Modeling the relationship between the Russian ruble exchange rate and oil prices using artificial neural networks

Abstract

The article examines the dependence between the Russian ruble exchange rate and oil prices with the use of neural network modeling. The relevance of the study can be confirmed by the interest of the monetary authorities in modeling the dynamics of the exchange rate for developing monetary policy measures. The research objective of the article is the estimation of the relationship between the Russian ruble exchange rate and oil prices using multilayer perceptron and recurrent neural network models. Moreover, the influence of additional factors, including foreign exchange interventions and geopolitical risks, is estimated. The results show that neural networks provide sufficient accuracy in estimation of the target variable. Furthermore, during the periods with foreign exchange interventions and high geopolitical instability there was confirmed a decoupling of the examined variables. The modeled time series preserve non-linear nature of exchange rate data generating process, as well as the asymmetry in the reaction of the ruble exchange rate to oil price shocks. The hyperparameters selection, use of bootstrap and ensembles of neural networks provide more robust estimates and confidence intervals for the oil price elasticity of the ruble exchange rate. Therefore, the combination of the aforementioned methods makes it possible to draw meaningful economic conclusions based on the trained neural networks, avoiding the problem of neural network weights non-interpretability.