premium can explain this failure only if: (1) the risk premium is more volatile than expected changes in the future spot exchange rate; and (2) the risk premium is negatively correlated with the amount of expected depreciation. Hodrick and Srivastava (1984) examined the risk premium determination for the forward exchange market. They found evidence of heteroskedasticity and the conditional expectation of the risk premium as a nonlinear function of the forward premium. Domowitz and Hakkio (1985) investigated the existence of risk premiums in the foreign exchange market based upon a conditional variance in market forecasting errors. They found evidence of different risk premiums in the exchange market for some, but not all, currencies. However, they also found little support for conditional variance as the only determinant of risk premium. Froot and Thaler (1990) presented a series of anomalies in the foreign exchange market and claimed that these anomalies in financial markets are often ‘‘explained” by economists with the use of some type of risk argument. Their argument was that such explanations often have a decisive debating advantage: poor testability. They further suggest that the bias is related to expectational errors rather than risk. Since there is no good general equilibrium model for exchange rates, it is difficult to test market inefficiency, which could be a possible solution for explaining these anomalies. Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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3

Frankel and Chinn (1993) used survey data for 17 different currencies to explain the movements of forward discount and found evidence of the presence of a risk premium and determined that changes in expectations still had a substantial effect. According to Engel (1996) and Flood and Rose (1996), the forward premium can be defined as a currency appreciation for a country with a higher interest rate, as opposed to a currency depreciation as predicted by theory. This anomaly is frequently expressed in terms of a regression of the spot returns against an interest rate differential as having a negative slope. Froot (1990) estimated an average value of 0.88 for the slope. Garcia and Olivares (2001) showed that neither CIP nor UIP is verified for the Brazilian market during the Real Plan period, and identified a time-variant exchange risk premium that is highly correlated with the Brazilian country risk. Sarno et al. (2012) applied a Bayesian model and extracted an endogenous risk premium for the no-arbitrage condition related to the interest rate and exchange rate term structures. The second relevant sub-group of research that supports this work is related to the development of models for conditional heteroskedasticity time dependence. Engle’s seminal work (1982), followed by Bollerslev (1986), respectively introduced a new class of stochastic processes called autoregressive conditional heteroskedasticity (ARCH), and its generalized form (GARCH) in which variance over time depends on the past and has important statistical properties for modeling risk and uncertainty. Engle et al. (1987) proposed an ARCH-M model where conditional variance is a determinant of risk premium, and is thus included in the average-return forecasting equation. The authors concluded that the risk premium is not time invariant, but varies systematically with agents’ perceptions of associated uncertainty. The GARCH-M model introduced by Bollerslev et al. (1988) was developed in order to capture the relationship between return and risk. Application of GARCH-M models to stocks, interest rates, and foreign exchange rates can be found in Bollerslev et al. (1994). Hsieh (1989) estimated ARCH and GARCH models for five different currencies and concluded that these models can remove heteroskedasticity in all of the sample currencies. Berk and Knot (2001) followed Engle et al.’s (1987) seminal work by introducing the UIP risk premium with the use of an ARCH model. Bansal and Dahlquist (2000) found that a negative correlation between the expected depreciation rate and risk premium is only present in developed countries (one of the conclusions of Fama’s (1984) fundamental equations, b2 < 0), while in emerging economies, it is common to obtain positive estimates. Garcia and Olivares (2001) also found a positive correlation for the Brazilian Real Plan. Evidence from Bansal and Dahlquist (2000) indicates that countries with low per-capita income, high inflation, and low credit ratings are those with the highest b2 estimates. Pramor and Tamirisa (2006) analyzed foreign exchange rate volatility using a CGARCH model and concluded that the two volatility components are influenced by different factors: the tendency for long-term volatility as a reflection of fundamental economic shocks and transitory volatility as a consequence of market confidence and short-term positions. Melander (2009) tested risk premium under UIP conditions by using a GARCH-M model for Bolivian data, supplying evidence that UIP was not supported. However, deviations related to UIP were smaller when a risk premium was introduced. Li et al. (2012) applied a CGARCH-M model to both developed and emerging markets. In general, they supported the idea that adding risk premium to UIP equations delivers more significant results than the original ordinary least square basic model. Coudert and Mignon (2013) applied a linear regression between carry-trade gains and a proxy of the default risk. This allowed them to confirm two hypotheses: (i) when market volatility is low, carry-trade gains are higher when invested in currencies issued by countries with high default risk and (ii) conversely, over a certain threshold, the more volatile the financial markets become, the deeper the losses on investment in high default risk countries. Using a second test, their findings showed support for the hypothesis that default risk contributes to excess returns in the exchange market during boom periods. Kiani (2013) modeled exchange rates using non-Gaussian state space models that encompass non-normality and GARCHlike effects for 10 different currencies. The results show statistically significant evidence of a time varying risk premium. Moreover, statistically significant evidence of volatility clustering was confirmed in all the series. Aysun and Lee (2014) found that UIP deviation is a stylized fact in both emerging and advanced economies, and that the deviations from the parity condition for the former seem to be smaller than for the latter. Furthermore, the UIP deviations can be attributed to the time-varying risk premium that is required by risk-averse investors based on GARCH-M analysis. These results suggest that time-varying risk premiums would explain a more substantial part of UIP deviations in emerging market economies than in advanced ones. In order to investigate the source of deviations from the UIP condition, they applied a DSGE model to measure the contribution of 19 shocks on the historical decomposition of excess currency returns for the US Dollar/Brazilian Real and US Dollar/Euro exchange rates. The authors found that risk premium shocks are the main determinant of the deviations from the UIP condition in the US/Brazil model but have a negligible effect on the deviations in the US/Euro area model. 3. Methodology This research employs a methodology that is similar to the procedure adopted by Li et al. (2012). Our innovation is that we have added country risk to the UIP econometric equation, as proposed by Garcia and Olivares (2001). CIP postulates that  the difference between domestic and foreign interest rates ðit;k  it;k Þ should be equal to the forward premium. In turn, UIP implies that the difference between interest rates should be equal to the expected change in the exchange rate based on the following equation: 

