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EMS exchange rate expectations and time-varying risk premia
Economics Letters 60 (1998) 351–355
EMS exchange rate expectations and time-varying risk premia a
b
Frederick G.M.C. Nieuwland , Willem F.C. Verschoor , Christian C.P. Wolff
c ,d ,
*
a
Algemeen Burgerlijk Pensioenfonds, Heerlen, The Netherlands De Nationale Investeringsbank, The Hague, The Netherlands c Limburg Institute of Financial Economics ( LIFE), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands d CEPR, London, UK b
Received 28 January 1998; accepted 7 May 1998
Abstract In this paper we examine exchange risk premia employing a survey dataset of EMS exchange rates. We are able to test a risk premium model directly, i.e. without having to rely on the rational expectations assumption. Our results indicate that time-varying risk premia are present in almost all cases and that a GARCH-in-mean specification for the premium is often appropriate. 1998 Elsevier Science S.A. All rights reserved. Keywords: Exchange rates; EMS; Risk premia; survey data JEL classification: F31
1. Introduction One of the well-estabished empirical regularities in the financial economics literature is the finding that the forward discount is a biased predictor of the future change in the exchange rate. The rejection of forward market efficiency is generally attributed to irrational expectations and time-varying risk premia. See, e.g., Frankel and Froot (1987); Cavaglia et al. (1994). Alternative methodologies have been explored in the literature to model and measure time-varying risk premia. In an interesting contribution Domowitz and Hakkio (1985) proposed a model in which risk premia are embedded in an autoregressive conditional heteroskedasticity (ARCH) framework. Their approach is conditional on the hypothesis that the forward exhange market is efficient or rational. In this paper we study European Monetary System (EMS) currency markets. We model risk premia using an approach that builds on the Domowitz and Hakkio analysis. Our approach implements a survey database of exchange rate expectations, covering a wide range of EMS exchange rates. The principal benefit of employing such data is that one obtains a direct measure of agent’s beliefs. Whereas the Domowitz and Hakkio study proceeds conditional on the assumption that expectations are rational, we are able to test the risk premium model directly, based on observed expectations. Our survey dataset is described in detail in Cavaglia et al. (1993), (1994). *Corresponding author. Tel.: 131 43 3883838; fax: 131 3 3258530; e-mail: [email protected] 0165-1765 / 98 / $19.00 1998 Elsevier Science S.A. All rights reserved. PII: S0165-1765( 98 )00128-1
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
352
The paper is organized as follows. In Section 2 we briefly describe the survey dataset. In Section 3 the methodology is explained. The empirical results are presented in Section 4 and our conclusions in Section 5.
2. The survey data From 1986 through 1991, Business International Corporation conducted a monthly survey of exchange rate expectations covering five European currencies relative to the Deutschmark, which were published in its Cross Rates Bulletin. For publication purposes, survey participants were asked a few days prior to the end of the month to fax 3, 6 and 12 month-ahead expectations of currencies with projections being made from the beginning of the following month. Thus, for instance, the 3, 6 and 12 month-ahead expected French franc / German mark rates recorded on December 27th, 1990, reflect slightly longer forecast horizons as they represent the spot rates expected for April 1st, 1991, July 1st, 1991, and January 2nd, 1992, respectively. The dates when the surveys were conducted were recorded, as well as the spot and 3, 6, and 12 month forward rates on that particular day.
