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A study on the influence of the discretisation unit on the effectiveness of modelling currency exchange rates using the binary-temporal representation

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  • Michał Dominik Stasiak

Abstract

An exchange rate can be expressed in the form of a binary-temporal representation. Such a representation is based on a discretization of movements in the exchange rate, in which to each change in the value - equal to a given discretization unit – two parameters are allocated: a binary value, consistent with the direction of change in the exchange rate (increase 1, decrease 0) and duration. Statistical examination proves the existence of dependencies between the parameters of previous changes and the direction of future changes. To model the exchange rate using the applied binary-temporal representation, an appropriate model was developed that enables estimation of the probability of the direction of future changes in the currency exchange rate based on the parameters of historical changes. This article presents an analysis of the influence of the chosen discretization unit on the quality of exchange rate modelling. For this purpose, software was written in MQL4 and C++. As a result of the study, an optimal value for the discretization unit and the optimal parameters of the model providing the highest efficiency were determined. The input data used in the analysis involved tick data for the AUD/NZD exchange rate for a five-year time frame 2012–2017.

Suggested Citation

  • Michał Dominik Stasiak, 2018. "A study on the influence of the discretisation unit on the effectiveness of modelling currency exchange rates using the binary-temporal representation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 57-70.
    • Handle: RePEc:wut:journl:v:2:y:2018:p:57-70:id:1350
    DOI: 10.5277/ord180204
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    References listed on IDEAS

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    1. Michał Dominik Stasiak, 2018. "Modelling of Currency Exchange Rates Using a Binary-Temporal Representation," Springer Proceedings in Business and Economics, in: Taufiq Choudhry & Jacek Mizerka (ed.), Contemporary Trends in Accounting, Finance and Financial Institutions, pages 97-110, Springer.
    2. Logue, Dennis E & Sweeney, Richard James, 1977. "'White-Noise' in Imperfect Markets: The Case of the Franc/Dollar Exchange Rate," Journal of Finance, American Finance Association, vol. 32(3), pages 761-768, June.
    3. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    4. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    Cited by:

    1. Krzysztof Piasecki & Michał Dominik Stasiak, 2020. "Optimization Parameters of Trading System with Constant Modulus of Unit Return," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    2. Michał Dominik Stasiak, 2020. "Candlestick—The Main Mistake of Economy Research in High Frequency Markets," IJFS, MDPI, vol. 8(4), pages 1-15, October.
    3. Krzysztof Piasecki & Michał Dominik Stasiak, 2019. "The Forex Trading System for Speculation with Constant Magnitude of Unit Return," Mathematics, MDPI, vol. 7(7), pages 1-23, July.
    4. Michał Dominik Stasiak, 2022. "Algoritmic Trading System Based on State Model for Moving Average in a Binary-Temporal Representation," Risks, MDPI, vol. 10(4), pages 1-15, March.

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