Et ðstþk  st Þ ¼ ðf tþk  st Þ ¼ ðit;k  it;k Þ

ð2Þ

Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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where st is the logarithm for the spot exchange rate at time t and ft,k is the logarithm for the forward exchange rate at t for delivery at t + k. Et(.) stands for the mathematical operator representing expectation. Thus, the expected depreciation should be equal to the forward premium, (ft,k  st), or the positive difference between interest rates. To observe UIP in a market it is also necessary to verify CIP. Thus, CIP is a necessary, but not sufficient, condition for verifying UIP. Considering rational expectations, models that seek evidence for UIP risk premium mostly take the equation form proposed by Domowitz and Hakkio (1985):

stþk  st ¼ a þ bðf t;k  st Þ þ RP t þ et;k

ð3Þ

Various estimated models that exclude risk premium (RP) encounter negative coefficients for b when testing the UIP hypothesis, (a = 0 and b = 1) implying that Cov(Et(st+k  st);RPt) < 0 and that Var(RPt) > Var(Et(st+k  st)). According to the methodology proposed by Fama (1984), the forward exchange rate market, ft, should be decomposed into an expected rate and a risk premium:

f t ¼ Et ðstþ1 Þ þ RP t

ð4Þ

All variables are in logarithmic form, and the expected future spot rate value, Et ðstþ1 Þ, is the rational prediction, conditional on all available information at time t. Eq. (4) is a specific definition for the premium component of the forward rate, but it is not the only possible one. The pricing of a currency futures contract, using the no-arbitrage condition as suggested by Hull (2012) is:

F ¼ Seðrr

 ÞðTtÞ

ð5Þ ⁄

where F is the future price, (ft = log Ft), S is the spot rate, (st = log St), r is the domestic interest rate, r is the foreign interest rate, and (T  t) is the annualized time expiration. Applying logarithms:

f t ¼ st þ ðrt  rt ÞðT  tÞ

ð6Þ

Eq. (5) is used in finance and equals the covered interest rate parity (CIP) condition, used in open macroeconomics. International empirical evidence shows that this condition works for developed economies, that is, it does not make a difference for an investor to buy bonds denominated in their country’s currency or in a foreign country’s currency (Bansal and Dahlquist, 2000). Introducing the convenience yield (y), called country risk, conforming to the proposal by Garcia and Olivares (2001), Eq. (5) transforms into

F ¼ Seðrr

 yÞðTtÞ

ð7Þ

Applying logarithms:

f t ¼ st þ ðrt  rt  yt ÞðT  tÞ

ð8Þ

Based upon Eqs. (8) and (4), we can compose:

st þ ðr t  r t  yt ÞðT  tÞ ¼ Et ðstþ1 Þ þ RP t

ð9Þ

Considering rational expectations:

stþ1 ¼ Et ðstþ1 Þ þ 1tþ1

ð10Þ

where 1tþ1 is white noise. Combining Eqs. (9) and (10) with the model proposed by Domowitz and Hakkio (1985) who investigated risk premium in the foreign exchange market based on the conditional variance of market forecast errors, the empirical test for the exchange risk premium can be expressed as:

stþ1  st ¼ a þ bðr t  r t  yt ÞðT  tÞ þ crtþ1 þ etþ1

ð11Þ

This econometric model is equivalent to including a risk premium in the uncovered interest rate parity theory (UIP). The UIP introduces uncertainty into the finance and macroeconomic model since it works with expectations instead of the forward rate (CIP). As in Berk and Knot (2001) and Melander (2009), we added conditional standard deviation (rtþ1 ) as a timevariant risk premium in the mean equation in order to construct the GARCH-M model. As such, Eq. (11) in its GARCH-M version can be written as:

stþ1  st ¼ a þ bðr t  r t  yt ÞðT  tÞ þ crtþ1 þ etþ1

r2tþ1 ¼ d0 þ /1 e2t þ /2 r2t

ð12Þ

The variable rtþ1 is the standard deviation of the error term and denotes the time-varying risk premium that directly affects the exchange rate. The risk premium has a constant component (a) and a time-variant component (rtþ1 ), which is highly reasonable, since if an investor’s risk aversion changes over time, the same behavior should be predicted for the risk Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

Currency

Brazil (USD/BRL) Chile (USD/CLP) Russia (USD/RUB) Poland (USD/PLN) Indonesia (USD/IDR) Turkey (USD/TRY) Mexico (USD/MXN) South Africa (USD/ZAR)

Spot

Domestic target interest rate

1-Month exchange traded future or forwarda

EMBI+ or 5 years CDS

Data range (M)

Data range (M)

Data range (M)

Stock exchange

Data range (M)

July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15

July/94-Oct/15 May/95-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15 July/94-Oct/15

July/94-Oct/15 July/00-Oct/15 Dec/02-Oct/15 April/03-Oct/15 Mar/01-Oct/15 Oct/08-Out/15 April/95-Oct/15 May/97-Oct/15

BM&F NDF RTS BSE NDF BSE CME CME

July/94-Oct/2015 Jan/03-Oct/15 Dec/97-Oct/15 Nov/94-Oct/15 Oct/04-Oct/15 July/99-Oct/15 Dec/97-Oct/15 Oct/00-Oct/15

b

Industrial production

Narrow money

CPI

Product

Data range (M)

Data range (M)

Data range (M)

EMBI+ CDS EMBI+ EMBI+CDS CDS EMBI+ EMBI+ CDS

July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015

July/94-Oct/2015 July/94-Oct/2015 Jun/95-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015

July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015 July/94-Oct/2015

Total sample UIP equation

Total sample CIP equation

255 153 214 251 131 194 213 179

255 148 154 150 131 84 213 179

a In general, we decide to use future market instruments to mitigate the credit risk of our analysis. We use Non-Deliverable Forwards only in Chile and Indonesia because they have a longer times series available at Bloomberg. b We decided to use the longest time series available: EMBI+ or 5 years CDS as risk country proxy depending on the currency. Only in the Polish Zloty do we have an EMBI+ series discontinuing in April/07, in which case we use 5 years CDS from this date up to Oct/15.

