Forecasting of exchange rates

Dror, Marika

The thesis investigates different exchange rate models and their forecasting performance. The work takes previous literature overview and summarize their findings. Despite the significant amount of papers which were done on the topic of exchange rate forecast, basically none of them cannot find an appropriate model which would outperform a forecast of a simple random walk in every horizon or for any currency pair. However, there are some positive findings in specific cases (e.g. for specific pair or for specific time horizon). The study provides up-to-date analysis of four exchange rates (USD/CZK, USD/ILS, USD/GBP and USD/EUR) for the period of time from January 2000 to August 2013 and analyse forecasting performance of seven exchange rate models (uncovered interest rate parity model, purchasing power parity model, monetary model, monetary model with error correction, Taylor rule model, hidden Markov model and ESTAR model). Although, the results are in advantage of Taylor rule model, especially for the exchange rate of USD/CZK, I cannot prove that the forecasting performance is significantly better than the random walk model. Except of the overall analysis, the work suppose instabilities in the time. Stock and Watson (2003) found that the forecast predictability is not stable over time. As a consequence, the econometric model can give us better forecast than random walk process at some period of time, however at other period, the forecasting ability can be worse than random walk. Based on Fluctuation test of Giacomini and Rossi (2010a) every model is analysed how the out-of-sample forecast ability changes over time.

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