TESTING METHODS AND MODELS TO FORECAST CRYPTOCURRENCIES EXCHANGE RATE

Stefan Simeonov (), Theodor Todorov () and Daniel Nikolaev ()
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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

Economics and Management, 2020, vol. 17, issue 1, 10-26

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

Keywords: cryptocurrencies; autoregression; ARMA; ARIMA; predictively modified frequency analysis of volatility and trend FAVT+M (search for similar items in EconPapers)
JEL-codes: C19 C58 G17 (search for similar items in EconPapers)
Date: 2020
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