Abstract
Currency crises have been analyzed and modeled over the last few decades. These currency crises develop mainly due to a balance of payments crisis, and in many cases, these crises lead to speculative attacks against the price of the currency. Despite the popularity of these models, they are currently shown as models with low estimation precision. In the present study, estimates are made with first- and second-generation speculative attack models using neural network methods. The results conclude that the Quantum-Inspired Neural Network and Deep Neural Decision Trees methodologies are shown to be the most accurate, with results around 90% accuracy. These results exceed the estimates made with Ordinary Least Squares, the usual estimation method for speculative attack models. In addition, the time required for the estimation is less for neural network methods than for Ordinary Least Squares. These results can be of great importance for public and financial institutions when anticipating speculative pressures on currencies that are in price crisis in the markets.
Highlights
A currency crisis is defined as the inability of the authorities of a country to defend a certain parity for the exchange rate
Computational methods improve Ordinary Least Squares (OLS) by a large margin, with Quantum-Inspired Neural Networks (QNN) being the one that best adjusts the result in terms of residuals
Results of of accuracy accuracy evaluation: evaluation: mean mean absolute absolute percentage percentage error error(MAPE). These results demonstrate the greater stability offered by the QNN model compared These results demonstrate the greater stability offered by the QNN model compared to the rest, especially in the light of the RMSE and mean absolute percentage error (MAPE) results obtained for three other to the rest, especially in the light of the RMSE and MAPE results obtained for three other
Summary
A currency crisis is defined as the inability of the authorities of a country to defend a certain parity for the exchange rate. In order to cover this gap, and given the importance that currency trading problems continue to have for many countries, the present study develops different machine learning techniques for estimating the two main popular speculative attacks models that respond to the most current concerns of the financial situation of the currencies. To this end, the data have been used for the cases of Mexico and Thailand, two countries that in recent decades have shown difficulties with the price of their currencies, being targets of attacks by numerous agents in the foreign exchange market. The conclusions of the study and its implications are exposed
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