3. Measuring time-varying risk premia: methodology In order to test whether the existence of time-varying risk premia is the economically important reason for rejection of forward market efficiency, the following equation is often fitted in the literature [see Frankel and Froot (1987); Cavaglia et al. (1994)]: Et St 1k 2 St 5 a 1 b (Ft,t1k 2 St ) 1 ´t
(1)
where St is the natural logarithm of the spot exchange rate, Ft,t1k is the logarithm of the forward rate at t for delivery at t1k. and Et St1k is the expectation of St 1k which is formed at time t. The null hypothesis of perfect substitutability implies that a 50 and b 51. Under the hypothesis that the correlation of the risk premium with the forward discount is zero, b will equal 1. Fitting Eq. (1) to the data by ordinary least squares, appropriately adjusting standard errors to reflect the overlapping nature of the data, we find for our sample (results not shown) that the joint hypothesis a 50 and b 51 as well as the simple hypothesis b 51 are rejected in almost all cases. Thus, variation in the forward discount for EMS currencies reflects a statistically significant degree of variation in the risk premium. Diagnostic checks on the residuals of the even regression equations reveal the presence of conditional heteroskedasticity in the residual ´t , at the longer forecast horizons. This evidence contrasts with the results of Domowitz and Hakkio, who found no significant ARCH effects, except for the case of the Japanese Yen. Based on the utility optimizing models with money growth by Lucas (1982), Domowitz and Hakkio presented an international asset pricing model in which the risk premium is a function of the conditional variances of domestic and foreign money supplies. We use their model as a starting point to model EMS exchange risk premia, but in the light of the apparent irrationality of expectations on the part of market participants, we do not impose rational expectations but, instead, test the model directly on the basis of our survey data.
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
353
The model we study is the following: Et St 1k 2 St 5 RP tk 1 b1 (Ft,t1k 2 St ) 1 ´t
(2)
RP tk 5 b0 1 u h t
(3)
´t uIt21 | N(0,h 2t )
(4)
2 h t2 5 a0 1 a1 ´ t221 1 g1 h t21
(5) k
Eqs. (2)–(5) represent a GARCH-in-mean model of the risk premium, RP t . The specification is slightly more general than Domowitz and Hakkio (1985), who study an ARCH-in mean model. The conditional volatility of ´t , h t , plays a dual role: it also enters the risk premium specification in Eq. (2). Our choice for a GARCH(1,1) specification in Eq. (5) is inspired by the Nieuwland et al. (1994) results, which indicate that this specification gives a parsimoneous and adequate representation of time series of EMS exchange rates.
4. Empirical results We estimate the model using the Berdt, Hall, Hall and Hausman algorithm to maximize the likelihood function associated with the model. All calculations were performed with the software package GAUSS. Maximum likelihood estimates of the parameters and their heteroskedasticityconsistent asymptotic standard errors are reported in Table 1. It is interesting to note in Table 1 that the coefficient u, which was always insignificant in the Domowitz and Hakkio study, is statistically significant here in a number of cases, lending some support to the risk premium specification. In addition, the results provide a fairly consistent rejection of the hypothesis b2 51 (no time-varying risk premia), suggesting significant variation in the risk premium. This conclusion also differs from Domowitz and Hakkio, who found significant variation in the risk premium for only two out of five currencies in their sample. The GARCH(1,1) specification is supported in a number of cases by significant a1 and g1 estimates. Note that in the case of the Italian lira / German mark exchange rate the coefficient estimates of a1 1g1 are greater than one in all three cases, indicating high persistence in volatility shocks. This may be due to shifts in monetary regimes which affect the level of the unconditional variances. (See Lamoureux and Lastrapes, 1990.)
5. Conclusions In this paper we examined EMS exchange risk premia over the 1986–1991 period. We extended the analysis of Domowitz and Hakkio (1985) using survey data in order to avoid relying on expectational rationality on the part of market participants. Our results indicate that time-varying risk premia were present in almost all cases and that the time-varying premia can be accounted for by our GARCH-in-
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
354
Table 1 Maximum likelihood estimates of the model in Eqs. (2)–(5). January 1st, 1986–September 1st, 1991
b0 BF / DM DG / DM FF / DM IL / DM SP/ DM
BF / DM DG / DM FF / DM IL / DM SP/ DM
BF / DM DG / DM FF / DM IL / DM SP/ DM
3 months 20.0135 (0.0051) 0.0074** (0.0032) 0.0020 (0.0066) 20.0222 (0.0063) 20.0090** (0.0037) 6 months 20.0177*** (0.0025) 0.0083*** (0.0027) 0.0065 (0.0189) 0.0031 (0.0130) 0.0085 (0.0085) 12 months 20.0076 (0.0058) 0.0068 (0.0071) 20.0204** (0.0114) 0.0073 (0.0076) 0.0215*** (0.0044)
b1
u
a0 (?10 2 )
a1
g1
L.L.