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Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

Table 1 Data summary.

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M.B. Coelho dos Santos et al. / J. Int. Financ. Markets Inst. Money xxx (2016) xxx–xxx

premium. If a and c are insignificantly different from zero, then there is no risk premium. If a – 0 but c = 0, then there is a constant risk premium. Only when c – 0 does the time-varying risk premium exist. Nevertheless, the final econometric model to be tested is a combination of the GARCH-M component (CGARCH-M) model with the asymmetric TARCH model (Glosten et al., 1993; Zakoïan, 1994), which introduces asymmetric effects into the transitory equation

stþ1  st ¼ a þ bðr t  r t  yt ÞðT  tÞ þ crtþ1 þ etþ1 ð13Þ

qtþ1 ¼ /1 þ /2 ðqt  /1 Þ þ /3 ðe2t  r2t Þ

r

2 tþ1

¼ qtþ1 þ /4 ðe  qt Þ þ /5 Dt ðe  qt Þ þ /6 ðr  qt Þ 2 t

2 t

2 t

where Dt is a dummy variable for capturing asymmetric effects: Dt = 1 when et < 0, indicating the presence of a transitory leveraged conditional variance (unexpected exchange rate appreciation) and Dt = 0 if otherwise; qt+1 is the long-run component of the conditional variance which reflects shocks to economic fundamentals and converges to the long-run timeinvariant volatility level /1 with a magnitude of /2; and ðr2tþ1  qtþ1 Þ is the short-run component which is more volatile and driven by market sentiment. The AR(1) /2 coefficient of permanent volatility in the long-run component should exceed the (/4 + /6) coefficients in the transitory component, which implies that in a stable model short-run volatility converges more rapidly than long-run volatility. The error distribution is modeled as a generalized error distribution (GED), et+1|Ut  GED (0, ht+1, v = 1), which enables the inclusion of kurtosis, which is very common in financial data, including exchange rates. Nelson (1991) used this distribution to model stock market returns, and Hsieh (1989) used it to model exchange rate distribution. The CGARCH model used here is the same proposed by Engle and Lee (1999). This model guarantees that volatility is not constant over the long-term and splits the risk premium volatility into two components, one with a long-run tendency and the other as short-run deviations from this tendency. Separating the risk premium into permanent and transitory aspects allows us to better understand the sources of uncertainty, since investment decisions strongly depend upon whether uncertainty is permanent or transitory (Byrne and Davis, 2005). For our empirical analyses, we use the monthly spot and the 1-month future exchange rates, the difference between domestic and US target interest rates (FDTR Index from Bloomberg), the country risk as measured by EMBI+ or by 5 years CDS depending on the availability for each currency, the industrial production, the narrow money and the consumer price index (CPI) for 8 different carry trade currencies. The currencies considered are Brazilian Real (USD/BRL), Chilean Peso (USD/ CLP), Russian Ruble (USD/RUB), Polish Zloty (USD/PLN), Indonesian Rupiah (USD/IDR), Turkish Lira (USD/TRY), Mexican Peso (USD/MXN) and South African Rand (USD/ZAR). The USD is the base currency, and all other currencies are expressed in terms of a unit of USD. Table 1 summarizes all data extracted from OECD stats and Bloomberg for the longest period available. 4. Results 4.1. Foreign exchange risk premium Table 2 shows the OLS results for the CIP and UIP tests. Both covered and uncovered parity null hypotheses (a = 0, b = 1) were rejected via Wald’s test for all currencies (available from the authors upon request). However, by including country risk in the main equation, we obtained significant parameters for the UIP equation, which shows its importance in the model. Contrary to other studies that analyzed emerging markets (Li et al., 2012), by including country risk, we found negative values for the interest rate differential parameters (b) for Brazil, Poland, Indonesia, Turkey, Mexico and South Africa, corresponding with Fama (1984). This negative value means that UIP does not hold and that the base currency (USD) depreciated when it should have appreciated to confirm the UIP hypothesis. To our surprise, when country risk is not included, the values of the interest rate differential parameters (b) for the same countries are significantly positive or insignificant. Considering the UIP equation, only Chile and Russia show significantly positive or insignificant b parameters, with and without the inclusion of country risk. However, as we move to the CIP equation, we find significantly positive b parameters for all currencies (without country risk) and similar results for Poland and Mexico (with the inclusion of country risk). One of the stylized facts from financial time series is the presence of a random walk (the random walk hypothesis). As GARCH models should be applied only to stationary series, it is necessary to perform a unit root test. Therefore, we have performed the unit root test for the difference between target interest rates and country risk (r  r⁄  y) and for the change in exchange rate (st+1  st). Results from both the Augmented Dickey–Fuller (ADF) and Phillips–Perron tests for all currencies (available from the authors upon request) rejected the null hypothesis (the series has a unit root), implying that all the series are stationary. Table 3 shows the estimated risk-adjusted UIP results applying the CGARCH-M model, according to Eq. (13). We did not include country risk in the CGARCH-M adjusted UIP equation only for Chile and Russia, as it was not statistically significant in the basic equation (see Table 2). The results from this model are remarkable. The intercept (a) is significant, indicating a constant risk premium for Brazil, Chile, Russia, Poland, Indonesia, Turkey, and South Africa. The coefficient for the interest rate differential (b) was negative for Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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M.B. Coelho dos Santos et al. / J. Int. Financ. Markets Inst. Money xxx (2016) xxx–xxx Table 2 OLS results for carry trade emerging market currencies.

