Browsing by Subject "Early warning system"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    The Challenge of predicting currency crises :how do definition and probability threshold choice make a difference?
    (2015-09) Aytaç, Ayşegül
    The focus of this thesis is currency crisis, particularly the evaluation of the models that attempt to forecast currency crisis. Here, we aim to investigate the impacts of definition differences and probability threshold choices on Early Warning Systems. In the first part of the thesis, in order to show that significances of the crisis indicators are dependent to crisis definitions of the models, we separately identify the significant variables for the models that are constructed with the depreciation based definition of Reinhart and Rogoff (2009) and Exchange Market Pressure Index based definition of Eichengreen et al. (1996). In the second part, in order to analyze the definition effect on prediction powers of EWS models, by using 20 different versions of Reinhart and Rogoff (2009) and Eichengreen et al.’s (1996) currency definitions from the literature as dependent variables and the significant variables from the first part of our thesis as explanatory variables we construct 20 different EWS models. Furthermore, to analyze the probability threshold choice effect on prediction powers of the Early Warning System models, in this part we identify 11 different threshold levels and forecast our models 11 times for each of those threshold levels. Our results show that crisis definitions and threshold choices significantly affect the prediction powers of the EWS models. To put it more explicitly, EMP index based definition is shown to be a better predictor compared to depreciation based definition. Furthermore, EMP index is found to give better results with higher standard deviation multiplier. Last but not least, it is empirically proven that 50% threshold is the optimal level for EWS analyses as until that level the prediction powers of the models significantly increase but keep constant above it.