0.2122*** (0.1064) 0.9548 (0.2771) 0.7577* (0.1673) 2.4866*** (0.4408) 0.2819*** (0.1798)
1.3406*** (0.5486) 20.7371* (0.3921) 20.6475 (1.4011) 20.8173*** (0.2054) 1.4544*** (0.2920)
0.0042 (0.0032) 0.0003 (0.0003) 0.0023*** (0.0005) 0.0018 (0.0013) 0.0005* (0.0003)
0.4337 (0.5036) 0.0000** (0.0000) 0.0790 (0.0700) 0.7323*** (0.2003) 0.0000 (0.0000)
0.2457 (0.3598) 0.9499*** (0.0507) 0.0000 (0.0000) 0.4779*** (0.1323) 0.9233*** (0.0275)
219.30
0.0810*** (0.0371) 0.7614* (0.1445) 0.9514 (0.1770) 0.3781*** (0.2128) 0.2105*** (0.1383)
2.2182*** (0.2991) 21.7600*** (0.5177) 21.4950 (2.3751) 0.2564 (0.5179) 0.2587 (0.3474)
0.0011 (0.0007) 0.0008 (0.0005) 0.0060** (0.0027) 0.0003 (0.0010) 0.0155** (0.0073)
0.1588** (0.0714) 0.1693* (0.1032) 0.0899 (0.1302) 0.4060*** (0.1246) 0.8536 (0.7672)
0.6898*** (0.0566) 0.6315*** (0.1852) 0.0753 (0.2780) 0.7236*** (0.0684) 0.0000 (0.0885)
0.0920*** (0.1101) 0.0395*** (0.2347) 0.5969*** (0.1324) 0.1542*** (0.0873) 0.2653*** (0.0655)
0.8288*** (0.2940) 20.7682 (0.9379) 1.6435 (1.2154) 0.2353 (0.1705) 20.6997*** (0.2117)
0.0080** (0.0039) 0.0019 (0.0013) 0.0041 (0.0097) 0.0038 (0.0026) 0.0000 (0.0000)
0.7281** (0.3534) 0.4264 (0.6647) 0.0136 (0.3429) 0.7959*** (0.2534) 0.0000 (0.0000)
0.0000 (0.0000) 0.4798 (0.4368) 0.5150 (1.3983) 0.3568*** (0.1520) 0.9863*** (0.0051)
232.46 268.58 181.67 211.45
228.25 253.87 231.15 186.74 179.06
203.24 224.15 221.84 167.69 180.19
*, **, ***, denote significance at the 10%, 5%, and 1% level for the hypotheses b0 50, b1 51, a0 50, a1 50, or g1 50, respectively. Note: Heteroskedasticity-consistent standard errors of the coefficients are given in parentheses. Abreviations: L.L., log-likelihood values; BF, Belgian franc; DG, Dutch guilder; DM, German mark; FF, French franc; IL, ltalian lira; SP, Spanish peseta.
mean specification in a number of cases. The results basically contrast with those of Domowitz and Hakkio, who found only minimal support for their specification for a different set of currencies.
References Cavaglia, S., Verschoor, W.F.C., Wolff, C.C.P., 1993. Further evidence on exchange rate expectations. Journal of International Money and Finance 12, 78–98.
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
355
Cavaglia, S., Verschoor, W.F.C., Wolff, C.C.P., 1994. On the biasedness of forward foreign exchange rates: Irrationality or risk premia?. Journal of Business 67, 321–343. Domowitz, I., Hakkio, C.S., 1985. Conditional variance and the risk premium in the foreign exchange market. Journal of International Economics 19, 47–66. Frankel, J.A., Froot, K.A., 1987. Using survey data to test propositions regarding exchange rate expectations. American Economic Review 77, 133–153. Lamoureux, C.G., Lastrapes, W.D., 1990. Persistence in variance, structural change and the GARCH model. Journal of Business and Economic Statistics 8, 225–234. Lucas, R.E., 1982. Interest rates and currency prices in a two-country world. Journal of Monetary Economics 10, 335–360. Nieuwland, F.G.M.C., Verschoor, W.F.C., Wolff, C.C.P., 1994. Stochastic trends and jumps in EMS exchange rates. Journal of International Money and Finance 13, 699–727.