CIP

r  r⁄ r  r⁄  y

UIP

r  r⁄ r  r⁄  y

Brazil (USD/BRL)

Chile (USD/CLP)

Russia (USD/RUB)

Poland (USD/PLN)

a

b

a

b

a

b

a

b

0.0016 (0.0013) 0.0072⁄⁄⁄ (0.0009) 0.00816 (0.0062) 0.0252⁄⁄⁄ (0.0065)

0.0174⁄⁄⁄ (0.0028) 0.00232 (0.0016) 0.0066 (0.0139) 0.0506⁄⁄⁄ (0.0065)

0.0005 (0.0005) 0.0019⁄⁄⁄ (0.0004) 0.0077⁄ (0.0042) 0.0003 (0.0028)

0.0251⁄⁄⁄ (0.0016) 0.0002 (0.0003) 0.1283⁄⁄ (0.0542) 0.0081 (0.0064)

0.0009 (0.0018) 0.0049⁄⁄⁄ (0.0007) 0.0082 (0.0065) 0.0094⁄ (0.0051)

0.0192⁄⁄ (0.0078) 0.0018 (0.0015) 0.0542⁄⁄⁄ (0.0141) 0.0051 (0.0080)

0.0002 (0.0008) 0.0032⁄⁄⁄ (0.0005) 0.0021 (0.0038) 0.0055⁄⁄ (0.0025)

0.0471⁄⁄⁄ (0.0105) 0.0025⁄⁄ (0.0010) 0.0234 (0.0161) 0.0179⁄⁄⁄ (0.0040)

Indonesia (USD/IDR)

CIP

r  r⁄ r  r⁄  y

UIP

r  r⁄ r  r⁄  y

Turkey (USD/TRY)

Mexico (USD/MXN)

South Africa (USD/ZAR)

a

b

a

b

a

b

a

b

0.0104⁄⁄ (0.0041) 0.0058⁄⁄⁄ (0.0009) 0.0012 (0.0136) 0.0084⁄⁄⁄ (0.0026)

0.1095⁄⁄⁄ (0.0262) 0.0018 (0.0018) 0.0323 (0.0885) 0.0306⁄⁄⁄ (0.0053)

0.0011 (0.0027) 0.0095⁄⁄⁄ (0.0026) 0.0002 (0.0048) 0.0163⁄⁄⁄ (0.0044)

0.0661⁄⁄⁄ (0.0152) 0.0038 (0.0026) 0.0218⁄⁄⁄ (0.0073) 0.0136⁄⁄ (0.0057)

0.0003 (0.0007) 0.0065⁄⁄⁄ (0.0006) 0.0049 (0.0034) 0.0112⁄⁄⁄ (0.0019)

0.0490⁄⁄⁄ (0.0035) 0.0070⁄⁄⁄ (0.0016) 0.0083 (0.0168) 0.0462⁄⁄⁄ (0.0046)

0.0013 (0.0012) 0.0075⁄⁄⁄ (0.0005) 0.0019⁄ (0.0105) 0.0122⁄⁄⁄ (0.0033)

0.0416⁄⁄⁄ (0.0075) 0.0013 (0.0011) 0.1021 (0.0637) 0.0616⁄⁄⁄ (0.0072)

Note: ⁄⁄⁄ 1%, ⁄⁄ 5%, and ⁄ 10% levels of significance. Standard errors are listed within parentheses. Residuals were uncorrelated (white noise). Results of Wald’s test for H0: a = 0; b = 1 were rejected for all currencies. Equations for CIP without and with country risk respectively: f t  st ¼ a þ bðrt  r t ÞðT  tÞ þ etþ1 and f t  st ¼ a þ bðrt  r t  yt ÞðT  tÞ þ etþ1 . Equations for UIP without and with country risk respectively: stþ1  st ¼ a þ bðrt  r t ÞðT  tÞ þ etþ1 and stþ1  st ¼ a þ bðr t  r t  yt ÞðT  tÞ þ etþ1 .

Table 3 Risk-adjusted UIP model adopting CGARCH-M with and without (^) country risk.

c a b u1 u2 u3 u4 u5 u6 u2 = u 3 = 0 b=1 a=c=0

Brazil

Chile^

Russia^

Poland

Indonesia

Turkey

Mexico

South Africa

0.5233⁄⁄⁄ (0.1632) 0.0308⁄⁄⁄ (0.0071) 0.0179⁄⁄⁄ (0.0030) 0.0040⁄ (0.0022) 0.9940⁄⁄⁄ (0.0049) 0.0016 (0.0040) 0.4001⁄⁄ (0.1606) 0.0894 (0.1607) 0.0326 (0.0864) 0.0000 0.0000 0.0000

1.3683⁄⁄ (0.5317) 0.0414⁄⁄⁄ (0.0161) 0.0726⁄ (0.0411) 0.0014⁄⁄⁄ (0.0004) 0.8003⁄⁄⁄ (0.1311) 0.0931 (0.0603) 0.0642 (0.0956) 0.0066 (0.1428) 0.4542 (0.6567) 0.0000 0.0000 0.0363

1.0143⁄⁄⁄ (0.1001) 0.0373⁄⁄⁄⁄ (0.0039) 0.0035⁄⁄⁄ (0.0024) 0.0009⁄⁄⁄ (0.0002) 0.6649⁄⁄⁄ (0.0051) 0.1786 (0.1695) 0.0764 (0.1681) 0.3193⁄⁄⁄ (0.0825) 0.3211 (0.2123) 0.0000 0.0000 0.0000

