Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR)

  • Amalia Listiani Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera
  • Dani Al Mahkya Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera
DOI: https://doi.org/10.35472/jsat.v6i2.904

Abstract

Blockchain is a technology that is used for recording digital transactions that are interconnected and cannot be changed. Cryptocurrencies use blockchain technology, which has advantages due to a high level of security, low fees, and a high return on investment. One of the most popular cryptocurrencies and one that has a high market cap is Bitcoin. High volatility carries the risk of large losses. So it is necessary to analyze the risk of loss and forecast Bitcoin. Forecasting is carried out using the Autoregressive Integrated Moving Average (ARIMA) model, which is then carried out by risk analysis using Value at Risk (VaR) using the Historical Data method. Based on the research results, ARIMA [4,1,2] was great for predicting Bitcoin, with a Mean Absolute Percentage Error (MAPE) of 6%. Based on the results of research with Value at Risk (VaR), investors have a maximum loss tolerance of 5.86% and there is a 5% possibility that the losses will exceed 5.85%.

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Author Biographies

Amalia Listiani, Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Dani Al Mahkya, Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Sains Aktuaria, Jurusan Sains, Institut Teknologi Sumatera

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Published
2022-12-29
How to Cite
LISTIANI, Amalia; MAHKYA, Dani Al. Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR). Journal of Science and Applicative Technology, [S.l.], v. 6, n. 2, p. 85-91, dec. 2022. ISSN 2581-0545. Available at: <https://journal.itera.ac.id/index.php/jsat/article/view/904>. Date accessed: 25 mar. 2023. doi: https://doi.org/10.35472/jsat.v6i2.904.
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Vol 6 No 2 (2022): Journal of Science and Applicative Technology December Chapter
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Articles
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