EMS exchange rate expectations and time-varying risk premia a
b
Frederick G.M.C. Nieuwland , Willem F.C. Verschoor , Christian C.P. Wolff
c ,d ,
*
a
Algemeen Burgerlijk Pensioenfonds, Heerlen, The Netherlands De Nationale Investeringsbank, The Hague, The Netherlands c Limburg Institute of Financial Economics ( LIFE), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands d CEPR, London, UK b
Received 28 January 1998; accepted 7 May 1998
Abstract In this paper we examine exchange risk premia employing a survey dataset of EMS exchange rates. We are able to test a risk premium model directly, i.e. without having to rely on the rational expectations assumption. Our results indicate that time-varying risk premia are present in almost all cases and that a GARCH-in-mean specification for the premium is often appropriate. 1998 Elsevier Science S.A. All rights reserved. Keywords: Exchange rates; EMS; Risk premia; survey data JEL classification: F31
1. Introduction One of the well-estabished empirical regularities in the financial economics literature is the finding that the forward discount is a biased predictor of the future change in the exchange rate. The rejection of forward market efficiency is generally attributed to irrational expectations and time-varying risk premia. See, e.g., Frankel and Froot (1987); Cavaglia et al. (1994). Alternative methodologies have been explored in the literature to model and measure time-varying risk premia. In an interesting contribution Domowitz and Hakkio (1985) proposed a model in which risk premia are embedded in an autoregressive conditional heteroskedasticity (ARCH) framework. Their approach is conditional on the hypothesis that the forward exhange market is efficient or rational. In this paper we study European Monetary System (EMS) currency markets. We model risk premia using an approach that builds on the Domowitz and Hakkio analysis. Our approach implements a survey database of exchange rate expectations, covering a wide range of EMS exchange rates. The principal benefit of employing such data is that one obtains a direct measure of agent’s beliefs. Whereas the Domowitz and Hakkio study proceeds conditional on the assumption that expectations are rational, we are able to test the risk premium model directly, based on observed expectations. Our survey dataset is described in detail in Cavaglia et al. (1993), (1994). *Corresponding author. Tel.: 131 43 3883838; fax: 131 3 3258530; e-mail: [email protected] 0165-1765 / 98 / $19.00 1998 Elsevier Science S.A. All rights reserved. PII: S0165-1765( 98 )00128-1
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
352
The paper is organized as follows. In Section 2 we briefly describe the survey dataset. In Section 3 the methodology is explained. The empirical results are presented in Section 4 and our conclusions in Section 5.
2. The survey data From 1986 through 1991, Business International Corporation conducted a monthly survey of exchange rate expectations covering five European currencies relative to the Deutschmark, which were published in its Cross Rates Bulletin. For publication purposes, survey participants were asked a few days prior to the end of the month to fax 3, 6 and 12 month-ahead expectations of currencies with projections being made from the beginning of the following month. Thus, for instance, the 3, 6 and 12 month-ahead expected French franc / German mark rates recorded on December 27th, 1990, reflect slightly longer forecast horizons as they represent the spot rates expected for April 1st, 1991, July 1st, 1991, and January 2nd, 1992, respectively. The dates when the surveys were conducted were recorded, as well as the spot and 3, 6, and 12 month forward rates on that particular day.
3. Measuring time-varying risk premia: methodology In order to test whether the existence of time-varying risk premia is the economically important reason for rejection of forward market efficiency, the following equation is often fitted in the literature [see Frankel and Froot (1987); Cavaglia et al. (1994)]: Et St 1k 2 St 5 a 1 b (Ft,t1k 2 St ) 1 ´t
(1)
where St is the natural logarithm of the spot exchange rate, Ft,t1k is the logarithm of the forward rate at t for delivery at t1k. and Et St1k is the expectation of St 1k which is formed at time t. The null hypothesis of perfect substitutability implies that a 50 and b 51. Under the hypothesis that the correlation of the risk premium with the forward discount is zero, b will equal 1. Fitting Eq. (1) to the data by ordinary least squares, appropriately adjusting standard errors to reflect the overlapping nature of the data, we find for our sample (results not shown) that the joint hypothesis a 50 and b 51 as well as the simple hypothesis b 51 are rejected in almost all cases. Thus, variation in the forward discount for EMS currencies reflects a statistically significant degree of variation in the risk premium. Diagnostic checks on the residuals of the even regression equations reveal the presence of conditional heteroskedasticity in the residual ´t , at the longer forecast horizons. This evidence contrasts with the results of Domowitz and Hakkio, who found no significant ARCH effects, except for the case of the Japanese Yen. Based on the utility optimizing models with money growth by Lucas (1982), Domowitz and Hakkio presented an international asset pricing model in which the risk premium is a function of the conditional variances of domestic and foreign money supplies. We use their model as a starting point to model EMS exchange risk premia, but in the light of the apparent irrationality of expectations on the part of market participants, we do not impose rational expectations but, instead, test the model directly on the basis of our survey data.