0.5828⁄⁄⁄ (0.0605) 0.0257⁄⁄⁄ (0.0002) 0.0081⁄⁄⁄ (0.0030) 0.0036 (0.0064) 0.9882⁄⁄⁄ (0.0280) 0.0959⁄ (0.0555) 0.0500 (0.1219) 0.1770 (0.1529) 0.7225⁄ (0.3888) 0.0000 0.0000 0.0000

0.1483 (0.2290) 0.0089⁄⁄ (0.0044) 0.0170⁄⁄⁄ (0.0027) 0.0006 (0.0004) 0.8213⁄⁄⁄ (0.0183) 0.2849 (0.3500) 0.2519 (0.3290) 0.2842 (0.3869) 0.1764 (0.6677) 0.0000 0.0000 0.0000

2.2460⁄⁄⁄ (0.1727) 0.0618⁄⁄⁄⁄ (0.0096) 0.0591⁄⁄⁄ (0.0040) 0.0013⁄⁄⁄ (0.0003) 0.9503⁄⁄⁄ (0.0076) 0.0282⁄⁄⁄ (0.0083) 0.0398⁄⁄ (0.0169) 0.0308 (0.0272) 0.8867⁄⁄⁄ (0.0502) 0.0000 0.0000 0.0000

0.2270 (0.1766) 0.0032 (0.0040) 0.0428⁄⁄⁄ (0.0030) 0.0009 (0.0006) 0.9523⁄⁄⁄ (0.0138) 0.1792⁄⁄ (0.087) 0.0574 (0.1648) 0.2731 (0.1709) 0.6188⁄ (0.3577) 0.0000 0.0000 0.0000

1.0782⁄⁄⁄ (0.3715) 0.0547⁄⁄⁄ (0.0147) 0.0679⁄⁄⁄ (0.0052) 0.0024⁄⁄⁄ (0.0009) 0.9506⁄⁄⁄ (0.0431) 0.0862⁄⁄ (0.0369) 0.0637 (0.0486) 0.1369⁄ (0.0772) 0.7376⁄⁄⁄ (0.0820) 0.0000 0.0000 0.0000

Note: ⁄⁄⁄ 1%, ⁄⁄ 5% and ⁄10% significance levels. All variables in bold are significant. Residuals do not have serial correlation (white noise). Standard error listed within parentheses. The p-value of the Wald’s tests of u2 = u3 = 0, b = 1 and a = c = 0 are on the table. Equations tested: (1) With country risk Stþ1  St ¼ a þ bðrt  r t  yt ÞðT  tÞ þ crtþ1 þ etþ1 . (2) (^) Without country risk Stþ1  St ¼ a þ bðr t  r t ÞðT  tÞ þ crtþ1 þ etþ1 .

Brazil, Poland, Indonesia, Turkey, Mexico, and South Africa, similar to the OLS results found in Table 2. This suggests that an increase in this differential leads to a fall in the expected exchange rate. This is consistent with previous literature (Froot and Thaler, 1990; Engel, 1996), and can be empirically verified for the Brazilian Real, which suffered strong appreciation from 2002 through 2013. However, this result is not verified with unanimity in the academic world. Bansal and Dahlquist (2000) found positive estimates for emerging countries, and Li et al. (2012) found a significant positive sign for Brazil. The positive b value means also that UIP does not hold and that the base currency (USD) appreciated when it should depreciate to confirm the UIP. This holds true for Chile and Russia where we found a positive and significant b in the equation without the additional country risk factor. The coefficient c corresponds to the time-varying risk premium. In all currencies,

Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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the Wald’s test for non-zero risk premium (a = 0, c = 0) was rejected, confirming the presence of risk premiums. Only in Indonesia did we find a constant risk premium. For the investing currencies BRL, CLP, PLN, and ZAR, the existence of a negative time-varying risk premium, along with a negative b, increase the forward bias puzzle, leading to an appreciation of those currencies. On other hand, for the investing currencies RUB and TRY, the existence of a positive time-varying risk premium leads to a depreciation of those currencies reducing the beta-effect which appreciates the investing currency. In theory, this could mean that currencies with a positive time-varying risk premium tend to approximate to UIP and therefore to the no-arbitrage theory, reducing the b effect. Another way to interpret the negative sign of the risk aversion parameter (c) is to show that it is consistent with classical asset pricing theory. The negative c coefficient corresponds to the mean–variance theory or rather to the expected utility theory. When there is an increase in risk, the expected return from holding the investing currency increases, because the investor will have to exchange it for the base currency (USD) when finishing the carry trade operation. In this sense, riskaverse investors will require more returns when they face higher risk, leading to a decrease in the depreciation of the investing (home) currency. Melander (2009) used a GARCH model to test the UIP condition for Bolivian data and reported a negative risk premium. Li et al. (2012) also found a significant negative result for Brazil. The /2 coefficient is significant for all currencies, confirming the presence of long-run volatility persistence in the risk premium. The closer the estimated value of the /2 is to one, the slower the long-run component of the conditional variance (qt+1) approaches the long-run time-invariant volatility level /1. This is the case for most currencies, with the exception of Russia, where shocks to economic fundamentals are rapidly absorbed since the risk premium converges quickly to a stable level (/1). The /4 and /6 coefficients measure the initial impacts of a shock and the memory intensity in the transitory component respectively. Therefore, the sum (/4 + /6) measures the persistence of a shock in the transitory component, and even though insignificant, the sum of the parameters is less than /2, implying stability in the model and a faster convergence of the shortrun than the long-run volatility. The larger long-run volatility component indicates that the risk premium suffers greater influence from fundamental economic shocks than from changes in market confidence. This is similar to the results found by Byrne and Davis (2005) and Pramor and Tamirisa (2006). The asymmetric coefficient /5 is negative and insignificant in most currencies, with the exception of South Africa (significant at 1%) and Russia (positive and significant at 10%). In spite of most results being insignificant, the negative asymmetric coefficient /5 implies that unexpected domestic currency depreciations have a larger effect on volatility than unexpected appreciations increasing short-term volatility. This is consistent with currency crises and it is in line with Byrne and Davis (2005). In the case of the Brazilian Real, a possible explanation for the mitigation of this effect could be the continuing interventions of Central Bank of Brazil in the derivatives market using FX swap and FX repurchasing through line auctions since 2013. Despite being almost insignificant in our study, the negative asymmetric coefficient /5 has important consequences for investors engaged in carry trade activities. Borrowing in low-interest currencies and investing in high-interest currencies exposes investors to an unexpected depreciation of the investing currency, which can offset the gains from the interest rate differential. Since in presence of asymmetries, unexpected depreciations have a greater impact on volatility, carry trade investors are exposed to two sources of risk: depreciation and higher volatility, both of which can impose large losses. Recent studies show that a higher variance in the foreign exchange market is significantly related to large future carry trade losses, which is in line with the unwinding of the carry trade in times of high volatility (Cenedese et al., 2014). Consistent with Cenedese et al. (2014), Christiansen et al. (2011) show that the level of foreign exchange volatility also affects the risk exposure of carry trade returns to capital markets. Those results raise the question about whether there is any relation between the coefficient for asymmetries (/5) and the risk aversion parameter c. Even if there are alternatives to hedge the exposition to a higher volatility by means of derivative markets, the existence of an asymmetric coefficient /5 could imply in a higher risk aversion parameter c, thus mitigating the effects of higher depreciation as found in Fama (1984). By reviewing the a, b, and c effects from Fig. 1 and Table 4, it might be appropriate to put the alternative hypothesis of a time-varying risk premium to a more informal sensibility check, asking how it would explain the unprecedented behavior of the exchange rate changes. It shows general appreciation for Brazilian, Polish, Chilean, and South African currencies from 2002 to 2013 (negative net sum of parameters), and general depreciation for Russian and Turkish currencies in the same period (positive net sum of parameters), consistent with the parameters’ signals. In the case of Chile, the time-varying risk premium seems to overlap the beta effect, since in the 2002–2013 period the investing currency (CLP) appears to appreciate. In both cases with the negative and positive net signal of parameters, the UIP does not hold (which also can be confirmed from the second Wald’s test in Table 3) so this opens space for carry trade strategies. Furthermore, the risk premium exists (from the third Wald’s test in Table 3), and the forward puzzle continues to be verified in the market. Fig. 2 below shows the estimated permanent and transitory risk-premium volatility components generated from the CGARCH-M model. The transitory component is more volatile than the permanent component and it seems to be influenced by market confidence related to short-term speculative pressures. This hypothesis is confirmed in Section 4.2. On the other hand, permanent volatility is influenced by macroeconomic fundamentals, such as the goods market, where adjustments are slower due to the natural inertia in this kind of market. In case of the Brazilian Real, we observe volatility peaks in times of currency regime change (1999) or crisis, such as the Brazilian election (2002) and the mortgage-backed securities meltdown

Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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Fig. 1. Spot exchange rate daily data. Since the currencies have different scales, they are listed on either the Left Hand Side (LHS) or Right Hand Side (RHS).

Table 4 Informal sensibility check—parameters’ signal analysis. Currency

USD/BRL USD/CLP USD/PLN USD/ZAR USD/RUB USD/TRY

FX change (%)

43 37 25 17 30 39

% of Time period (%)

49 82 50 73 84 76

Investing currency

Base currency

Fama regression

Risk-premium

BRL, CLP, PLN, ZAR, RUB, TRY

USD

Beta

Time-varying Gamma

Appreciation Appreciation Appreciation Appreciation Depreciation Depreciation

Depreciation Depreciation Depreciation Depreciation Appreciation Appreciation

0.0179 0.0726 0.0081 0.0679 0.0035 0.0591

0.5233 1.3683 0.5828 1.0782 1.0143 2.2460

Constant Alpha 0.0308 0.0414 0.0257 0.0547 0.0373 0.0618

Net sum of parameters

0.510 1.254 0.565 1.091 0.981 2.125

FX change: spot foreign exchange rate change. % of Time period: percent of total time period in which the change was observed.

(2008). Similar volatility peaks are observed for Russia (1998 Ruble Crisis) and for most currencies following the Lehman Brothers Crisis of 2008–2009. In the next section, we examine variables having possible influences on the risk-premium volatility originated by the CGARCH-M model. 4.2. Drivers of risk-premium volatility Risk-premium volatility is divided into permanent and transitory components. According to Engle and Lee (1999) and Pramor and Tamirisa (2006), the two volatility components are influenced by different factors: long-term volatility resulting from fundamental economic shocks and transitory volatility due to short-term market confidence and positions. In this section, we seek to confirm whether transitory and permanent risk-premium volatilities are determined by market confidence and fundamental factors, respectively. To accomplish this, a determinant for transitory risk-premium volatility is initially tested. We opted to test the relationship with the VIX (the CBOE Volatility Index), as it is typically used as a proxy to capture global risk appetite and liquidity restrictions for funding. Table 5 shows the results of this test where we found a positive relation with VIX for the Brazilian Real, Chilean Peso, Polish Zloty, and Indonesian Rupiah. The values are small because we are making a regression against a small value as dependent variable. Lustig et al. (2009) proposed a no-arbitrage model that predicts negative loadings on the common risk factor for the risk premium on low interest rate currencies and positive loadings for the risk premium on high interest rate currencies (which typically include more emerging than developed market currencies). The authors’ arguments are that in times of global market uncertainty, there is a ‘‘flight-to-quality” where investors demand a much higher risk premium for investing in high interest rate currencies, and accept lower (or more negative) risk premiums on low interest rate currencies. According to our results, it seems that global risk appetite has an influence not only on the risk premium, but also on volatility (transitory component) implying some kind of relation with market confidence. Our findings are similar with those reported by Lustig et al. (2010) where the equity option-implied volatility index (VIX) is positively correlated with emerging markets’ excess returns (risk premium), but negatively correlated with developed markets’ returns. Sarno et al. (2012) found similar results for developed markets as well. Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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USDPLN 1

