Evaluating US Dollar Index Movements Using Markov Chains-Fuzzy States Approach

  • Berna UzunEmail author
  • Ersin Kıral
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


The U.S. dollar (USD) is one of the most used currencies in the world and also the most commonly used currency in international payments. The U.S. dollar index (USDX) is a measure of the value of the USD relative to the value of a basket of currencies of the majority of the U.S.’s most significant trading partners. Therefore, dollar index gives to market players and regulators more valuable information about the dollar value rather than the regional value among the currencies. Since it can be counted as a key indicator for the direction of the USD, the Central Banks are also closely monitoring the USDX. In recent years, large fluctuations in dollar value have caused US price instability to increase. The aim of this study is to classify the USDX with triangular fuzzy sets and evaluate the USDX movements using Markov Chain of the Fuzzy States method. The data used in this study consist of the monthly changes rate of the USDX over the January 2003 to May 2018 period. The movements of the monthly USDX have been analysed with the probabilistic transition matrix of the fuzzy states, then the steady condition of the changes rate of the USDX has been presented. These outcomes give significant information to the decision makers about the USDX movements. With this model, we are able to evaluate USDX movements and estimate the expected USDX for long and short term without missing any movements between boundaries of the states.


USD index Fuzzy sets Markov chains of the fuzzy states 


  1. 1.
    Landefeld, J.S., Moulton, B.R., Vojtech, C.M.: Chained-dollar indexes. J. Surv. Curr. Bus. 11, 8–16 (2003)Google Scholar
  2. 2.
    Manning, L., Andrianacos, D.: Dollar movements and inflation: a vector error correction model. Appl. Econ. 25(12), 1483–1488 (2004)CrossRefGoogle Scholar
  3. 3.
    Platt, G.: Dollar stays strong, but yen tumbles. J. Glob. Finance 4, 68–69 (2009)Google Scholar
  4. 4.
    Cretien, P.D.: Currencies, eurodollars, silver and gold: not your average. Futur. Mag. 9, 40–43 (2009)Google Scholar
  5. 5.
    Kim, K.: US inflation inflation and the dollar exchange rate: a vector error correction model. Appl. Econ. 30(5), 613–619 (2010)CrossRefGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  7. 7.
    Kruse, R., Buck-Emden, R., Cordes, R.: Processor power considerations - an application of fuzzy Markov chains. Fuzzy Sets Syst. 21, 289–299 (1987)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Yoshida, Y.: Markov chains with a transition possibility measure and fuzzy dynamic programming. Fuzzy Sets Syst. 66, 39–57 (1994)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zadeh, L.A.: Maksimizing sets and fuzzy Markoff algorithms. IEEE Trans. Syst. Man Cybern. – Part C: Appl. Rev. 28, 9–15 (1998)CrossRefGoogle Scholar
  10. 10.
    Pardo, M.J., Fuente, D.: Fuzzy Markovian decision processes: application to queueing systems. Comput. Math Appl. 60, 2526–2535 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kıral, E., Uzun, B.: Forecasting closing returns of Borsa istanbul index with Markov chain process of the fuzzy states. J. Econ. Finance Account. 4(1), 15–23 (2017)Google Scholar
  12. 12.
    Uzun, B., Kıral, E.: Application of Markov chains-fuzzy states to gold price. Procedia Comput. Sci. 120, 365–371 (2017)CrossRefGoogle Scholar
  13. 13.
    Kıral, E.: Modeling brent oil price with Markov chain process of the fuzzy states. J. Econ. Finance Account. 5(1), 79–83 (2018)Google Scholar
  14. 14. Accessed 07 Jan 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Near East UniversityNicosiaTurkey
  2. 2.Cukurova UniversityAdanaTurkey

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