In , the real exchange rate between Brazil and the U. And sure enough, the nominal exchange rate depreciated by roughly 60 percent between and However, Rebelo reminds investors that this rule of thumb only applies to countries with floating exchange rates and an inflation-targeting monetary policy. While that covers most developed countries, it excludes countries like China, which do not have floating exchange rates.
Rebelo believes that examining real exchange rates should become standard in evaluating foreign investments. But expertise can also come with a curse. A Broadway songwriter and a marketing professor discuss the connection between our favorite tunes and how they make us feel.
The remaining observations from M1 to M12 were held back for forecasting. Extensive literature provides evidence of a significant relationship between exchange rate and selected macro-economic fundamentals.
In the literature of forecasting time series, we have mainly two types of models i. One of the most commonly used models for potential forecasting values is Box and Jenkins The ARIMA model is an integrated model when, after taking the first or second difference, the time series becomes stationary. It takes the historical data and decomposes it into the mechanism of Autoregression. ARIMA models are written as ARIMA a, b, c, where, according to the Box-Jenkins technique, a defines the order of the autoregressive model, b establishes the order of the moving average, and c indicates the order of the stationary one.
If the time series is stationary, it will be referred to as the model of ARMA a, b. Joshe et al. Deka et al. AsadUllah et al. This model implies that the time series forecast values would be equal to the real-time series values for the last available time period, i.
The ES model Gardner, is widely used in economics and finance for forecasting purposes. It is distinct from the ARIMA method, according to Brooks , where the model used an earlier linear combination of the previous time series value for the prediction of future values.
The new values will be more useful for predicting future time series than the old values for previous time series results. The ordinary model in the exponential smoothing model is the single parameter exponential smoothing model, i. However, the single parameter model is only valid for the time series and has no trend and seasonal effects.
It can be described as:. Y t k is the time series k speculated values, y t is the time t observed values, and x an is the weighting parameter. Its scale ranges from 0 to 1. High alpha values suggest that the influence of historical knowledge will soon be wiped out and vice versa. Borhan et al. The researchers concluded that the prediction of exponential smoothing is better than that of other predicted models.
Maria et al. First, they perform better for determining co-integration relations in small samples. Second, they can be applied irrespective of whether the regressors are stationary at a level or the first difference i. They cannot be applied, however, if the regressors are I 2. Le et al. Bahmani et al. They found an asymmetric effect of the exchange rate on the variation of the trade balance.
Moreover, Turan et al. Due to the extensive application and benefits of the above technique, the author has decided to include the NARDL technique in this study to analyze the non-linear relationship between the exchange change rate and different explanatory variables. Qamruzzaman et al. The findings suggest that there is a non-linear relationship among these variables in the long-run. For the combination reason, some researchers tend to use unequal weights instead of fixed equivalent weights.
Due to the above reasons, the authors include var-cor and equal weightage methods to combine forecasting the exchange rate. Modugno et al. The authors stated that the IPI is positively correlated with the GDP; therefore, it has been used by numerous researchers when they require GDP data in high frequency, i. The author further argues that a significant portion of the service sector consumes by the production sector; therefore, the argument against the reliability of IPI due to the increase of service sector share in GDP is insignificant.
The first step towards different univariate and multivariate series analyses is to ensure that the time series does not possess any upward or downward trend. There should not be any seasonality issue that eventually leads to non-stationary problems.
Table 2 depicts that the time series of the exchange rate of USD versus Pakistan Rupee was not stationary at a level with a t-value of —1. The authors took the first difference to adjust the trend, which indicates that the time series is now stationary with a p-value of 0.
Table 2 also represents the unit root results of all explanatory variables. It concludes that only Gross Domestic Product, described by IPI, is stationary at a level whereas trends in other explanatory variables have been adjusted by taking 1 st difference. ARDL Co-Integration technique requires that all variables be stationary at Level 0 or first difference I ; therefore, the results of Table 2 reveals that the pre-requisite has been fulfilled.
The author forecasts the exchange rate from the ARIMA model as it has been earlier mentioned that the time series of the exchange rate was found stationary at 1 st difference. From chosen lags, three models were selected as per rules i. The author chooses the best-fitted model by considering parsimony, i. The results are as below:. Table 4 interprets the result of the exponential smoothing model for forecasting. The Akaike Info Criterion was used to choose the best-fitted model; therefore, in this case, the additive trend has been spotted, and the best-fitted model A.
C result is Table 4 portrays the relationship between macro-economic fundamentals and exchange rates throughout the long run. It is found that an increase of one unit in foreign reserves leads to a rise in 0. Thus, there is a non-linear relationship between foreign reserves and the exchange rate. This strategy is commonly known as the carry trade. The relative economic strength method doesn't forecast what the exchange rate should be, unlike the PPP approach.
Rather, this approach gives the investor a general sense of whether a currency is going to appreciate or depreciate and an overall feel for the strength of the movement. It is typically used in combination with other forecasting methods to produce a complete result. Another common method used to forecast exchange rates involves gathering factors that might affect currency movements and creating a model that relates these variables to the exchange rate. The factors used in econometric models are typically based on economic theory, but any variable can be added if it is believed to significantly influence the exchange rate.
They believe an econometric model would be a good method to use and has researched factors they think affect the exchange rate. From their research and analysis, they conclude the factors that are most influential are: the interest rate differential between the U. The econometric model they come up with is shown as:.
The coefficients a, b, and c will determine how much a certain factor affects the exchange rate and direction of the effect whether it is positive or negative. This method is probably the most complex and time-consuming approach, but once the model is built, new data can be easily acquired and plugged in to generate quick forecasts. Forecasting exchange rates is a very difficult task, and it is for this reason that many companies and investors simply hedge their currency risk.
However, those who see value in forecasting exchange rates and want to understand the factors that affect their movements can use these approaches as a good place to begin their research. The Economist. Your Privacy Rights.
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We and our partners process data to: Actively scan device characteristics for identification. These findings suggest that while movements of nominal exchange rates in the short term are notoriously difficult to predict, exchange rate models based on macroeconomic fundamentals can explain real effective exchange rate movements in the medium term surprisingly well. This article is published in collaboration with VoxEU. The views expressed in this article are those of the author alone and not the World Economic Forum.
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