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
353
The model we study is the following: Et St 1k 2 St 5 RP tk 1 b1 (Ft,t1k 2 St ) 1 ´t
(2)
RP tk 5 b0 1 u h t
(3)
´t uIt21 | N(0,h 2t )
(4)
2 h t2 5 a0 1 a1 ´ t221 1 g1 h t21
(5) k
Eqs. (2)–(5) represent a GARCH-in-mean model of the risk premium, RP t . The specification is slightly more general than Domowitz and Hakkio (1985), who study an ARCH-in mean model. The conditional volatility of ´t , h t , plays a dual role: it also enters the risk premium specification in Eq. (2). Our choice for a GARCH(1,1) specification in Eq. (5) is inspired by the Nieuwland et al. (1994) results, which indicate that this specification gives a parsimoneous and adequate representation of time series of EMS exchange rates.
4. Empirical results We estimate the model using the Berdt, Hall, Hall and Hausman algorithm to maximize the likelihood function associated with the model. All calculations were performed with the software package GAUSS. Maximum likelihood estimates of the parameters and their heteroskedasticityconsistent asymptotic standard errors are reported in Table 1. It is interesting to note in Table 1 that the coefficient u, which was always insignificant in the Domowitz and Hakkio study, is statistically significant here in a number of cases, lending some support to the risk premium specification. In addition, the results provide a fairly consistent rejection of the hypothesis b2 51 (no time-varying risk premia), suggesting significant variation in the risk premium. This conclusion also differs from Domowitz and Hakkio, who found significant variation in the risk premium for only two out of five currencies in their sample. The GARCH(1,1) specification is supported in a number of cases by significant a1 and g1 estimates. Note that in the case of the Italian lira / German mark exchange rate the coefficient estimates of a1 1g1 are greater than one in all three cases, indicating high persistence in volatility shocks. This may be due to shifts in monetary regimes which affect the level of the unconditional variances. (See Lamoureux and Lastrapes, 1990.)
5. Conclusions In this paper we examined EMS exchange risk premia over the 1986–1991 period. We extended the analysis of Domowitz and Hakkio (1985) using survey data in order to avoid relying on expectational rationality on the part of market participants. Our results indicate that time-varying risk premia were present in almost all cases and that the time-varying premia can be accounted for by our GARCH-in-
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
354
Table 1 Maximum likelihood estimates of the model in Eqs. (2)–(5). January 1st, 1986–September 1st, 1991
b0 BF / DM DG / DM FF / DM IL / DM SP/ DM
BF / DM DG / DM FF / DM IL / DM SP/ DM
BF / DM DG / DM FF / DM IL / DM SP/ DM
3 months 20.0135 (0.0051) 0.0074** (0.0032) 0.0020 (0.0066) 20.0222 (0.0063) 20.0090** (0.0037) 6 months 20.0177*** (0.0025) 0.0083*** (0.0027) 0.0065 (0.0189) 0.0031 (0.0130) 0.0085 (0.0085) 12 months 20.0076 (0.0058) 0.0068 (0.0071) 20.0204** (0.0114) 0.0073 (0.0076) 0.0215*** (0.0044)
b1
u
a0 (?10 2 )
a1
g1
L.L.
