Exchange rate dynamics: Comparisons of Chile and Peru

The exchange rate between the Chilean Peso and the US dollar can be effectively modeled using a few key predictors. Using data from the International Monetary Fund’s International Financial Statistics (IFS) database, I employ an autoregressive distributive lag (ARDL) model based on 20 years of quarterly data to show that the value of the peso is negatively related to Chilean interest rates and stock market returns, as well as the price of oil worldwide. The significance of the model is tested with a bounds test, and it passes the necessary tests for heteroskedasticity and autocorrelation. As a forecasting tool, this model outperforms a random walk, as well as simple autoregressive and moving average models when predicting the exchange rate. I contrast this model for Chile with a similar analysis of the exchange rate between the United States and Peru. This Peruvian model is more complicated as it requires a series of time dummy variables in order to obtain a model that rejects the null hypothesis for the bounds test. Additionally, the Peruvian exchange rate is related to the value of imports and exports. Due to the more complicated nature of the Peruvian model, it is not as powerful in its predictive ability as the Chilean model, but it does outperform other, simpler models of the Peruvian exchange rate. This study contributes to the understanding of exchange rate dynamics and the potential impact of future fluctuations. This work also allows for a better understanding of how the central banks and governments of Chile, Peru, and the United States can anticipate and respond to fluctuations in the relative values of the peso, sol, and dollar.