Probability forecasts of the Swiss franc/euro (CHF/EUR) exchange rate are generated before, surrounding and after the placement of a floor on the CHF/EUR by the Swiss National Bank (SNB). The goal is to determine whether the exchange rate floor has a positive, negative or insignificant effect on the calibration of the probability forecasts from three time-series models: a vector autoregression (VAR) model, a VAR model augmented with the LiNGAM causal learning algorithm, and a univariate autoregressive model built on the independent components (ICs) of an independent component analysis (ICA). Score metric rankings of forecasts and plots of calibration functions are used in an attempt to identify the preferred time-series model based on forecast performance. The study not only finds evidence that the floor on the CHF/EUR has a negative impact on the forecasting performance of all three time-series models but also that the policy change by the SNB altered the causal structure underlying the six major currencies.
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