Title: Monthly forecasting of the dollar to the ruble exchange rate. Adaptive Kalman filter
Authors: Sergei Borodachev - Ural Federal University (Russia) [presenting]
Abstract: The goal is to develop a model that allows us to forecast the dollar to the ruble exchange rate for a month ahead based on macroeconomic data, published at monthly intervals. Proposed structural model of the dynamics of the ruble and dollar masses that determine the exchange rate, depending on changes in foreign exchange reserves, the balance of foreign trade, the monetary base, the MICEX index, the price of oil. With the help of the Kalman filter, the model parameters, the dynamics of the money masses, and the forecasting of the dollar exchange rate were estimated. Monthly data were used from the beginning of 2015 to mid-2017. The estimation of the capacity of dollar market was found in about half the capacity of the MICEX index funds.Average error of forecasts, based on information available one step before the forecasted moments (RMSEA) was 1.99 RUB. Adaptive form of KF was developed when, similarly to the EM algorithm, the phases of KF estimation in the window and minimization of average prediction error to determine the optimal estimates for the system model parameters in this moment are sequentially alternated. With this RMSEA became 1.39 RUB.