0.2122*** (0.1064) 0.9548 (0.2771) 0.7577* (0.1673) 2.4866*** (0.4408) 0.2819*** (0.1798)
1.3406*** (0.5486) 20.7371* (0.3921) 20.6475 (1.4011) 20.8173*** (0.2054) 1.4544*** (0.2920)
0.0042 (0.0032) 0.0003 (0.0003) 0.0023*** (0.0005) 0.0018 (0.0013) 0.0005* (0.0003)
0.4337 (0.5036) 0.0000** (0.0000) 0.0790 (0.0700) 0.7323*** (0.2003) 0.0000 (0.0000)
0.2457 (0.3598) 0.9499*** (0.0507) 0.0000 (0.0000) 0.4779*** (0.1323) 0.9233*** (0.0275)
219.30
0.0810*** (0.0371) 0.7614* (0.1445) 0.9514 (0.1770) 0.3781*** (0.2128) 0.2105*** (0.1383)
2.2182*** (0.2991) 21.7600*** (0.5177) 21.4950 (2.3751) 0.2564 (0.5179) 0.2587 (0.3474)
0.0011 (0.0007) 0.0008 (0.0005) 0.0060** (0.0027) 0.0003 (0.0010) 0.0155** (0.0073)
0.1588** (0.0714) 0.1693* (0.1032) 0.0899 (0.1302) 0.4060*** (0.1246) 0.8536 (0.7672)
0.6898*** (0.0566) 0.6315*** (0.1852) 0.0753 (0.2780) 0.7236*** (0.0684) 0.0000 (0.0885)
0.0920*** (0.1101) 0.0395*** (0.2347) 0.5969*** (0.1324) 0.1542*** (0.0873) 0.2653*** (0.0655)
0.8288*** (0.2940) 20.7682 (0.9379) 1.6435 (1.2154) 0.2353 (0.1705) 20.6997*** (0.2117)
0.0080** (0.0039) 0.0019 (0.0013) 0.0041 (0.0097) 0.0038 (0.0026) 0.0000 (0.0000)
0.7281** (0.3534) 0.4264 (0.6647) 0.0136 (0.3429) 0.7959*** (0.2534) 0.0000 (0.0000)
0.0000 (0.0000) 0.4798 (0.4368) 0.5150 (1.3983) 0.3568*** (0.1520) 0.9863*** (0.0051)
232.46 268.58 181.67 211.45
228.25 253.87 231.15 186.74 179.06
203.24 224.15 221.84 167.69 180.19
*, **, ***, denote significance at the 10%, 5%, and 1% level for the hypotheses b0 50, b1 51, a0 50, a1 50, or g1 50, respectively. Note: Heteroskedasticity-consistent standard errors of the coefficients are given in parentheses. Abreviations: L.L., log-likelihood values; BF, Belgian franc; DG, Dutch guilder; DM, German mark; FF, French franc; IL, ltalian lira; SP, Spanish peseta.
mean specification in a number of cases. The results basically contrast with those of Domowitz and Hakkio, who found only minimal support for their specification for a different set of currencies.
References Cavaglia, S., Verschoor, W.F.C., Wolff, C.C.P., 1993. Further evidence on exchange rate expectations. Journal of International Money and Finance 12, 78–98.
F.G.M.C. Nieuwland et al. / Economics Letters 60 (1998) 351 – 355
355
Cavaglia, S., Verschoor, W.F.C., Wolff, C.C.P., 1994. On the biasedness of forward foreign exchange rates: Irrationality or risk premia?. Journal of Business 67, 321–343. Domowitz, I., Hakkio, C.S., 1985. Conditional variance and the risk premium in the foreign exchange market. Journal of International Economics 19, 47–66. Frankel, J.A., Froot, K.A., 1987. Using survey data to test propositions regarding exchange rate expectations. American Economic Review 77, 133–153. Lamoureux, C.G., Lastrapes, W.D., 1990. Persistence in variance, structural change and the GARCH model. Journal of Business and Economic Statistics 8, 225–234. Lucas, R.E., 1982. Interest rates and currency prices in a two-country world. Journal of Monetary Economics 10, 335–360. Nieuwland, F.G.M.C., Verschoor, W.F.C., Wolff, C.C.P., 1994. Stochastic trends and jumps in EMS exchange rates. Journal of International Money and Finance 13, 699–727.