Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter

Sergei Borodachev

doi:doi:10.11648/j.ijssam.20180302.12

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Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter

Sergei Borodachev

Published in International Journal of Systems Science and Applied Mathematics (Volume 3, Issue 2)

Received: 1 June 2018 Accepted: 19 June 2018 Published: 13 July 2018

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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.

Published in	International Journal of Systems Science and Applied Mathematics (Volume 3, Issue 2)
DOI	10.11648/j.ijssam.20180302.12
Page(s)	24-29
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Currency Market, Adaptive Kalman Filter, Exchange Rates, Prediction Error of Adequacy

References

[1]	Michele Ca’Zorzi, Marcin Kolasa and Michał Rubaszek, “Exchange rate forecasting with DSGE models,” Journal of International Economics, vol. 107, pp. 127-146, 2017.
[2]	Martin S. Eichenbaum, Benjamin K. Johannsen and Sergio Rebelo, “Monetary Policy and the Predictability of Nominal Exchange Rates,” National Bureau of Economic Research Working Paper Series, No. 23158, 2017.
[3]	S. M. Borodachev, “Prediction of the dollar to the ruble rate. A system-theoretic approach,” AIP Conference Proceedings, vol. 1863, p. 560025, 2017.
[4]	M. Aguiar, S. Chatterjee, H. Cole and Z. Stangebye, “Quantitative Models of Sovereign Debt Crises,” in Handbook of Macroeconomics, vol. 2, Amsterdam: North-Holland, 2016, pp 1697-1755.
[5]	Martín Uribe and Stephanie Schmitt-Grohé, Open Economy Macroeconomics, Princeton University Press, 2017.
[6]	S. M. Borodachev, “GDP and efficiency of Russian economy,” AIP Conference Proceedings, vol. 1926, p. 020011, 2018.
[7]	Yemei Qin, Hui Peng, Yanhui Xi, Wenbiao Xie, Yapeng Sun and Xiaohong Chen. “An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure mode,” Applied Soft Computing, vol. 43, pp. 390-405, 2016.
[8]	S. M. Borodachev, “Unobservable characteristics of currency and stock markets system in Russia,” paper presented in 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts (SGEM 2017), Conference Proceedings Book 1, vol. 1, pp. 831-836.
[9]	A. P. Dempster, N. M Laird, and D. B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society, Series B, vol. 39 (1), pp. 1-38, 1977.
[10]	S. M. Borodachev, “Economic applications of Adaptive Kalman filter,” paper presented in 27th International Scientific Conference on Economic and Social Development (Rome, 1-2 March 2018), Conference Proceedings Book, pp. 584-588.

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APA Style

Sergei Borodachev. (2018). Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. International Journal of Systems Science and Applied Mathematics, 3(2), 24-29. https://doi.org/10.11648/j.ijssam.20180302.12

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ACS Style

Sergei Borodachev. Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. Int. J. Syst. Sci. Appl. Math. 2018, 3(2), 24-29. doi: 10.11648/j.ijssam.20180302.12

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AMA Style

Sergei Borodachev. Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. Int J Syst Sci Appl Math. 2018;3(2):24-29. doi: 10.11648/j.ijssam.20180302.12

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@article{10.11648/j.ijssam.20180302.12,
  author = {Sergei Borodachev},
  title = {Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {3},
  number = {2},
  pages = {24-29},
  doi = {10.11648/j.ijssam.20180302.12},
  url = {https://doi.org/10.11648/j.ijssam.20180302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20180302.12},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter
AU  - Sergei Borodachev
Y1  - 2018/07/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijssam.20180302.12
DO  - 10.11648/j.ijssam.20180302.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 24
EP  - 29
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20180302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

Sergei Borodachev

Graduate School of Economics and Management, Ural Federal University, Ekaterinburg, Russia

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Plain Text BibTeX RIS

APA Style

Sergei Borodachev. (2018). Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. International Journal of Systems Science and Applied Mathematics, 3(2), 24-29. https://doi.org/10.11648/j.ijssam.20180302.12

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ACS Style

Sergei Borodachev. Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. Int. J. Syst. Sci. Appl. Math. 2018, 3(2), 24-29. doi: 10.11648/j.ijssam.20180302.12

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AMA Style

Sergei Borodachev. Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter. Int J Syst Sci Appl Math. 2018;3(2):24-29. doi: 10.11648/j.ijssam.20180302.12

Copy | Download

@article{10.11648/j.ijssam.20180302.12,
  author = {Sergei Borodachev},
  title = {Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {3},
  number = {2},
  pages = {24-29},
  doi = {10.11648/j.ijssam.20180302.12},
  url = {https://doi.org/10.11648/j.ijssam.20180302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20180302.12},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Monthly Forecasting of the Dollar to the Ruble Exchange Rate with Adaptive Kalman Filter
AU  - Sergei Borodachev
Y1  - 2018/07/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijssam.20180302.12
DO  - 10.11648/j.ijssam.20180302.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 24
EP  - 29
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20180302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

Copy | Download