A new approach to forecasting exchange rates

Abstract

a b s t r a c tBuilding on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several potential problems associated with broad price indexes, such as the consumer price index used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon. These high-frequency forecasts could be appealing to those who want up-to-date exchange-rate forecasts. Finally, as our forecasts are obtained through a simulation procedure, estimation uncertainty is made explicit in our framework that provides the entire distribution of exchange rates, not just a single point estimate. Using exchange rates of six major currencies to illustrate the approach, we compare the Big Mac forecasts with those derived from a random walk and the CPI and find some support for our approach, especially at longer term horizons.

The Big Mac Index two decades on: an evaluation of burgernomics

“…(2003) find that the BMI is more supportive to the validity of PPP than the CPI using a panel cointegration methodology. Employing an iterative simulation technique, Clements and Lan (2010) find PPP results consistent with Chen et al . (2003) and further conclude that the BMI does at least as good a job as the CPI in terms of exchange rate forecasting.…”

Using Artificial Neural Networks to forecast Exchange Rate, including VAR‐VECM residual analysis and prediction linear combination

“…Econometric models have been widely applied for this task; however, they have also been heavily criticized as the majority are linear and work under assumptions that restrict them. Thus, the use of artificial intelligence techniques has spread, such as Artificial Neural Networks (ANN), which are able to model the non‐linear behavior of time series through their learning, adaptability, and training properties, without needing to know the data structure in advance, favoring models focused on forecasting ((Chen & Leung, ); (Davis, Episcopos, & Wettimuny, ); (Kodogiannis & Lolis, ); (Yao & Tan, )).Despite the discrepancies between different studies on exchange rate linearity, the literature shows a greater interest in non‐linear models to analyze the behavior of this time series ((Clements & Lan, ); (Yu, Wang, & Lai, ); (Leung, Chen, & Daouk, )). The goal of this study is to verify that there is an improvement in the forecasts of exchange rate returns when using embedded models as opposed to using only econometric or artificial intelligence models, all in a rolling windows frame.…”

Data Clustering using Autoregressive Integrated Moving Average (ARIMA) model for Islamic Country Currency: An Econometrics method for Islamic Financial Engineering

“…The value of exchange rate is important as one of the indicators to shows the strength of economic condition for particular country. Since the breakdown of the Bretton Woods system of fixed exchange rates in the early 1970s, forecast currency values has become crucial for many purposes such as international comparisons of incomes, earnings and the costs of living by international agencies, management and alignment of exchange rates by governments, and corporate financial decision making (Clements and Lan, 2010) [8]. Even though, it is widely agreed that forecast of exchange rate is difficult task because it was influenced by many factors.…”