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|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24917||2002||21 صفحه PDF||سفارش دهید||9010 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Economic Modelling, Volume 19, Issue 4, August 2002, Pages 509–529
A number of econometric target zone models is estimated for the Belgian franc and the Dutch guilder vis-à-vis the deutsche mark, with a particular focus on the modeling of endogenous devaluation risk. Both currencies can be characterized by mean reversion, whereas the theoretical S-effect is observed only for the Belgian franc. Exchange rate volatility can be adequately modeled by means of a GARCH(1,1) process. For the Belgian franc, exchange rate tensions have been induced by movements in the inflation differential vis-à-vis Germany and the level of foreign exchange reserves, whereas for the Dutch guilder the interest rate differential vis-à-vis Germany and the level of foreign exchange reserves have been particularly important.
This article focuses on the movements in exchange rates within a system of target zones such as the Exchange Rate Mechanism (ERM) of the European Monetary System (EMS). In particular, it will focus on the modeling of endogenous devaluation risk. The models developed in the paper will be applied to the experience of the Belgian franc and the Dutch guilder within the ERM. While both currencies have been irrevocably fixed with the coming into being of the EMU and the adoption of the euro, the experience from their ERM participation might contain valuable lessons for the countries currently participating in ERM-II (Denmark and Greece), as well as those countries — mainly Central and Eastern European — for which future participation in ERM-II will be a prerequisite for joining the euro area. Belgium and the Netherlands have been participating in the ERM since its establishment in 1979. Both countries may be characterized as small open economies that attach great value to stable exchange rates. To a large extent, monetary policy in EMS countries has always been aimed at a stable exchange rate against the deutsche mark. Underlying this choice is the importance of Germany as a partner in foreign trade and, above all, the pronounced anti-inflation reputation of the Bundesbank. Before monetary policy was completely subordinated to the exchange rate target, both currencies experienced a number of devaluations (Ungerer et al., 1990 and Knot and De Haan, 1995). The Dutch guilder, for instance, was devalued twice in the early years of the ERM (1979–1983) before being anchored definitively in a narrow band around central parity. Before 1987, the Belgian authorities even devalued as many as seven times, as the country's deteriorating fundamentals frequently caused speculative pressure on the franc. From March 1990 on, the National Bank of Belgium has adhered to the so-called franc fort policy, under which the franc is virtually pegged at central parity. In the theoretical target zone models developed in the late 1980s, the probability of a realignment is often assumed to be exogenous or is sometimes not even modeled at all (Krugman, 1991, Svensson, 1991 and Lindberg and Söderlind, 1994). To circumvent these limitations and to investigate the empirical behavior of exchange rates and devaluation risk in a target zone, various authors have constructed econometric target zone models. After an extensive study of different model specifications, Nieuwland et al. (1991) conclude that an AR(1)–GARCH(1,1) jump model best describes exchange rate developments within the ERM. They model the probability of a jump by means of a Poisson distribution, while the observed clustering of extreme values necessitates a GARCH specification. In a MA(1)–GARCH(1,1) jump model presented by Vlaar (1992), the probability of a jump is conditioned on economic fundamentals such as the inflation differential vis-à-vis Germany and the trade surplus. The study shows that the Dutch and French probabilities of a jump are affected by developments in the inflation differential and that the Danish probability of a jump is related to developments in the trade surplus. For Belgium, Ireland, and Italy, no significant relationships are reported. Ball and Roma (1993) adopt a different approach to model exchange rate dynamics within the ERM. Their decomposition of the exchange rate into the central parity (ct) and the exchange rate within the band (xt) is more in line with the theoretical target zone models. Assuming that xt follows an Ohrnstein–Uhlenbeck process owing to intramarginal interventions and that the likelihood of a realignment depends on the position within the band, they show that both the jump element and the mean reversion element are important aspects in the modeling of ERM exchange rates. Engel and Hakkio (1994) emphasize the fact that within the ERM extreme exchange rate changes have a tendency to cluster. Their model is characterized by a ‘quiet’ distribution and a jump distribution in which the probability of a sampling from one of the two distributions depends on the position within the band and the type of distribution of the previous sampling. They find that the probability of a jump increases as the exchange rate approaches the upper band and/or as the previous observation also involved a jump. Finally, one of the most advanced econometric target zone models at present is that of Bekaert and Gray (1996). Their model, which is estimated on the basis of data for the FF/DM exchange rate, distinguishes itself from other econometric target zone models by the large number of explanatory variables with which devaluation risk is endogenized. In the present study, three of these models will be described and estimated for the Belgian franc and the Dutch guilder. Various economic fundamentals will be identified that influence the probability of a realignment. Apart from being used to endogenize the probability of realignment, a number of practical applications will be considered. Models in international finance are often based on specific assumptions regarding the exchange rate distribution (Boothe and Glassman, 1987), for instance, the assumption of normally distributed exchange rate changes in the construction of an efficient asset portfolio or valuation methods of currency options. By charting the stochastic processes underlying the exchange rate movements within target zones, the legitimacy of such assumptions may be assessed. The paper is organized as follows. Section 2 first analyzes the exchange rate movements of the Belgian franc and the Dutch guilder from the foundation of the ERM in March 1979 up to just before the widening of the fluctuation margins in July 1993. The results of this analysis serve as a guideline for the estimation of an elementary jump model in Section 3. To allow for endogenous devaluation risk, the model presented by Bekaert and Gray (1996) is subsequently described and estimated in Section 4. On the basis of the outcomes for both Belgium and the Netherlands, Section 5 then presents a new specification whose predictive performance is tested in Section 6. Section 7 offers some concluding remarks.
نتیجه گیری انگلیسی
We have reviewed the movements in exchange rates within target zone exchange rate systems such as the ERM, focussing on the experiences of the Belgian franc and the Dutch guilder in particular. In the theoretical target zone literature, which mainly emerged during the late 1980s, the modeling of realignments stemming from speculative attacks is rather limited. Target zone models are, however, suitable for analyzing the movements in exchange rates within a credible target zone. These models predict, for instance, that exchange rates have a tendency to return to the central parity (mean reversion) and that their variability diminishes near the boundaries of the target zone (the S-effect). These implications can be tested by means of econometric target zone models. Our estimations have shown that an S-effect can only be observed for the Belgian franc, while mean reversion can be asserted for both currencies. Additionally, our results suggest that exchange rate volatility can be adequately modeled by means of a GARCH(1,1) process. This implies that the volatility for both currencies depends on previous observations and that there is a clustering of extreme values. Exchange rate tensions in Belgium may be foreshadowed by movements in the inflation differential and the level of reserves, whereas for the Netherlands, sizeable exchange rate movements have been preceded by changes in the level of reserves and changes in interest rate differentials. The latter jumps were, however, rarely high enough to force a realignment. Finally, our model displayed a reasonably adequate degree of predictive power with respect to both currencies.