0,0025

Feb/09

9

0,002

8

0,0015

7 6

0,001

5 0,0005

4 3

0

2

-0,0005

Fev/09

1 0 nov-94

-0,001 nov-96

nov-98

nov-00

GARCHCONDVAR

nov-02

nov-04

nov-06

nov-08

nov-10

GARCHPERMANENT

nov-12

nov-14

GARCHTRANSITORY

USDIDR 0,012

0 Dec/08

0,01

0

0,008

0

Sep/13

0,006

0

0,004

0

0,002

-

0 nov-04 nov-05 nov-06 nov-07 nov-08 nov-09 nov-10 nov-11 nov-12 nov-13 nov-14 GARCHCONDVAR

GARCHPERMANENT

USD/CLP Nov/2008

0,002

0,

0,006 0,005

Jun/04

0,0015

6 Out/2011 5

0,001

4

0,0005

3

0

2

-0,0005

1

-0,001

0 an-03 jan-04 jan-05 jan-06 jan-07 jan-08 jan-09 jan-10 jan-11 jan-12 jan-13 jan-14 jan-15

-0,0015

GARCHCONDVAR

GARCHPERMANENT

GARCHTRANSITORY

USDZAR 0,0025

8 7

-

GARCHTRANSITORY

Jun/05

Sep/07

0,

Jan/10

0, 0,

0,004

0, 0

0,003

-0 -0

0,002

-0 -0

0,001

-0 0 nov-00

-0 nov-02

nov-04 GARCHCONDVAR

nov-06

nov-08 GARCHPERMANENT

nov-10

nov-12

nov-14

GARCHTRANSITORY

Fig. 2. Risk-premium volatility components.

Table 6 shows the regression results between the permanent risk-premium volatility component and the observable fundamental variables. The variable D(m  m⁄) represents the percentage change in the logarithmic difference between domestic and US narrow money (M1), respectively. The same is true for the industrial production and price index: D(ip  ip⁄) and D (p  p⁄). Data were extracted from the OECD. Engel and West (2005) proposed the difference between D(m  m⁄)  D

Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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M.B. Coelho dos Santos et al. / J. Int. Financ. Markets Inst. Money xxx (2016) xxx–xxx Table 5 Transitory risk-premium volatility and global aversion to risk.

Brazil Chile Russia Poland Indonesia Turkey Mexico South Africa

a

b

0.0021⁄⁄ (0.0010) 8.7E05⁄⁄ (4.13E05) 0.00021 (0.000448) 0.00033⁄⁄⁄ (5.19E05) 0.00034⁄⁄⁄ (8.93E05) 4.0E06 (0.0001) 0.00028⁄⁄⁄ (6.92E05) 7.34E05 (0.0001)

0.0001⁄⁄ (0,00004) 4.17E06⁄⁄ (1.90E06) 2.70E05 (1.90E05) 2.21E05⁄⁄⁄ (2.34E06) 2.12E05⁄⁄⁄ (4.03E06) 1.88E06 (4.98E06) 1.10E05⁄⁄⁄ (3.03E06) 1.96E06 (5.09E06)

Note: ⁄⁄⁄ 1%, ⁄⁄ 5% and ⁄ 10% significance levels. All variables in bold are significant. Standard error listed within parentheses. The regression model is kt = a + bVIXt + et, where kt is the transitory risk-premium volatility component, VIXt is the CBOE volatility index and et is the error term. Residuals do not have serial correlations.

Table 6 Permanent risk-premium volatility component and fundamental variables.

Brazil Chile Russia Poland Indonesia Turkey Mexico South Africa

a

b

h

0.0024⁄⁄⁄ (4.30E05) 0.00132⁄⁄⁄ (3.93E05) 0.0018⁄⁄⁄ (0.0003) 0.00154⁄⁄⁄ (6.54E05) 0.0007⁄⁄⁄ (8.96E05) 0.00446⁄⁄⁄ (0.0004) 0.00066⁄⁄⁄ (0.0004) 0.0020⁄⁄⁄ (4.90E05)

7.72E05 (5.03E05) 6.93E06 (6.83E06) 2.54E06 (3.22E05) 1.64E06 (7.85E06) 1.77E08 (4.21E08) 5.85E07 (1.11E06) 4.35E07 (1.62E06) 3.17E06⁄⁄ (1.52E06)

0.0002 (0.0002) 5.03E05⁄ (2.63E05) 0.0002 (0.0008) 2.69E05⁄ (1.43E05) 0.0002 (0.0003) 0.0003 (0.0006) 6.35E06 (1.59E05) 0.0002 (0.0002)

Note: ⁄⁄⁄ 1% , ⁄⁄ 5% and ⁄ 10% significance levels. All variables in bold are significant. The model regression is kt = a + b[D(m  m⁄)  D(ip  ip⁄)]t + h[D(p  p⁄)]t + et where kt is the permanent risk-premium volatility component and et is the error term. Residuals do not have serial correlation.

