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Testing Methods And Models To Forecast Cryptocurrencies Exchange Rate

Author

Listed:
  • Stefan Simeonov

    (Department of Finance, Tsenov Academy of Economics, Svishtov)

  • Theodor Todorov

    (Department of Finance, Tsenov Academy of Economics, SvishtovAuthor-Name: Stefan Simeonov)

  • Daniel Nikolaev

    (Department of Finance, Tsenov Academy of Economics, Svishtov)

Abstract

The course of cryptocurrencies forms by various factors which makes it difficult to apply fundamental methods for their forecasting. For these reasons technical analysis and various statistical models are used for short-term forex and financial market forecasting. In this study we test three models: the classical autoregression model (AR), the Box-Jenkins ARIMA, and the predictively modified model Frequency Analysis of the Volatility and Trend with movable calculation (FAVT-M). The five cryptocurrencies with the largest market capitalization as of July 10, 2019 are subject to test forecasting. The AR and ARIMA results report compromise confidence within the first 5 - 6 days, after which they show significant deviations from the actual course achieved. FAVT-M generates immediate signals for the reversal of the short-term trend, but at this stage they are not clear enough for its reliable independent application in forecasting cryptocurrencies

Suggested Citation

  • Stefan Simeonov & Theodor Todorov & Daniel Nikolaev, 2020. "Testing Methods And Models To Forecast Cryptocurrencies Exchange Rate," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 10-26.
    • Handle: RePEc:neo:journl:v:17:y:2020:i:1:p:10-26
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    References listed on IDEAS

    as
    1. Stefan SIMEONOV & Teodor TODOROV, 2018. "Designing The Investment Profile Of The Shares Traded On The Bulgarian Stock Exchange In The Period From August 2016 To December 2017," Economics 21, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 1 Year 20, pages 70-100.
    2. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," JRFM, MDPI, vol. 12(3), pages 1-18, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    cryptocurrencies; autoregression; ARMA; ARIMA; predictively modified frequency analysis of volatility and trend FAVT+M;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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