Volume 3, Issue 2, March 2018, Page: 24-29
Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter
Sergei Borodachev, Graduate School of Economics and Management, Ural Federal University, Ekaterinburg, Russia
Received: Jun. 1, 2018;       Accepted: Jun. 19, 2018;       Published: Jul. 13, 2018
DOI: 10.11648/j.ijssam.20180302.12      View  577      Downloads  23
Abstract
The goal: to develop a model that allows you 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 (KF), the model parameters, the dynamics of the money masses were estimated, and forecasting of the dollar exchange rate was done. 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. 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.
Keywords
Currency Market, Adaptive Kalman Filter, Exchange Rates, Prediction Error of Adequacy
To cite this article
Sergei Borodachev, Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter, International Journal of Systems Science and Applied Mathematics. Vol. 3, No. 2, 2018, pp. 24-29. doi: 10.11648/j.ijssam.20180302.12
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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