(GDP  GDP⁄) as the fundamental observable variable. However, since monthly GDP data is difficult to obtain, we used the industrial production index as a proxy D(m  m⁄)  D(ip  ip⁄), similar to Sarno et al. (2012). According to our results, the fundamental observable variables do not add information to predict the long-term riskpremium volatility component for Brazilian Real, Russian Ruble, Indonesian Rupiah, Turkish Lira, and Mexican Peso. This implies that unobservable shocks, i.e., unanticipated events, should be more influential than fundamental variables in determining the permanent risk-premium volatility component. Dornbusch (1980) also demonstrates that much of the instability observed in exchange rates is due to unanticipated disturbances. Engel and West (2005) arrived at a similar conclusion, but while forecasting futures changes in exchange rates. 5. Conclusion and future work In this paper, we performed four different tests in an attempt to discover whether the forward premium puzzle is observed in typical carry trade currencies. In all countries surveyed in this study, neither the expectations of the CIP nor UIP hold. With the exception of Chile and Russia, we found negative values for the interest rate differential parameter for the countries assessed, including Brazil, Poland, Indonesia, Turkey, Mexico, and South Africa. This negative value demonstrates that the UIP does not hold for these currencies and that the base currency (USD) depreciated when it should appreciate if the UIP were confirmed. This indicates the presence of a forward bias puzzle, opening space for carry trade operations in the foreign exchange market. The major avenue of research that attempts to understand this puzzle searches for an appropriate time-varying risk premium. Therefore, to shed light on this subject, we utilized a UIP risk-adjusted model adopting CGARCH-M with and without country risk. Using this model has yielded some interesting conclusions. First, it shows the same result as the two previously applied tests for the UIP equation. For the equation including country risk, the interest rate differential (b) was negative for Brazil, Poland, Indonesia, Turkey, Mexico, and South Africa. For the equation without country risk, the interest rate differential (b) was positive for Chile and Russia. The positive b value means that UIP does not hold and the base currency (USD) appreciated when it should have depreciated in order to confirm the UIP. In all currencies, the Wald test for a non-zero risk premium was rejected, confirming the presence of risk premiums. The coefficient c corresponds to the time-varying risk premium. We conducted an informal sensibility check to understand the net signal effect of a, b, and c in the behavior of the exchange rate changes. We found a general appreciation for the Brazilian, Chilean, Polish, and South African currencies from 2002 to 2013 (negative net sum of parameters), and a general depreciation for the Russian and Turkish currencies in the same period (positive net sum of parameters), consistent with the parameters’ signals. In the case of Chile, the time-varying risk premium seems to overlap the beta effect, since during the period the Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012

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investing currency appears to appreciate. In all cases, the negative and the positive net signal of parameters means that UIP does not hold, opening space for carry trade strategies. Another way to view the financial/economic interpretation of a negative risk premium is found in the expected utility theory. When there is an increase in risk (standard deviation), the expected return from holding the investing currency increases, based on the premise that the investor will have to convert it to his or her base currency (the unwinding of the carry trade). Therefore, risk-averse investors require more returns when they face higher risk. Furthermore, our study also verified the following: (1) the presence of long-term persistence for risk-premium volatility for all countries; (2) In Russia, shocks to economic fundamentals are rapidly absorbed since the risk premium converges to a stable level unlike in others countries; and (3) indications that unexpected depreciations in local currency have a greater effect than appreciations only for the South African Rand (USD/ZAR). This is consistent with currency crises and is in line with Byrne and Davis (2005). In the case of the Brazilian Real, a possible explanation for the mitigation of this effect could be the continuing interventions of the Central Bank of Brazil in the derivatives market using FX swap and FX repurchasing through line auctions since 2013. We also searched for the drivers of transitory and permanent risk-premium volatility. Transitory risk-premium volatility is influenced by market confidence related to speculative pressures for the Brazilian Real, Chilean Peso, Polish Zloty, and Indonesian Rupiah. Unobservable shocks, i.e., unanticipated events, have more influence than fundamental variables in determining the permanent risk-premium volatility component for the Brazilian Real, Russian Ruble, Indonesian Rupiah, Turkish Lira, and Mexican Peso. Despite finding evidence of a risk-premium for emerging market carry trade currencies, the risk-adjusted UIP model presented in this study could still be improved. As mentioned by Froot and Thaler (1990) while this class of ‘‘statistical” models has provided rich information about the predictable components of exchange rate changes, it has not provided much evidence to suggest that these components are actually attributable to risk. One way to minimize this problem is to use a broader set of currencies to analyze if interesting new patterns can be identified. In this sense, this paper has contributed to fill in an important gap in the literature. Further research could analyze in depth the relationship between asymmetries and risk aversion to explain the forward premium puzzle in currency markets and whether this presents a form of tradable risk for investors. Future studies could also incorporate spot and forward bid-ask spreads as a proxy for (i) transaction costs and (ii) liquidity risk. In terms of transaction costs, incorporation of bid-ask spreads would reduce the returns of the carry trade, resulting in a change in the estimated model parameters. On the other side, as literature relates bid-as spreads to (i) liquidity restrictions in international exchange markets (Bessembinder, 1994), (ii) higher volatility (Glassman, 1987; Boothe, 1988) and (iii) uncertainty (Bollerslev and Melvin, 1994; Louis et al., 1999), new research could incorporate bid-ask spreads as proxy for time varying risk premiums (see for example the studies from Simpson and Grossmann (2014) for developed market currencies and Grossmann and Simpson (2015) for the British Pound and Euro). Appendix: Abbreviations AR(1): autoregressive lag 1; ARCH: autoregressive conditional heteroskedasticity; CDS: credit default swap; CIP: covered interest rate parity; EMBI+: emerging markets bond index plus; FX: foreign exchange; GARCH: generalized autoregressive conditional heteroskedasticity; GED: generalized error distribution; TGARCH: threshold generalized autoregressive conditional heteroskedasticity; VIX: volatility index; UIP: uncovered interest rate parity. 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Please cite this article in press as: Coelho dos Santos, M.B., et al. Evidence of risk premiums in emerging market carry trade currencies. J. Int. Financ. Markets Inst. Money (2016), http://dx.doi.org/10.1016/j.intfin.2016.04.012