  Support Vector Machine (SVM) is powerful classification technique based on
the idea of structural risk minimization. Use of kernel function enables curse
of dimensionality to be addressed. However, proper kernel function for certain
problem is dependent on specific dataset and as such there is no good method on
choice of kernel function. In this paper, SVM is used to build empirical models
of currency crisis in Argentina. An estimation technique is developed by
training model on real life data set which provides reasonably accurate model
outputs and helps policy makers to identify situations in which currency crisis
may happen. The third and fourth order polynomial kernel is generally best
choice to achieve high generalization of classifier performance. SVM has high
level of maturity with algorithms that are simple, easy to implement, tolerates
curse of dimensionality and good empirical performance. The satisfactory
results show that currency crisis situation is properly emulated using only
small fraction of database and could be used as an evaluation tool as well as
an early warning system. To the best of knowledge this is the first work on SVM
approach for currency crisis evaluation of Argentina.
