Work detail

Exchange Rate Forecasting: An Application with Model Averaging Techniques

Author: Mgr. Jaroslav Mida
Year: 2015 - summer
Leaders: prof. Roman Horváth Ph.D.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 78
Awards and prizes:
Abstract: The exchange rate forecasting has been an interesting topic for a long time. Beating the
random walk model has been the goal of many researchers, who applied various
techniques and used various datasets. We tried to beat it using bayesian model averaging
technique, which pools a large amount of models and the final forecast is the average of
forecasts of these models. We used quarterly data from 1980 to 2013 and attempted to
predict the value of exchange rate return of five currency pairs. The novelty was the fact
that none of these currency pairs included U.S. Dollar. The forecasting horizon was one,
two, four and eight quarters. In addition to random walk, we also compared our results to
historical average return model using several benchmarks, such as root mean squared
error, mean absolute error or direction of change statistic. We found out that bayesian
model averaging can not generally outperform random walk or historical average return,
but in specific setting it can produce forecasts with low error and with high percentage of
correctly predicted signs of change.




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