Using genetic algorithm in dynamic model of speculative attack

Evolution of speculative attack models show certain progress in developing idea of the role of expectations in the crisis mechanism. Obstfeld (1996) defined expectations as fully exogenous. Morris and Shin (1998) endogenised the expectations with respect to noise leaving information significance away. Dynamic approach proposed by Angeletos, Hellwig and Pavan (2006) operates under more sophisticated assumption about learning process that tries to reflect time-variant and complex nature of information in the currency market much better. But this model ignores many important details like a Central Bank cost function. Genetic algorithm allows to avoid problems connected with incorporating information and expectations into agent decision making process to an extent. There are some similarities between the evolution in Nature and currency market performance. In our paper an assumption about rational agent behaviour in the efficient market is criticised and we present our version of the dynamic model of a speculative attack, in which we use a genetic algorithm to define decision-making process of the currency market agents. The results of our simulation seem to be in line with the theory and intuition. An advantage of our model is that it reflects reality in quite complex way, i.e. level of noise changes in time (decreasing), there are different states of fundamentals (with 'more sensitive' upper part of the scale), number of inflowing agents can be low or high (due to different globalization phases, different capital flow phases, different uncertainty levels).